Research Status, Challenges and Future Perspectives of Geological Hazard Monitoring Methods in Mining Areas

Highlights

What are the main findings?

• The monitoring demands for various mining-induced geological hazards exhibit highly differentiated characteristics, shifting from macro-scale deformation tracking to high-precision observation.
• The current monitoring technological framework is transitioning from single-method approaches to multi-source synergistic integration; however, bottlenecks persist in heterogeneous data fusion, scale unification, and critical state analysis of catastrophic instability.

What is the implication of the main finding?

• The deep integration of multi-platform observational data with mechanical and hydrological evolution models will drive a fundamental shift in monitoring, transitioning from mere deformation identification to proactive risk prediction.
• Establishing multi-scale dynamic monitoring systems and digital visualization platforms serves as a critical safeguard, ensuring the lifecycle safety management of mines and promoting the coordinated development of resource extraction and regional ecological restoration.

Abstract

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on five typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration.

1. Introduction

As an essential material foundation for human survival and development, mineral resources undergo large-scale extraction activities that inevitably disrupt underground rock structures. This, in turn, triggers a series of geological hazards, making related issues increasingly severe. Globally, the frequent incidence of mining-related geological hazards has not only precipitated severe casualties and property losses but also triggered substantial economic attrition and ecological degradation, rendering their adverse social impacts increasingly prominent. These hazards have become a critical factor constraining the sustainable socioeconomic development of these regions. The distribution characteristics of geohazard susceptibility in major global mining areas are illustrated in Figure 1. Statistical data indicate that more than half of the world’s mining areas suffer from geohazards induced by surface deformation, leading to enormous economic losses [1]. In China, in key coal mining areas such as the Loess Plateau and the Ordos Basin, disasters including surface subsidence, ground fissures, landslides, collapses, and sinkholes caused by high-intensity mining are becoming increasingly prominent, posing a severe threat to the production, daily life, and residential safety of local inhabitants. Consequently, related research has received widespread attention from the academic community, resulting in a series of significant scholarly advancements [2].

Globally, research on surface subsidence has encompassed 1596 cities, representing 22% of the world’s major urban centers [3]. Driven by the synergistic effects of anthropogenic activities and natural factors, surface subsidence has emerged as a ubiquitous global geological hazard. Its impact extends beyond severe disruptions to urban planning; it also exacerbates soil salinization, seawater intrusion, and irrigation backflow risks, leading to substantial economic losses [4]. Ground fissures, another characteristic geological hazard in mining areas, directly compromise land-surface spatial integrity. In the case of the Yujialiang coal mine, the density of ground fissures attains 77/10 km², with maximum widths extending several meters and depths of approximately 4 m, constituting a severe threat to regional transportation infrastructure and the safety of residents’ lives and property [5]. The occurrence of landslides is influenced by the coupling of multiple factors, being susceptible to the combined effects of complex geological environments, extreme climatic conditions, and anthropogenic activities [6]. Specifically, underground coal mining not only triggers cascading disasters that endanger personnel, infrastructure, and transportation networks but also further heightens the risk of landslides. In recent years, with the continuous expansion of open-pit mining, the stability of high-steep slopes has become increasingly critical. Artificial excavation or external disturbances induce the redistribution and concentration of stress within the slope rock mass, facilitating fissure development and the evolution of unstable rock masses. Under external triggers such as earthquakes or heavy rainfall, these unstable masses are prone to detaching from the parent body and rolling down, ultimately leading to collapse disasters. The aforementioned geological hazards in mining areas not only cause direct economic losses but also induce a series of chain environmental issues, which in turn adversely affect the sustainable development of mining regions. This underscores the necessity and urgency of conducting research on mining-related geohazards. Accordingly, to systematically reveal the intrinsic connections and evolutionary pathways between different types of geohazards in mining areas, a schematic diagram of the evolution model for mining geohazards was constructed (Figure 2).

The global prevalence and profound impact of the aforementioned geohazards have accelerated the construction and refinement of scientific and efficient monitoring frameworks. As a core component of early warning, disaster prevention, and mitigation, the monitoring of geological hazards in mining areas has achieved widespread international recognition for its technical and strategic significance. In practice, the establishment of multi-temporal InSAR monitoring networks enables continuous dynamic surveillance of mining areas throughout production, operation, and waste disposal phases. This provides a scientific data foundation and theoretical basis for surface restoration and land reclamation efforts [7,8]. Furthermore, to accurately retrieve three-dimensional (3D) deformation fields, ascending and descending InSAR imagery have been integrated with Global Navigation Satellite System (GNSS) data to monitor surface subsidence in the San Francisco Bay Area, with various data integration strategies systematically evaluated for their effectiveness [9]. Notably, the implementation of UAV remote sensing technology in the Shennong mining area of China reduced emergency response times by 50%, significantly mitigating casualties and economic losses [5]. Driven by continuous technological advancements, the scope of monitoring applications has expanded across numerous countries and regions, serving as a robust pillar for global disaster prevention and control in mining areas. Representative case studies include applications in China [10], Japan [11], India [12], Iran [13], Spain [14], Mexico [15], and the United States [16,17].

Against the backdrop of the increasingly prominent hazards posed by geohazards in global mining areas, the establishment of scientific and efficient monitoring and evaluation systems is of paramount importance. Accordingly, this study aims to summarize the research progress in monitoring technologies for mining areas, and to compare the effectiveness, limitations, and applicability of various methods in addressing different types of disasters. Firstly, the characteristics and monitoring requirements of typical geological hazards, such as surface subsidence, ground fissures, landslides, collapses, and sinkholes, are analyzed. Secondly, the technical advantages and constraints of diverse approaches—spanning ground-based, aerial, and spaceborne platforms, multi-source remote sensing fusion, and emerging technologies—are systematically reviewed. Subsequently, the feasibility of integrated multi-technology monitoring is demonstrated through representative case studies of mining areas. Finally, based on an analysis of current research bottlenecks, future development trends in monitoring technologies are discussed. Establishing an integrated “space-air-ground” monitoring system not only enables the early warning of disasters but also provides a quantitative basis for assessing damaged geological environments and formulating remediation strategies. By translating monitoring results into decision-support tools for restoration, this study aims to provide a systematic theoretical foundation for precisely predicting mine disasters and reconstructing ecological functions in mining areas.

2. Materials and Methods

2.1. Types, Characteristics, and Monitoring Requirements of Typical Mining Geological Hazards

Under the synergistic effect of mining disturbances and complex geological environments, typical geological hazards such as surface subsidence, ground fissures, landslides, collapses, and sinkholes are prone to developing in mining areas. Given the substantial disparities in formation mechanisms, evolutionary pathways, and morphological attributes among different hazard types, their respective monitoring priorities and precision requirements differ significantly. Building upon this, a technical roadmap for the identification of geological hazard types and the analysis of monitoring requirements in mining areas has been established (Figure 3). This roadmap facilitates a systematic transition from hazard cognition to monitoring architecture. It provides clear theoretical and technical guidance for the scientific planning of subsequent geological hazard monitoring systems in mining areas.

To ensure clarity and avoid ambiguity within this review, it is essential to distinguish between vertical ground failure and slope-related failures. In the subsequent subsections, the term “collapse” is strictly restricted to slope and rock mass instability (Section Collapse), whereas highly localized vertical ground failures associated with underground voids are uniformly categorized and discussed as “sinkholes” (Section Sinkholes).

2.1.1. Major Types of Geological Hazards

Surface Subsidence

The increasing diversity and destructiveness of geological hazards in mining areas worldwide have intensified the urgency for precision monitoring. Characterizing typical geological hazards are a fundamental prerequisite for establishing a scientific and efficient monitoring system for mining environments. Among these, surface subsidence in mining areas is a geological hazard triggered by the redistribution of the stress equilibrium state in the rock mass surrounding the goaf. This stress imbalance leads to the continuous evolution of overlying strata movement, ultimately manifesting as significant alterations in surface geomorphology and spatial positioning [18]. The morphology of mining-induced surface subsidence typically appears as a subsidence basin, characterized by maximum subsidence at the center that decreases towards the periphery as shown in Figure 4a. Based on morphological features, it can be categorized into continuous and discontinuous surface subsidence. The evolutionary process generally follows a temporal pattern of “initial slow movement, active mid-stage, and terminal stabilization,” as illustrated in Figure 4b,c. The magnitude and rate of surface subsidence are governed by multivariate factors, including mining depth, goaf dimensions, rock mass mechanical properties, and geological structural conditions. Notably, the risk of surface subsidence increases significantly in regions characterized by deep-level mining or the presence of incompetent rock strata [19]. Existing studies indicate that in most global mining districts, the magnitude of surface subsidence correlates positively with the thickness of the extracted coal seam. In certain severely impacted regions, cumulative surface subsidence can reach several meters, directly altering the regional landforms and triggering a series of derivative disasters, as shown in Figure 4b [20].

