Search Results (576)

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

Natural fractures in marine shales are crucial storage spaces and migration pathways for oil and gas, making the study of their formation mechanisms and distribution patterns essential for hydrocarbon exploration and development. This review systematically evaluates the progress in natural fracture studies, transitioning from qualitative to quantitative approaches, with a focus on the genetic mechanisms, distribution patterns, and methodological advancements of fracture types. The review finds that: (1) Integrated “geological-geophysical-dynamic” analyses significantly improve the prediction accuracy of tectonic fracture networks compared to traditional stress-field models. Bedding-parallel fracture development is primarily controlled by the interplay between diagenetic evolution and in situ stress, with their critical opening conditions now being quantifiable; (2) Crucially, the application of micro-scale in situ techniques (e.g., Laser Ablation Inductively Coupled PlasmaMass Spectrometer, laser C-O isotope analysis, carbonate U-Pb dating) has successfully decoded the geochemical signatures and absolute timing of fracture fillings, revealing multiple episodes of fluid activity directly tied to hydrocarbon migration. (3) The combined application of multiple techniques holds promise for deepening the understanding of the coupling mechanisms between fractures. The combined application of these techniques provides a robust framework for deciphering the coupling mechanisms between fracture dynamic evolution and hydrocarbon migration, offering critical insights for future exploration. Full article

Ground settlement is readily induced by shield–tail gaps formed during tunneling, where soil loss must be compensated through backfill grouting. However, improper grouting control may trigger tunnel uplift, segment misalignment, and, after solidification, problems such as voids, cracking, and water ingress. Ensuring construction safety and long-term serviceability requires both reliable detection of grouting effectiveness and a mechanistic understanding of grout diffusion. This review systematically synthesizes sensing technologies, diffusion modeling, and intelligent data interpretation. It highlights their interdependence and identifies emerging trends toward multimodal joint inversion and real-time grouting control. Non-destructive testing techniques can be broadly categorized into geophysical approaches and sensor-based methods. For synchronous detection, vehicle-mounted GPR systems and IoT-based monitoring platforms have been explored, although studies remain sparse. Theoretically, grout diffusion has been investigated via numerical simulation and field measurement, including the spherical diffusion theory, columnar diffusion theory, and sleeve-pipe permeation grouting theory. These theories decompose the diffusion process of the slurry into independent movements. Nevertheless, oversimplified models and sparse monitoring data hinder the development of universally applicable frameworks capable of capturing diverse engineering conditions. Existing techniques are further constrained by limited imaging resolution, insufficient detection depth, and poor adaptability to complex strata. Looking ahead, future research should integrate complementary non-destructive methods with numerical simulation and intelligent data analytics to achieve accurate inversion and dynamic monitoring of the entire process, ranging from grout diffusion and consolidation to defect evolution. Such efforts are expected to advance both synchronous grouting detection theory and intelligent and digital-twin tunnel construction. Full article

This study presents the digitization and integration of Kazakhstan’s legacy gravimetric maps at a scale of 1:200,000 into a modern geospatial database using ArcGIS. The primary objective was to convert analog gravity data into a structured, queryable, and spatially analyzable digital format to support contemporary geoscientific applications, including geoid modeling and regional geophysical analysis. The project addresses critical gaps in national gravity coverage, particularly in underrepresented regions such as the Caspian Sea basin and the northeastern frontier, thereby enhancing the accessibility and utility of gravity data for multidisciplinary research. The methodology involved a systematic workflow: assessment and selection of gravimetric maps, raster image enhancement, georeferencing, and digitization of observation points and anomaly values. Elevation data and terrain corrections were incorporated where available, and metadata fields were populated with information on the methods and accuracy of elevation determination. Gravity anomalies were recalculated, including Bouguer anomalies (with densities of 2.67 g/cm³ and 2.30 g/cm³), normal gravity, and free-air anomalies. A unified ArcGIS geodatabase was developed, containing spatial and attribute data for all digitized surveys. The final deliverables include a 1:1,000,000-scale gravimetric map of free-air gravity anomalies for the entire territory of Kazakhstan, a comprehensive technical report, and supporting cartographic products. The project adhered to national and international geophysical mapping standards and utilized validated interpolation and error estimation techniques to ensure data quality. The validation process by the modern gravimetric surveys also confirmed the validity and reliability of the digitized historical data. This digitization effort significantly modernizes Kazakhstan’s gravimetric infrastructure, providing a robust foundation for geoid modeling, tectonic studies, and resource exploration. Full article