The impacts of mining-induced surface subsidence are characterized by their systemic nature and prolonged duration. Such surface subsidence can lead to the degradation of adjacent arable land and a decline in soil fertility. Furthermore, it disrupts underground hydrological systems, potentially triggering groundwater table drawdown or aquifer contamination. Differential deformation may also result in structural fissuring in buildings and road collapses, thereby threatening the lives and property safety of residents within and around the mining areas. Through the analysis of multiple global mining cases, this research demonstrates that surface subsidence can persist for several years, posing significant challenges for subsequent remediation and land reclamation efforts [21].

From both mechanistic and monitoring perspectives, it is imperative to explicitly distinguish between progressive subsidence and localized collapse phenomena. The latter category-encompassing failures driven by cavity migration, piping, or dissolution-is systematically discussed in Section Sinkholes. In contrast, localized collapses frequently lack distinct early-stage surface manifestations and exhibit highly confined spatial footprints. Consequently, their detection necessitates fundamentally different monitoring strategies, prioritizing the high-frequency identification of subsurface anomalies over large-scale surface deformation tracking. Accordingly, the evolutionary characteristics and monitoring methods discussed in this subsection focus predominantly on progressive, basin-scale surface subsidence.

Ground Fissure

Ground fissures are linear structural ruptures hazards formed during the process of surface subsidence. Fundamentally, they represent a failure that occurs when the rock strata are subjected to tensile stress that exceeds their intrinsic tensile strength [22]. As a characteristic geological hazard induced by underground mining, ground fissures are widely prevalent various types of goafs globally. Their spatial distribution patterns typically align with the strike of the goaf, manifesting primarily as extensional fissures. In coal mining subsidence zones, the fissure orientation is typically perpendicular to the advance direction of the working face. Furthermore, fissure development exhibit significant spatial heterogeneity relative to the working face location, as shown in Figure 5a [23,24]. Field investigations demonstrate that ground fissures exhibit an immense span in geometric scales: widths range from several centimeters to several meters, while lengths extend from tens of meters to over a thousand meters, as shown in Figure 5b,c. Their developmental depth also varies significantly. The spatial distribution and geometric parameter characteristics are illustrated in Figure 5d. Concurrently, a close correlation exists between the degree of ground fissure development and mining intensity, demonstrating a significant positive correlation [5]. With the continuous growth in global energy demand, mining activities are progressively extending into deeper mineral zones. Consequently, ground fissure evolution is characterized by increasing widths, extensions, and depths, as well as expanded impact areas. This has become a universal challenge constraining the green, safe, and sustainable development of the global mining industry [25].

Landslide

Landslides represent a critical category of geological hazards characterized by the large-scale instability and sliding of surface or deep rock and soil masses. These events are primarily triggered by the failure of structural support attributable to high-intensity underground resource extraction. Consequently, they pose a significant threat to both operational safety and ecological environment stability (e.g., resulting in vegetation destruction and severe soil erosion) within mining areas. Research indicates that mining activities disrupt the equilibrium of the primordial stress field, leading to the proliferation of internal fractures within the overlying strata and a marked reduction in the shear strength of the rock and soil masses. Geomechanically bodies are prone to sudden instability and failure in complex stress environments. This is especially true when disturbances from underground goafs interact with the sliding effects of open-pit slopes (Figure 6). Given their extensive impact zones and significant destructive power, these hazards present a persistent threat to socioeconomic stability on a global scale [27]. At the engineering scale, landslides frequently induce the instability of waste rock piles, which may subsequently evolve into high-energy debris flows, directly endangering the lives of mining personnel and the integrity of site infrastructure. At the regional scale, landslides often exhibit distinct characteristics of hazard chains. The associated surface subsidence and ground fissures not only modify regional hydrogeological conditions but also compromise the structural integrity of surrounding farmlands, transportation infrastructure, and residential buildings. Under specific topographic conditions, these events may catalyze secondary disasters such as debris flows, resulting in irreversible, multi-scale degradation of the fragile ecological environment in mining districts [25,28].

Collapse

Within the framework of mining-induced geological hazards, it is imperative to explicitly define the terminology adopted in this paper. While the generic term “collapse” may broadly encompass vertical ground failure associated with underground voids, such as sinkholes and cavity-roof collapse (systematically discussed as a distinct hazard category in Section Sinkholes), this subsection is strictly confined to collapses occurring within slopes and rock masses. Specifically, the discussion herein focuses on the mechanisms, characteristics, and controlling factors of slope instability and rock mass collapses subjected to mining disturbances, carefully distinguishing them from void-induced vertical collapse phenomena.

Collapse is a typical dynamic catastrophic mode of slope instability in mining areas. It primarily describes the phenomenon in which discontinuities inherent within high and steep slopes undergo instantaneous coalescence or non-linear propagation under the influence of gravity, mining disturbances, and other external forces. This process results in the detachment of rock masses from the parent body, followed by abrupt instability and free-fall. Such geohazards typically manifest on the high-steep slopes of open-pit mines, unstable rock masses impacted by underground goafs, and tectonically complex fault zones [29]. Research indicates that the slope gradient is a critical parameter controlling the development of collapse. When the gradient exceeds 45°, the frequency and scale of collapses increase significantly due to the synergy of gravitational potential energy and tensile stress. The correlation between the probability of occurrence and the slope gradient is illustrated in Figure 7. In contrast to landslide hazards characterized by well-defined continuous sliding surfaces, the mechanical mechanism of collapse is primarily governed by the orientation and integrity of structural planes. Their failure modes exhibit prominent features of block falling, bouncing, or toppling induced by tensile stress. As the collapse process is accompanied by extremely high instantaneous kinetic energy conversion and energy release, it is characterized by higher spontaneity and greater challenges in early warning. Consequently, it often inflicts more devastating instantaneous impacts on mining operational areas.

Globally, in addition to traditional coal mining, non-coal mining areas-such as metal and rare earth mines-are equally threatened by collapse disasters. These non-coal mining areas typically exhibit significant lithological heterogeneity. Moreover, owing to frequent tectonic activities, the internal fracture networks within the rock masses are highly developed and dense, resulting in low integrity and stability of their natural structures. Particularly in regions where high-intensity tectonic zones intersect with rainy climates, the surge in pore water pressure induced by heavy rainfall coupled with the degradation of rock structural planes creates a significant synergistic effect. This drives the non-linear expansion of concealed fractures, eventually triggering instantaneous dynamic instability of the rock mass [31]. The process of collapse disasters is characterized by a typical “high-energy and short-duration” profile, posing a devastating threat to mining infrastructure and personnel safety. Due to the extreme unpredictability and suddenness of such events, conventional monitoring methods still face significant challenges in early warning. Consequently, this has become a core risk within the production safety systems of mining areas, imposing more stringent theoretical and technical requirements for achieving standardized safety management [32].

Sinkholes

In addition to macro-scale surface subsidence and slope instability, sinkholes constitute another typical geological hazard in mining operations that requires critical attention, particularly in regions characterized by soluble rock strata or extensive underground mined-out areas. Unlike slow, progressive subsidence processes at the basin scale, sinkholes typically exhibit pronounced spatial localization and sudden onset, with their formation often forming rapidly and causing severe destruction. Mechanistically, sinkholes are closely linked to the evolution of underground cavities, encompassing a sequence of processes such as void expansion, upward migration, and roof failure. Furthermore, their occurrence is strongly governed by hydrogeological conditions, including groundwater level fluctuations and seepage erosion; in soluble rock environments, dissolution processes further accelerate cavity development, thereby increasing the likelihood of collapses. Due to their localized and abrupt nature, such hazards are frequently challenging to anticipate in advance via conventional surface deformation monitoring. Consequently, a more effective monitoring strategy should focus on the dynamic evolution of underground cavity structures and the anomalous responses of groundwater systems; for instance, integrating geophysical exploration with hydrological monitoring techniques enables earlier identification of potential risks.

As highly destructive hazards, the formation and evolution mechanisms of sinkholes remain a focal point in engineering geology and environmental geosciences. Beyond long-term natural karst processes, mining and mine closure activities have emerged as significant triggers. Particularly within soluble strata, such as evaporites and carbonates, sinkhole development is predominantly governed by dissolution processes. Groundwater circulation accelerates cavity expansion through continuous water-rock interactions and compromises the stability of overlying strata, ultimately inducing collapse. Distinct from purely stress-driven mechanical subsidence, dissolution-dominated sinkholes are fundamentally chemically driven processes; they exhibit more pronounced abruptness and significant time-lag effects, frequently occurring years after mine closure.

2.1.2. Monitoring Requirements for Different Geological Hazards

Owing to the significant disparity in the causal backgrounds, mechanistic drivers, and evolutionary characteristics of various geological hazards, monitoring strategies must be tailored with precision commensurate with specific hazard types. Surface subsidence is the primary manifestation of deformation in mining areas. Currently, the methodologies used to monitor this phenomenon are undergoing a profound transformation. As mining depth increases, traditional leveling surveys can no longer meet full-coverage engineering requirements due to limitations in sensing density and monitoring frequency. Consequently, monitoring techniques are evolving toward high-precision spatial-temporal monitoring that integrates multi-source heterogeneous data [33]. Current research widely emphasizes the construction of a collaborative “Space-Air-Ground” integrated monitoring system based on UAV photogrammetry, InSAR, and terrestrial GNSS. Through the complementarity and fusion of multi-scale data, this system enables full-process dynamic monitoring of complex deformation fields in mining areas. It should be noted that this paradigm is not limited to surface subsidence, as illustrated in Figure 8, it serves as a comprehensive monitoring framework for a broader spectrum of mining-induced geological hazards, including surface subsidence, ground fissure, and slope instability [34,35].

For ground fissure hazards, monitoring efforts should focus on the “early identification of the latent period” and the “refined characterization of morphological evolution.” In modern monitoring technology systems, regional scanning using UAV equipped with high-resolution optical or thermal infrared sensors is now a predominant approach. By integrating intelligent algorithms such as Active Contour Models (ACM) and deep learning, the automated extraction and multi-dimensional quantitative analysis of ground fissure geometric parameters can be achieved. This not only significantly augments the precision of spatial monitoring coverage but also enhances the rapid response capability to evolutionary hazard trends [36,37]. Regarding landslides and collapses, the core logic of monitoring has shifted from simple “post-disaster identification” to “early warning of catastrophic transformation.” Landslide monitoring requires the construction of a 3D stereoscopic observation network to simultaneously capture surface 3D displacements and deep rock-soil mass deformations, thereby accurately inverting the developmental depth and mechanical evolutionary morphology of the sliding surface [38]. In contrast, collapse monitoring focuses more on the structural stability and subtle deformation rates of isolated catastrophic bodies [39]. Currently, the global focus of monitoring is shifting from the analysis of cumulative displacement to the surveillance of dynamic evolution rates; specifically, by identifying time-series anomalies in deformation rates (such as millisecond-level or hour-level non-linear acceleration trends), a higher reliability in early warning for sudden-onset disasters can be achieved.

In summary, typical geological hazards within mining areas exhibit significant heterogeneity in their causal mechanisms and disaster-causing effects. Consequently, targeted monitoring should be implemented by leveraging multi-scale and multi-source data fusion technologies.

Table 1. Comparative analysis of various geological disasters in mining area.

Disaster Type	Precision Requirements	Core Detection Indicators	Key Monitoring Objects	Examples of Monitoring Techniques
Surface subsidence	Vertical displacement: ±1–5 mm; Horizontal displacement: ±1–2 mm	Subsidence magnitude, subsidence rate, horizontal displacement, subsidence basin status	Ground surface above goaf	InSAR, GNSS, Precision leveling
Ground fissures	Width ≥ 0.1 mm; Length error ≤ 1%	Width, length, depth, density	Fissure development zones	UAV photogrammetry, LiDAR
Landslides	Displacement ≥ 1 mm; Strain ≥ 10 μℇ; Pore water pressure ≥ 1 kPa	3D displacement, strain distribution, groundwater status, Factor of Safety (FoS)	Slope bodies, Dump sites	GNSS, Inclinometers, Rain gauges
Collapses	Displacement ≥ 0.5 mm; Vibration frequency: 1100 Hz	Displacement of unstable rock masses, vibration signals, stress state, meteorological conditions	Unstable rock masses	Terrestrial Laser Scanning (TLS), Microseismical monitoring
Sinkholes	Cavity detection resolution ≤ 0.5 m; Vertical displacement ≥ 1 mm	Underground cavity dimensions, groundwater level variations, soil moisture, surface micro-subsidence	Karst terrain, active dewatering zones, underground pipe networks	Ground Penetrating Radar (GPR), Groundwater wells, InSAR

systematically summarizes the core disaster-causing characteristics of four categories of geological hazards and their corresponding monitoring methods, aiming to provide a theoretical and technical foundation for the precision prevention, mitigation, and collaborative governance of geological hazards in mining areas.

2.2. Monitoring Methodology Systems

In recent years, global monitoring of geological hazards in mining areas has been undergoing a paradigm shift from “single-point monitoring” to “multidimensional integration.” With advancements in sensing technologies and communication methods, monitoring techniques are increasingly characterized by high frequency, full automation, and collaborative intelligence [40]. To address the complex monitoring requirements of both underground and open-pit mining environments, researchers have established a diversified monitoring framework encompassing terrestrial, aerial, space-based, emerging cross technologies and specific monitoring strategies for sinkholes and dissolution hazards.

2.2.1. Ground-Based Monitoring Technologies

Traditional Geodetic Monitoring

By virtue of its superior precision, traditional geodetic techniques remain the core methodology for studying surface movement patterns in mining areas and monitoring the deformation of buildings and structures. This approach primarily utilizes instruments such as levels, total stations, and inclinometers to achieve direct acquisition of geometric deformation parameters from catastrophic bodies (Figure 9). A review of existing literature indicates that these technologies have a long history of application in the field of mine disaster monitoring. Due to their well-defined physical significance in observation and mature technical standards, they maintain high authority in the extraction of characteristic parameters for surface subsidence basins and the early-stage evaluation of slope stability [41].

The level is the core instrument for achieving “sub-millimeter” precision in vertical displacement during surface monitoring of mining areas; its physical principle is based on horizontal line-of-sight measurement within the framework of geometric optics. By precisely calculating the elevation difference between survey points, leveling provides highly reliable vertical displacement data. It serves as a critical tool for establishing the vertical control network datum and capturing the evolutionary trends of local subsidence basins [42]. This technique is extensively applied in evaluating the stability of mining structures and researching the characteristics of surface movement and deformation. In engineering practice, previous studies have successfully captured subtle differential subsidence trends through 13 consecutive periods of high-precision leveling observations on the main shaft derrick of the Wolonghu coal mine, demonstrating the method’s efficacy in ensuring the safety of critical facilities. Although leveling offers the advantages of high precision and cost-efficiency, its limitations are significant: constrained by contact-based operations and manual observation modes, it is characterized by high labor intensity and long monitoring cycles, making it difficult to meet the real-time monitoring requirements for sudden catastrophic events. Furthermore, the efficiency of point deployment and observation is severely restricted in areas with complex topography or large-gradient deformation [43].

As a pivotal tool for transitioning mine monitoring from traditional one-dimensional measurements to full 3D spatial analysis, the Total Station integrates electromagnetic wave distance measurement (EDM) with electronic angle measurement technologies. By emitting high-frequency laser or infrared pulse signals, it simultaneously captures the slant range, horizontal angle, and vertical angle of the target, thereby enabling the precise calculation of instantaneous coordinate variations in the measuring point in three-dimensional space [44]. This equipment is extensively applied in the stability assessment of open-pit slopes and hazardous rock masses. Previous studies have utilized the coordinate differencing method alongside Total Stations to achieve dynamic deformation monitoring of unstable terrains in mining areas. The achieved precision fully meets the requirements of engineering early-warning systems for capturing minute displacements [45]. Compared to leveling surveys, the Total Station significantly enriches data dimensionality, enabling the acquisition of both 3D vector displacements and the overall tilting trends of a deforming body in a single measurement. Despite its high operational flexibility, the signal echoes are susceptible to interference in extreme mining climates characterized by high dust levels and haze. Furthermore, constrained by line-of-sight (LOS) conditions and the degree of automation, this technology continues to face challenges regarding long-term, real-time monitoring across ultra-large-scale regions [46].

Inclinometers, particularly deep borehole inclinometers, are core instruments in the monitoring of geological hazards in mining areas, focusing on the internal strain within rock and soil masses. By measuring the incremental changes in the tilt angle within a borehole, these devices calculate the lateral displacement of the geological body. They are primarily categorized into two types: sliding inclinometers and fixed inclinometers [47]. Distinct from levels and total stations, inclinometers can penetrate below the ground surface to directly capture the movement processes of rock masses, thereby revealing the evolution patterns of shear zones within landslide bodies. Existing research has demonstrated that this technology offers irreplaceable advantages in tracking deep deformation characteristics over long cycles, providing critical data support for the precise localization of the sliding surface. However, the deployment of inclinometers is highly dependent on complex borehole construction, which imposes stringent requirements on the geological environment. Furthermore, the monitoring information is primarily limited to lateral displacement derived from angular conversions [48].

Based on existing research, the aforementioned content summarizes three commonly employed methods for surface monitoring in mining areas: Digital levels establish deformation benchmarks with sub-millimeter vertical precision, primarily utilized for elevation baseline observations. Total stations achieve 3D coordinate measurements of specific points by integrating distance and angular measurement technologies, which are mainly used to construct the spatial coordinate system for macroscopic surface deformation fields. Inclinometers overcome LOS limitations by capturing the lateral displacement and tilting processes within rock and soil masses through deep inclination measurements, effectively bridging the gap in underground monitoring where traditional surface techniques remain blind.

Automated Sensing Networks

With the deep integration of Internet of Things (IoT) technology and low-power sensing technologies, automated sensing networks have gradually achieved real-time collection and transmission of monitoring data. Consequently, they have emerged as the primary developmental direction for surface monitoring. By integrating GNSS, crack meters, various sensors, and IoT protocols, these networks establish an all-weather monitoring grid covering both the surface and the subsurface. This integration facilitates a strategic shift from manual monitoring to automated monitoring paradigms [49].

GNSS monitoring technology utilizes space-based navigation satellite signals for high-precision positioning. Its underlying physical principle involves processing carrier phase observations to obtain the real-time 3D coordinate evolution trends of monitoring points. This typically encompasses two operational modes: static baseline control and dynamic the real-time kinematic (RTK) [50]. Compared with optical measurement techniques, GNSS technology exhibits significant technical advantages. It not only offers a high degree of automation and network deployment flexibility but also effectively circumvents accumulated errors, enabling high-precision, all-weather, and full-range monitoring. However, the observational precision of GNSS remains challenged in complex environments characterized by signal obstruction, such as dense forests, deep canyons, or high and steep slopes. Furthermore, the hardware costs for high-performance equipment remain relatively high [51]. In the practice of mining subsidence and landslide early warning, the application of GNSS has become increasingly mature. For instance, high-performance GNSS array receivers developed by researchers have achieved millimeter-level horizontal accuracy in subsidence basin monitoring, enabling the precise capture of subtle deformation characteristics within surface movement basins [52]. Frontier research globally further indicates that, through multi-frequency signal combination optimization and fast ambiguity resolution techniques, GNSS is now capable of capturing sub-millimeter dynamic deformations at a millisecond-level temporal resolution. This provides critical data support for the early identification of sudden collapses and landslides (Figure 10) [49].

Fissure meters serve as the most intuitive instruments for capturing critical failure in geohazard monitoring within mining areas. Their physical principle is based on resistive strain gauges or optical fiber sensing technologies, which linearly convert the minute opening and closing of fissures into electrical or optical signal outputs [53,54]. Often integrated with Fiber Bragg Grating (FBG) technology, fissure meters facilitate the establishment of quasi-distributed monitoring networks, typically deployed at key locations such as rock mass fracture zones or foundation pit slopes [55]. This technology enables the continuous recording of the dynamic propagation process of fissures at exceptionally high sampling frequencies, allowing for the precise monitoring of critical nodes. Its core strengths lie in high sensitivity and instantaneous response, frequently providing early warning signals prior to the occurrence of landslides or collapses. However, as a typical localized point-based monitoring method, it cannot provide large-scale 3D displacement fields as GNSS technology does. Furthermore, it imposes stringent requirements on the representativeness of deployment locations and the density of measurement points [56,57].

Environmental sensor network monitoring typically employs Micro-Electro-Mechanical Systems (MEMS) or optical fiber technologies, with data transmitted in real-time via wireless methods such as 4G and 5G [58]. While early research primarily focused on the reliability testing of individual sensors, recent studies have shifted toward integrated designs. By incorporating multiple sensors into a single node and leveraging machine learning algorithms, researchers have achieved the integrated acquisition of diverse data types, including displacement, stress, and groundwater levels. The advantage of this approach lies in its ability to provide correlated data between triggering factors and resulting effects, thereby supporting causal analysis and mechanistic research. However, its limitations include the relatively lower precision of individual sensors and their susceptibility to the high-temperature and high-pressure environments typical of mining areas. As the underlying infrastructure connecting all aforementioned sensing nodes, the IoT platform operates on the core principle of utilizing wireless communication technologies (e.g., NB-IoT, Lora WAN, 4G/5G) to transmit field data to cloud servers in real-time. In recent years, the integration of edge computing and Artificial Intelligence (AI) technologies has enabled these platforms to not only achieve efficient storage and management of massive datasets but also perform local pre-processing and anomaly detection, significantly reducing data latency. The platform is characterized by its robust integration capabilities and management potential, supporting remote command across different regions. Nevertheless, the complex shielding environments in mining areas often lead to signal fluctuations during Non-Line-of-Sight (NLOS) transmissions. Furthermore, the long-term operational costs and hardware-software compatibility remain significant constraints in the practical implementation of these engineering solutions [59].

In summary, geological hazard monitoring in modern mining areas is undergoing a paradigm shift from manual inspection to automated real-time monitoring. By integrating macro-positioning via GNSS, deformation early-warning through crack meters, the interconnection of environmental sensors, and big data processing on IoT platforms, a high-resolution, highly intelligent, and highly reliable geological hazard monitoring system has been established.

2.2.2. Aerial Monitoring Technology

Aerial monitoring technology has become an indispensable technical pillar for geological hazard monitoring in mining areas. In recent years, driven by the rapid advancement of UAV photogrammetry, aerial monitoring has gradually evolved from a traditional single-image acquisition mode into a comprehensive monitoring system characterized by multi-platform and multi-technology synergy. Its applications encompass critical fields such as surface deformation monitoring, environmental impact assessment, and safety risk early warning in mining districts [60]. To clarify the technical architecture of aerial monitoring in mining areas, systems are generally categorized into UAV platforms and airborne remote sensing platforms, based on the physical characteristics of the carrier platforms and their operational scales. However, considering the widespread deployment and high-resolution advantages in routine near-surface monitoring, the discussion in this section specifically focuses on the application of UAV platforms.

UAV, characterized by flexible deployment and high resolution, have emerged as a vital tool within near-surface aerial monitoring systems for mining areas. By integrating high-resolution optical imagery with oblique photogrammetry, UAV enable the 3D reconstruction of complex mining terrains and the generation of Digital Elevation Models (DEM) and Digital Surface Models (DSM). This provides a robust basis for revealing the evolution of surface deformation, calculating volume changes induced by mining disturbances, and identifying localized abnormal subsidence zones (Figure 11) [4]. To meet the demands for dynamic monitoring in complex deformation areas, researchers have proposed 3D surface subsidence monitoring frameworks, such as UAV-MSSH. While ensuring monitoring accuracy, these schemes significantly enhance the efficiency of data acquisition, demonstrating substantial engineering applicability-particularly in the analysis of the central zones of subsidence basins and high-risk hazard points [61]. UAV photogrammetry is highly effective in detecting significant macroscopic displacements, including large-scale vertical subsidence, horizontal displacements, open-pit mine slope deformations, and volumetric changes (such as sinkholes or waste dump landslides). Regarding detection thresholds, the accuracy typically ranges from the centimeter to decimeter level, depending on the ground sample distance (GSD) and the deployment of ground control points (GCPs) or RTK/PPK technologies.

This high-resolution, UAV-based monitoring paradigm not only provides a quantitative basis for evaluating mining-induced subsidence, but also demonstrates profound early warning value in the proactive identification of sinkholes. In the context of dissolution-aggravated sinkhole development, aerial monitoring exhibits significant predictive potential. UAV platforms equipped with thermal infrared or multispectral sensors can effectively identify localized soil moisture anomalies and vegetation stress signals associated with active groundwater circulation and cavity evolution. Furthermore, in regions characterized by soluble rock strata, high-resolution UAV photogrammetry enables the detailed mapping of early deformation features, such as the subtle surface depressions that often precede collapses, thereby providing a critical basis for the identification and early warning of abrupt sinkholes.

2.2.3. Spaceborne Monitoring Technology

On a global scale, spaceborne monitoring technology has become a vital technical means for addressing issues such as environmental assessment, surface deformation monitoring, and ecological change tracking in mining areas. Spaceborne monitoring enables geological hazard monitoring in mining districts at a wide-area scale. Characterized by extensive coverage and stable data acquisition cycles, it is particularly suitable for regional disaster risk assessment and trend analysis [60,62].

Specifically, comprehensive monitoring of the mining environment requires the precise assessment of operational metrics, particularly reclamation health and the thermal evolution of tailings. “Reclamation health” refers to the ecological recovery status of post-mining land, assessed via the quantitative retrieval of parameters such as the Normalized Difference Vegetation Index (NDVI) and soil moisture (SM) using optical satellite imagery. “Tailing heat evolution” involves tracking thermal anomalies and potential spontaneous combustion risks within tailings impoundments, primarily utilizing thermal infrared and hyperspectral sensors. To ensure the reliability of these indicators, the spaceborne monitoring framework establishes specific detection thresholds and performance standards. For structural monitoring (e.g., subsidence basins and slope displacement), time-series InSAR technology can achieve millimeter-level minimum deformation detection thresholds. For ecological assessments such as reclamation health, the framework achieves a rigorous classification accuracy threshold of 98%. Furthermore, the dynamic monitoring of transient events, such as the thermal evolution of tailings, is supported by instruments with all-weather sensing capabilities; this sets strict temporal detection thresholds that ensure a rapid revisit cycle of one to three days. Figure 12 illustrates the technical framework for the quantitative remote sensing monitoring of the ecological environment in mining areas, highlighting the synergistic effects of optical, radar, and thermal infrared data.

Optical Remote Sensing

Optical remote sensing is one of the earliest technologies applied to mining area monitoring. It primarily utilizes satellite-borne optical sensors to acquire surface reflectance information and analyzes the evolution of hazards through the comparative study of multi-temporal imagery, encompassing both multispectral and hyperspectral remote sensing. Derived from satellite platforms such as the Landsat series, Sentinel-2, and the Gao Fen (GF) series, optical imagery data is widely employed in mining change identification due to its high spatial resolution [63,64]. This technology is typically applied to the identification of large-scale subsidence areas, the mapping of ground fissure distributions, and the dynamic monitoring of landslide boundaries. Furthermore, it is frequently integrated with other data sources to enhance the spatial resolution of cartographic products and object classification performance, thereby strengthening the analytical capacity for mining landforms and land cover patterns [65], as illustrated in Figure 13. The core advantages of optical remote sensing data lie in its wide-area coverage and cost-effective acquisition; moreover, its rich spectral information significantly enhances the ability to distinguish and classify different land cover types. However, this type of data exhibits high sensitivity to meteorological conditions and is susceptible to interference from clouds, fog, precipitation, and airborne dust particles in mining environments. In mining areas with frequent cloud cover or high dust concentrations, the precision of optical monitoring is often constrained, and technical bottlenecks persist in capturing sub-millimeter or minute deformation signals [66].

InSAR

As a core method of spaceborne monitoring, InSAR technology, leveraging its capability to acquire high-precision surface deformation information, has established mature methodologies represented by Differential InSAR (D-InSAR), Persistent Scatterer InSAR (PS-InSAR), and Small Baseline Subset InSAR (SBAS-InSAR). The fundamental principle of this technology involves extracting the phase difference between multi-temporal SAR images of the same area. Through interferometric processing and topographic phase unwrapping, pure surface deformation signals are inverted and extracted (Figure 14) [67]. A significant advantage of InSAR technology lies in its all-day and all-weather observation capabilities, coupled with strong robustness against cloud, rain, and illumination conditions. This makes it a critical tool for monitoring surface subsidence and slope stability in mining areas globally. Such an approach is critical for the early detection and warning of sinkholes. In recent years, relevant studies have further confirmed that multi-temporal SAR interferometric processing techniques (such as SBAS-InSAR) can effectively capture the spatial-temporal characteristics of abnormal subsidence evolution in goaf areas, open-pit mining areas, and their surrounding surfaces. This provides highly reliable data support for revealing the dynamic evolutionary processes of the land surface in mining districts [68,69].

From the perspective of technical principles, SBAS-InSAR represents only one significant branch within the Multi-Temporal InSAR (MT-InSAR) technical framework. With the continuous accumulation of long-time-series SAR data, methodologies such as PS-InSAR, SBAS-InSAR, and the next-generation Distributed Scatterer InSAR (DS-InSAR) have evolved steadily, establishing distinct technical pathways in terms of processing strategies and target selection. These methods offer respective advantages in terms of surface subsidence rate inversion precision, spatial coverage capacity, and adaptability to low-coherence regions in mining areas. To systematically review the core characteristics and applicable conditions of various mainstream InSAR techniques, a comprehensive comparison is presented in

Table 2. Comparison of different InSAR techniques.

InSAR Technique Type	Deformation Precision	Spatial Resolution	Temporal Resolution	Application Scenarios	Advantages	Limitations
D-InSAR	±1–3 mm	10–30 m	Dependent on satellite revisit cycles	Large-gradient deformation, short-term monitoring	Rapid data processing, low hardware requirements	Prone to decorrelation, highly sensitive to atmospheric delay
PS-InSAR	±0.1–1 mm	5–25 m	12–35 days	Long-term slow deformation, urban areas, and mining districts	High precision, strong stability	Reliance on Persistent Scatterers (PS), massive data volume
SBAS-InSAR	±0.5–2 mm	10–20 m	12–35 days	Large-scale deformation, integrated monitoring of mining areas	Strong resistance to decorrelation, capability for time-series analysis	Complex data processing, long processing cycles

.

Thermal Infrared (TIR) Remote Sensing

TIR remote sensing is a technique used to invert land surface temperature (LST) states based on the thermal radiation characteristics of surface objects. By acquiring thermal radiation data from satellite-borne TIR sensors, this technology can effectively capture surface temperature anomalies in mining areas, thereby identifying potential geological hazard risks. This technology plays an irreplaceable role in identifying thermal anomalies, geothermal activities, or anthropogenic surface disturbances in mining districts [70]. Compared to visible light remote sensing, TIR possesses all-day monitoring capabilities. It provides critical surface thermal information even at night or under complex meteorological conditions, demonstrating unique advantages in coalfield fire detection, thermal pollution assessment, and the monitoring of high-temperature-induced disasters. Existing studies have demonstrated that TIR data obtained from the Landsat-8 satellite enables efficient inversion of the surface temperature fields in mining areas and coalfields. By analyzing the high-temperature signatures generated by coal seam spontaneous combustion, researchers can accurately identify potential risk zones and reveal the patterns of localized geothermal evolution [71]. Despite its advantages in large-scale rapid inspection and nocturnal operation, TIR is characterized by relatively limited spatial resolution, and its quantitative analytical precision is susceptible to atmospheric fluctuations. To compensate for these limitations, TIR is frequently applied in synergy with InSAR technology. By utilizing the dual indicators of deformation and temperature, this integrated approach provides deep insights into the triggering mechanisms and risk diffusion pathways of mining disasters, significantly enhancing the reliability of hazard identification [71].

In summary, individual remote sensing technologies are no longer sufficient to meet the complex demands of modern geological hazard monitoring in mining areas. Global spaceborne monitoring for mining districts is evolving from single-sensor observations toward an integrated monitoring system characterized by multi-source, multi-scale, and high spatial-temporal resolution [8]. Within this framework, optical remote sensing provides a macro-perspective on ecological evolution and vegetation cover; InSAR technology reveals the spatial-temporal dynamic evolution of surface deformation; and TIR remote sensing captures latent surface thermal anomaly information.

Table 3. Comparison of typical aerospace monitoring technologies.

Technology Type	Spectral Bands	Primary Applications	Advantages	Limitations
Optical remote sensing	Visible/ Near-Infrared/ Short-Wave Infrared	Land cover classification, mining area changes, vegetation health	Intuitive classification results, high spatial resolution	Limited by weather conditions and illumination
InSAR	Microwave	Surface subsidence, deformation monitoring	All-weather capability, high-precision deformation monitoring	Acquisition intervals dependent on satellite orbital revisit cycles
TIR remote sensing	8–14 µm	Land surface temperature anomalies	Sensitive to nighttime observations and thermal anomalies	Lower spatial resolution than optical imagery

compares the performance indicators and application characteristics of these three mainstream spaceborne monitoring technologies. By incorporating multi-source data fusion algorithms and AI techniques, these methods collectively construct an efficient, three-dimensional, and real-time monitoring architecture for geological hazards in mining areas. This provides a solid technical guarantee for the safe production of the global mining industry and the fine-grained management and protection of the ecological environment.

2.2.4. Multi-Source Remote Sensing Data Fusion

Multi-source remote sensing data fusion has emerged as a mainstream trend in geological hazard monitoring within mining areas. By integrating multi-scale observational data from satellites, aerial platforms, and UAV, the complementary advantages of different sensors-in terms of coverage, spatial resolution, and temporal frequency-can be effectively consolidated. This synergy not only significantly enhances the precision of deformation monitoring but also broadens its application scenarios in complex environments, such as the stability monitoring of waste rock dumps and tailings storage facilities (TSFs) [2,60]. Furthermore, the applicability of various monitoring technologies varies significantly across different surface subsidence gradients within mining areas. UAV technology demonstrates superior precision in central zones with large deformations, whereas D-InSAR and SBAS techniques exhibit greater stability in areas with moderate-to-small deformations and peripheral regions, as illustrated in Figure 15 [35]. In summary, by employing multi-source remote sensing technologies combined with InSAR and fusion algorithms, a geological hazard monitoring system covering all-spatial-scale characteristics has been established. This framework provides critical technical support for deformation early warning and refined management in mining districts.

2.2.5. Emerging Technologies

Safety monitoring systems in mining areas are undergoing a technical transformation, shifting from traditional point-based sensing technologies toward high-density, wide-area, and real-time dynamic monitoring. By constructing high-density, holographic sensing networks, emerging monitoring technologies are gradually becoming the core direction for upgrading mine monitoring capabilities. The following sections will focus on several emerging technologies, including Distributed Fiber Optic Sensing (DFOS), LiDAR, and microseismical monitoring, as well as their integrated applications with deep learning and machine learning (Figure 16).

DFOS

DFOS technology utilizes the optical fiber itself as a continuous sensing medium, enabling real-time dynamic monitoring of physical parameters such as strain, temperature, and acoustic waves across extensive mining areas. Compared to traditional point-based sensors, DFOS provides continuous, high-density data along the entire length of the fiber. It is widely applied in multi-parameter monitoring fields, including slope stability assessment, the evolution of ground fissures, surrounding rock deformation, and surface subsidence [72]. In engineering practice, slope deformation monitoring represents one of the typical application scenarios for DFOS. To illustrate this process more intuitively, Figure 17 demonstrates the fundamental principle of using distributed optical fibers to monitor slope strain [73]. The core advantages of DFOS lie in its excellent immunity to electromagnetic interference, corrosion resistance, and long-term durability, which support continuous, long-distance online monitoring. Nevertheless, the technology still faces significant challenges in large-scale engineering deployment, the optimization of coupling performance between fibers and rock-soil masses, and the quantitative interpretation of massive datasets [74]. Currently, relevant research is focusing on the application of Distributed Acoustic Sensing (DAS) in seismic wave detection and localization. These efforts aim to enhance the coverage dimensions of microseismical monitoring, thereby achieving real-time and precise monitoring of rock mass fracturing processes in deep mines.

Leveraging its multi-parameter sensing capabilities, DFOS extends beyond monitoring conventional mechanical deformation, demonstrating unique advantages in the early warning of dissolution-related hazards and underground cavity expansion. Utilizing Distributed Temperature Sensing (DTS) and Distributed Strain Sensing (DSS), this technology can identify localized thermal anomalies triggered by anomalous groundwater seepage paths. Crucially, prior to macro-scale sinkhole, DFOS can capture subtle deep precursory strains resulting from roof rock mass weakening due to hydrogeochemical dissolution, thus providing a reliable foundation for the early identification of hazards.

LiDAR Technology

As an active remote sensing technology, LiDAR acquires three-dimensional spatial information of targets by emitting laser pulses and measuring the travel time and intensity of the reflections. With the rapid development of various platforms-including Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), TLS, and UAV laser radar-the application of LiDAR in fields such as mining area deformation monitoring, topographic reconstruction, and slope stability assessment has become increasingly mature [75].

Table 4. Technical specifications and performance comparison of different LiDAR platforms.

LiDAR Type	Point Cloud Density	Ranging Accuracy	Detection Threshold	Scanning Range	Spatial Resolution	Application Scenarios
TLS	1000–10,000 pts/m2	±2 mm	1–3 cm	0.5–1000 m	Millimeter-level	Fine-scale slope modeling, monitoring of unstable rock masses
UAV-LiDAR	50–500 pts/m2	±5 mm	5–15 cm	100–1000 m	Centimeter-level	Regional subsidence, monitoring of vegetation-covered areas
ALS	1–10 pts/m2	±15 mm	10–30 cm	1–10 km	Decimeter-level	Large-scale topographic mapping, regional monitoring

summarizes the performance characteristics of different LiDAR systems.

LiDAR is capable of rapidly acquiring high-density point cloud data with precision reaching the sub-millimeter level. It is well-suited for the refined monitoring of roadway geometric changes, support structure deformations, and the spalling process of rock masses [76]. Existing studies indicate that the core application areas of this technology encompass stability monitoring and risk early-warning for open-pit mine slopes, as well as the quantitative analysis of surface subsidence and pit area deformation. By integrating with other sensor data, LiDAR has become a critical data source for digital twin modeling and automated control in mines [77,78]. Judging from current research trends, LiDAR point cloud data is being deeply integrated into Building Information Modeling (BIM), thereby enabling real-time visual management of underground operational environments [79].

Microseismical Monitoring

Microseismical monitoring involves the deployment of a series of high-sensitivity sensors to capture seismic waves and minute fracture signals generated by geological hazards in mining areas in real time. This is particularly crucial for early warning of sinkhole hazards. By analyzing the location and magnitude of these hazards, it serves as a crucial method for identifying stress changes and providing early warnings for rock mass fracturing. As monitoring systems evolve toward higher precision and networked architectures, this technology has gained extensive attention in the field of mine safety monitoring [80]. The implementation of microseismical monitoring technology relies on a system architecture characterized by multi-module collaboration, encompassing key stages such as sensing acquisition, signal transmission, and data processing, as illustrated in Figure 18.

In recent years, deep learning-based microseismical signal processing models, such as VGG4-CNN, have significantly improved the detection rate and classification accuracy of microseismical events. Research indicates that these models not only achieve an identification accuracy of over 94% but also feature rapid response times and high robustness, effectively overcoming the sensitivity of traditional threshold methods to noise interference. Meanwhile, progress has been made in integrating microseismical monitoring with fiber-optic acoustic sensing systems, which is expected to generate higher-density event data and enable more refined early identification of geological hazards [81].

Deep Learning and Machine Learning

With the explosive growth of monitoring data, traditional signal processing methods have encountered bottlenecks in areas such as weak signal extraction and the automated identification of geological events. Consequently, deep learning and machine learning have emerged as pivotal methodologies in the field of geological hazard analysis. These technologies provide novel avenues for the intelligent analysis of monitoring data and have been extensively applied to hazard identification, parameter inversion, and trend prediction, as summarized in

Table 5. Comparison of deep learning and machine learning monitoring technologies.

Algorithm Category	Representative Algorithms	Application Scenarios	Accuracy	Data Requirements	Computational Complexity	Advantages
Machine learning	Support Vector Machine (SVM)	Hazard classification, fissure identification	85–90%	Moderate (≥500 sets)	Moderate	Robust generalization, effective for small sample sizes
	Random Forest (RF)	Parameter inversion, risk assessment	88–92%	Moderate (≥800 sets)	Moderate	Strong anti-interference, resistant to overfitting
Deep learning	Convolutional Neural Network (CNN)	Image recognition, fissure extraction	90–95%	Large (≥1000 sets)	High	Powerful feature extraction, high recognition accuracy
	Long Short-Term Memory (LSTM)	Time-series forecasting, deformation trends	92–97%	Large (≥2000 sets)	High	Captures temporal dependencies, high predictive accuracy
	Fully Convolutional Network (FCN)	Semantic segmentation, hazard boundary delineation	91–94%	Large (≥1500 sets)	High	Pixel-level segmentation, precise boundary identification

[82]. Deep learning and machine learning are not isolated monitoring techniques; rather, they serve as critical tools for the efficient processing and intelligent interpretation of the aforementioned monitoring data. These technologies are characterized by a high degree of automation and rapid processing speeds, enabling the extraction of deep-seated data features. However, their performance is often constrained by the scale and quality of training samples. Enhancing the generalization capability of models within complex mining environments remains a primary focus of current research [83].

In summary, DFOS emphasizes continuous and high-density spatial monitoring capabilities, while LiDAR focuses on large-scale, high-precision three-dimensional spatial modeling. Microseismical monitoring enables the sensitive detection of dynamic fracturing and stress changes, whereas deep learning and machine learning provide robust back-end support for the intelligent analysis and event identification of high-dimensional data. Future monitoring systems will place greater emphasis on the synergistic fusion of multi-source information and real-time intelligence to address the severe challenges posed by deep mining and safety production in extra-large mines.

2.2.6. Specific Monitoring Strategies for Sinkholes and Dissolution Hazards

Monitoring areas susceptible to sinkholes and dissolution presents unique challenges, as these hazards are highly localized and primarily driven by underground cavity expansion rather than broad basin-scale deformation. Consequently, adopting targeted, multidisciplinary monitoring strategies is essential to capture precursory signals prior to sudden sinkholes.

Regarding surface deformation tracking, high-resolution UAV photogrammetry and LiDAR are highly effective in mapping subtle, localized topographic depressions and minor terrain variations, which often serve as precursors to sinkhole emergence. While InSAR can capture millimeter-level precursory deformation, its application is frequently constrained by severe “spatial decorrelation” issues in vegetated areas or regions with excessively steep deformation gradients. To mitigate this limitation, GNSS and precise leveling remain indispensable for the continuous, high-precision tracking of localized displacements at high-risk locations.

Addressing underground cavity development and hydrogeological anomalies requires the integration of surface observations with deep exploration technologies. Geophysical tools, including GPR, Electrical Resistivity Tomography (ERT), microgravity surveys, and seismic methods, are crucial for pinpointing underground cavities, detecting cavity migration, and identifying dissolution zones. Furthermore, deep borehole inclinometers can monitor deep-seated shear deformation. Crucially, since dissolution processes are chemically and hydrologically driven, conducting continuous hydrogeological monitoring (e.g., of groundwater level fluctuations, pore water pressure, and hydro chemical sensing) is vital for establishing a comprehensive sinkhole hazard early warning system.

2.2.7. Technical Comparison

In recent years, research on geological hazard monitoring technologies in mining areas has continued to deepen, yielding significant achievements in fields such as multi-source remote sensing applications, refined ground observations, and multi-technology integration. To systematically clarify the functional positioning and scope of application for various technologies, this paper summarizes and compares the primary monitoring methods currently in use. The comparison is conducted across multiple dimensions-including monitoring hierarchy, technical methodology, core advantages, limitations, and applicable hazard types-as presented in

Table 6. Hierarchical framework of monitoring technologies for geological hazards.

Monitoring Hierarchy	Technological Methods	Core Advantages	Limitations	Applicable Hazard Types
Ground-based monitoring	Traditional geodetic monitoring	Millimeter-level precision	High labor intensity for point-based surveys; restricted by terrain and weather	Subsidence pits, initial stages of landslide deformation
	Automated sensing networks	24/7 real-time dynamic monitoring; high automation	High node costs; vulnerable to damage from mining activities	Landslides, collapses, ground fissures
Aerial monitoring	UAV	Highly flexible; superior spatial resolution	Limited flight endurance; low wind resistance; heavy data processing load	Extent of collapse deposits, overall landslide morphology
Spaceborne monitoring	Optical Remote Sensing	Extensive coverage; intuitive interpretation	Significant interference from clouds/fog; unable to penetrate vegetation	Large-scale environmental surveys of mining districts
	InSAR	All-weather; large-scale; millimeter-level areal deformation precision	Limited by vegetation decorrelation and excessive deformation gradients	Ground subsidence basins in goaf areas, extremely slow landslides
	TIR remote sensing	High sensitivity to thermal anomalies	Low spatial resolution; susceptible to complex surface temperature noise	Coal seam self-ignition zones, abnormal groundwater seepage
Multi-source remote sensing data fusion		Leverages multi-sensor synergies; balances wide coverage with high-res monitoring	Complex fusion algorithms; spatial-temporal discrepancies across sources	Surface subsidence, fissures, tailings dam deformation, waste dump stability
Emerging technologies	DFOS	Multi-point sensing along a single cable; long-range; EMI-resistant	Strict installation requirements; expensive instrumentation	Mining-induced overburden failure, internal slope slips surfaces
	LiDAR	Capable of canopy penetration; acquires high-precision 3D information	Costly hardware; complex point cloud processing	Potential landslide sites with dense vegetation cover
	Microseismical monitoring	3D real-time localization and energy assessment of deep fractures	Sensitive to noise; highly dependent on velocity models	Mining-induced tremors, rock bursts, goaf collapses
	DL/ML	Powerful nonlinear fitting for massive heterogeneous datasets	requires large training datasets	Multi-source fusion for subsidence prediction; multimodal early warning
Specific monitoring strategies for sinkholes and dissolution hazards		Highly targeted for karst features	Depth penetration limits; complex data interpretation	Sinkholes, dissolution hazards, karst collapses

. Overall, different technologies possess unique characteristics in terms of spatial scale, precision level, and response capability, enabling them to meet the monitoring requirements of specific hazard types under certain conditions. However, these technologies are also subject to certain application constraints and limitations in their scope of utility. Regarding the construction of prevention and control systems, various technologies exhibit a layered and specialized application model: during the large-scale general survey stage, InSAR and optical remote sensing are primarily employed to rapidly identify regional deformations and surface anomalies. For localized high-risk areas, automated GNSS monitoring networks are utilized in conjunction with manual investigations to enhance the precision and reliability of hazard identification. During emergency response or when acquiring refined topography, UAV and LiDAR technologies are frequently adopted to leverage their advantages in high resolution and flexible deployment. Meanwhile, the monitoring of deep underground structural stability relies on DFOS and microseismical technology to achieve continuous and dynamic observation.

3. Results and Analysis

3.1. Application Cases

3.1.1. Case Study I

As core geological safety challenges faced by traditional mining areas, slope instability and surface subsidence induced by mining activities have garnered significant attention. Taking the Kangal open-pit coal mine in Turkey as the engineering background, researchers constructed a long-term deformation evolution analysis framework based on MT-InSAR. This framework aims to accurately identify potential geological hazard risk zones and their spatial-temporal evolution patterns. The coal seams in this mining area are hosted within a typical limestone-marl composite surrounding rock formation. The stress redistribution and slope structural disturbances induced by large-scale open-pit excavation constitute a complex engineering geological evolution background. The primary research area is located on the slope of the mine’s waste dump. Based on geomorphological characteristics and the evolution of deformation vectors, this area was divided into three distinct monitoring zones, as illustrated in Figure 19 [67].

This study employed Multi-Temporal MT-InSAR to simultaneously extract horizontal and vertical surface displacement velocity fields. By conducting classification statistics and spatial density evolution analysis of Distributed Scatterers (DS) at different stages, a three-dimensional evaluation framework of “Velocity-Area Ratio-Time Evolution” was established. The core of this methodology lies in its move beyond the acquisition of isolated displacement values; instead, it aims to reveal the periodic patterns and continuous evolutionary characteristics of the deformation process through long-term sequential observations [67]. By streamlining the systematic workflow of this research, the complete technical path for mining hazard identification can be clearly demonstrated (Figure 20). This case study provides robust evidence that MT-InSAR is not only capable of precisely depicting the instantaneous deformation state of mining areas but, more crucially, offers value through statistical analysis and trend fitting to achieve in-depth identification of the entire hazard evolution process. This transition from “deformation measurement” to “behavioral interpretation” holds significant practical value for the long-term safety management of mining areas [67].

3.1.2. Case Study II

In the field of mining-induced geological hazard monitoring and mechanism research, our research group has developed a representative integrated multi-source data research approach, providing a novel methodology for the multi-scale analysis of ground fissure issues. This study utilized UAV photogrammetry to conduct large-scale, high-precision fissure mapping, achieving non-contact and highly efficient hazard identification within complex gully terrains. Through the comparative validation of orthophoto interpretation and field measurements, the accuracy of fissure identification has been significantly enhanced, effectively compensating for the limitations of insufficient coverage in traditional manual surveys and the constrained resolution of satellite remote sensing. Furthermore, the study established a relatively comprehensive morphological description system for ground fissures, providing a quantitative basis for hazard grade assessment and zonation-based management. The overall research framework and technical workflow of this study (encompassing data acquisition, morphological feature extraction, and numerical simulation validation) are illustrated in Figure 21 [84].

At the mechanistic level, this study further incorporates the Particle Flow Code (PFC) discrete element method to reveal the mechanical pathways through which mining-induced disturbances propagate to surface damage from the perspective of microstructure evolution. Research results indicate that the fracturing behavior of key strata plays a decisive role in controlling the types of ground fissures; distinct temporal sequences and spatial combinations of fracturing correspond to varied morphological characteristics of ground fissures. Meanwhile, specifically for loess gully geomorphological conditions, the study divides the surface movement and deformation process into three stages: slope-bottom tension, slope-body traction, and slope-top sliding, revealing the coupled effects of mining-induced impact and slope-sliding action. Overall, by integrating UAV photogrammetry with PFC simulation, this study achieves a multi-scale bridging from surface morphology identification to strata structure control mechanisms, effectively filling the explanatory gap between macroscopic surface monitoring results and deep rock mass evolution logic [84].

While the two aforementioned case studies effectively demonstrate the potential of the integrated approach, it is crucial to acknowledge the current limitations within broader engineering practice. At present, comprehensive multi-source integrated monitoring is still transitioning from theoretical research to widespread application. In most routine mining operations, owing to constraints regarding cost, data fusion technologies, and equipment accessibility, on-site monitoring still predominantly relies on a single data source. Consequently, the presented cases represent an emerging trend rather than the current industry standard.

4. Discussion

4.1. Current Challenges and Future Perspectives

4.1.1. Current Challenges

Although the existing monitoring technology matrix encompasses spaceborne, airborne, and ground-based sensors, the inherent limitations of individual monitoring methods have become increasingly prominent when contending with the extremely complex mining-induced environments. Consequently, attaining the practical objectives of “high precision, high frequency, and full coverage” remains a significant challenge. These challenges are primarily manifested in the following four aspects:

(1) Multi-dimensional Data: Single data dimensionality and insufficient scientific rigor in early-warning criteria present significant challenges. Current research is predominantly confined to analyzing deformation magnitudes or rates; consequently, there is a persistent deficit of parametric support for identifying critical instability thresholds. For instance, the occurrence of landslides is typically closely associated with groundwater fluctuations, mining-induced disturbances, and stress redistribution. If monitoring relies solely on displacement data, it is difficult to capture precursor signals in a timely manner. The limitation of a single data dimension renders risk assessment overly dependent on empirical experience, making it challenging to construct a scientifically rigorous early-warning system with universal utility.

(2) Environmental Adaptability: The environmental adaptability of spaceborne and airborne remote sensing technologies remains constrained. Although spaceborne and aerial remote sensing can achieve large-area coverage, their sensing precision is severely constrained by environmental interference and surface conditions. Optical remote sensing is highly susceptible to cloud cover, dust in mining areas, and diurnal variations, making it difficult to achieve continuous, all-weather monitoring. Furthermore, when dealing with non-continuous large-gradient deformation zones (e.g., central subsidence basins) or regions with high vegetation coverage, InSAR technology often suffers from severe “spatial-temporal decorrelation” due to difficulties in phase unwrapping. This results in a substantial attrition of valid deformation data in critical high-risk areas.

(3) Deployment and Stability: Substantial challenges persist in the deployment and stability of deep monitoring and emerging technologies. Regarding monitoring technologies targeting deep underground and concealed engineering projects, their survivability and signal quality are significantly compromised by harsh operating conditions. The deployment of DFOS and deep-well inclinometers is highly dependent on pre-existing engineering conditions, such as boreholes. When rock masses undergo severe shearing or large-scale deformation, the risk of physical fractures or coupling failures occur easily, leading to monitoring discontinuity. Although microseismical monitoring can capture deep fracturing signals, relying solely on microseismical networks often makes it difficult to achieve high-precision source localization within the complex mechanical noise environments of mining areas. Furthermore, establishing a direct spatiotemporal correlation between internal fracturing and macroscopic surface deformation remains challenging.

(4) Maintenance and Data Continuity: Bottlenecks in the anti-interference capabilities of systems under extreme operating conditions. The complex electromagnetic and physical environments of mining areas pose a significant threat to the long-term stability of monitoring systems. Mining areas are often characterized by high dust, high humidity, intense electromagnetic interference, or severe mining-induced disturbances. Specifically, GNSS is susceptible to signal obstruction and multipath effects; optical remote sensing is constrained by weather and smoke/dust; and the stability of InSAR declines in areas with dense vegetation or large-scale deformations. DFOS is highly sensitive to coupling conditions and construction quality, while microseismical monitoring relies heavily on wave velocity models and is prone to noise interference. Under complex operating conditions, all aforementioned technologies face challenges regarding precision maintenance, data continuity, and long-term stable operation.

4.1.2. Future Perspectives

In response to the safety requirements within the context of deep mining and high-intensity extraction, core innovations in future geological hazard monitoring systems for mining areas will evolve toward multi-scale fusion, intelligent prediction, and digital management. There is an urgent need to construct a comprehensive monitoring and early-warning framework.

(1) Layered Coordination: Multi-platform layered coordination will become the fundamental framework. Spaceborne LiDAR and InSAR at the aerospace scale can be utilized for regional-level deformation screening and long-term trend analysis. Aerial platforms (UAV, LiDAR) are responsible for refined mapping and emergency verification of key areas; ground-based GNSS and automated sensor networks can provide continuous displacement sequences. Underground distributed fiber-optic sensing and microseismical systems can perceive deep fracturing and stress evolution. Through the unification of coordinate datums and error control, the organic fusion of multi-scale data can be achieved.

(2) Multi-parameter Fusion: Multi-parameter fusion will drive the enhancement of early-warning capabilities. Future monitoring will no longer be confined to the single indicator of displacement; instead, it will integrate deformation rates, strain changes, thermal anomalies, groundwater fluctuations, and energy release characteristics to construct a comprehensive discriminant model. The coupled analysis of key indicators-such as parameter inversion of surface subsidence basins, identification of landslide acceleration phases, and the fissure propagation rate of collapse masses-will significantly improve the reliability of geological hazard early warnings.

(3) Digital Visualization: Construction of digital and visualization platforms will facilitate the practical implementation of multi-source fusion. By leveraging 3D laser point clouds, BIM, and real-time monitoring data, a dynamically updated digital model of the mining area can be constructed. This will enable the visual representation of surface subsidence, ground fissures, sinkholes and slope stability status, providing an intuitive basis for risk control and excavation decision-making.

5. Conclusions

Geological hazards, such as surface subsidence, ground fissures, landslides, collapses and sinkholes, occur frequently in mining areas worldwide. These phenomena not only pose severe threats to the safety of mining operations but also exert profound impacts on the stability of regional ecosystems. As mining depth increases and extraction intensity escalates, the monitoring technology systems for mining geological hazards are undergoing a paradigm shift from individual methods toward integrated systems. By systematically reviewing the applicability and limitations of various monitoring methods, this paper identifies key technical bottlenecks and outlines future developmental directions:

(1) Monitoring requirements have transitioned from generalization to precision. Depending on the evolution mechanisms of diverse geological hazards, monitoring priorities exhibit distinct differentiated characteristics. Surface subsidence monitoring currently focuses on the spatiotemporal evolution patterns of subsidence basins and the parameter inversion of probability integral methods. Ground fissure monitoring employs high-resolution imagery and intelligent algorithms to achieve automated feature extraction. Meanwhile, the core requirements for landslide and collapse monitoring have shifted from static displacement surveillance to the early dynamic capturing of deformation rate anomalies (e.g., millisecond- or hour-scale acceleration). Specifically, for areas susceptible to sinkholes, the focus must shift from surface deformation to the dynamic evolution of underground cavities and anomalous hydrogeological responses.

(2) Current technological systems are currently transitioning from individual methods toward multi-source synergy. Conventional monitoring frequently falls short in addressing the abrupt nature of dissolution-related hazards. The synergistic mechanisms between heterogeneous monitoring methods (conventional monitoring frequently falls short in addressing the abrupt nature of dissolution-related hazards) are not yet mature, and bottlenecks persist in multi-source data integration regarding scale unification, error control, and physical interpretation. Furthermore, some monitoring systems overemphasize the acquisition of deformation magnitudes while neglecting the in-depth analysis of catastrophic instability thresholds and mechanical mechanisms. Consequently, this leads to insufficient depth in translating monitoring results into risk early warnings and decision-support applications.

(3) Future research trajectories will focus on multi-platform and system-integrated applications. Multi-platform coordination, multi-parameter fusion, and intelligent analysis constitute the critical pathways for constructing a comprehensive monitoring system. By deeply integrating measured data with mechanical evolution models, the monitoring paradigm can shift from mere deformation identification toward proactive risk prediction, especially in complex dissolution environments where hydrological coupling is essential. Meanwhile, leveraging digital twins and visualization platforms will enable the normalized application of monitoring achievements throughout the entire life cycle of mine safety management.

Overall, the monitoring of mining-induced geological hazards has entered a paradigm shift from technological accumulation toward systemic integration. Against the dual context of deep high-intensity mining and ecological redline constraints, the construction of a comprehensive monitoring system—characterized by multi-scale coordination and dynamic prediction capabilities—represents a critical direction for enhancing mine safety and achieving the coordinated development of resource exploitation and environmental protection.