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows. Performance was assessed using R², RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R² from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications. Full article

Seafloor morphology forms regional sea level rise (SLR), affecting ocean circulation. Although many studies have examined global sea level rise, there remains a lack of analyses that show how seafloor morphology modifies the rate and character of regional SLR. Previous studies have rarely investigated the geophysical interactions between seafloor morphology and sea level modulation, leaving a gap in explaining the spatial variability of sea level trends and accelerations. The aim of the study is to assess the impact of seafloor morphology on the regional rate and character of Sea Level Rise (SLR) in the western Pacific, in the Japan Trench region. Seafloor morphology, through its interactions with gravity and circulation processes, is a major factor in how SLR trends and accelerations are determined across different locations. The analysis is based on hybrid datasets: numerical models, bathymetric data, and altimetric time series of sea level anomalies (SLA) from 1993 to 2023. SLR trends, seasonal and nodal cycles were determined at 78 virtual stations. Regional rates of sea level changes were estimated using linear regression, harmonic analysis, Continuous Wavelet Transform (CWT), and Kalman filtering. Future SLR was simulated using a modified Monte Carlo method with an AR(1) autoregressive model and a block bootstrap technique. The results indicated that SLR trends are positively correlated (r ≈ 0.9) with mean dynamic topography (MDT) and negatively correlated with depth (r ≈ –0.4), confirming the impact of ocean circulation and seafloor morphology on regional SLR. The strong, positive correlation of trends with the amplitude of the 18.61-year nodal cycle (r > 0.8) indicates the important role of long-term tidal components. The highest SLR accelerations (up to 1.7 mm/yr²) were observed in locations of seamounts and subduction zones, while in the ocean trench, the rate of change stabilized or inversed locally. The results confirm the research hypothesis—the regional rate of sea level rise depends on the morphology of the seafloor and the associated geophysical and dynamic processes. The results have wide global application, supporting the implementation of the UN Sustainable Development Goals, the development of marine protection and management policies, infrastructure planning and coastal safety. Full article

Three-dimensional gravity inversion technology involves inferring the underground density structure based on observed gravity anomaly data. In addition to gravity inversion based on physics-driven methods, deep learning, as a purely data-driven technique, is increasingly gaining attention in geophysical inversion problems. However, purely data-driven methods rely on the implicit relationships within the data during the inversion process, which results in a lack of clear physical significance. This study proposes a three-dimensional gravity inversion method that integrates physical equations with deep learning. Based on the U-Net architecture, the gravity forward equation is incorporated as a physical constraint term, and a composite loss function—comprising three-dimensional mean squared error, a depth-weighting function, and three-dimensional intersection-over-union loss—is constructed to enhance inversion accuracy. Numerical experiments indicate that this method outperforms traditional algorithms in terms of density recovery accuracy and boundary clarity. When applied to gravity anomaly data from the Tangshan earthquake region in China, this method successfully inverted the three-dimensional subsurface density structure, revealing a high-density anomaly beneath the seismic source area, which provides important evidence for understanding the regional earthquake generation mechanism. Full article

Mine water inrush remains one of the major hazards threatening the safety of coal mining operations. To assess the feasibility of integrating transient electromagnetic (TEM) and direct-current (DC) methods for advanced detection in underground settings, a three-dimensional geoelectric forward model for both techniques was developed in COMSOL Multiphysics based on the fundamental principles of electromagnetic prospecting. The model was used to examine the electromagnetic responses of water-rich anomalies surrounding mine roadways under different source configurations and spatial positions. Comparative analyses show that both DC and TEM methods effectively detect water-bearing targets within 40 m of the roadway, whereas TEM exhibits superior sensitivity at greater distances. TEM achieves its highest sensitivity when the anomaly is located within an azimuthal range of 30–45°. The characteristic response patterns derived from the simulations were applied to interpret field data acquired at the Tashan Coal Mine. The interpretation successfully delineated the presence and orientation of the water-bearing body ahead of the excavation face, and subsequent underground drilling verified the accuracy of the predictions. These findings demonstrate that COMSOL-based electromagnetic forward modeling provides a reliable framework for interpreting advanced geophysical detection data and is feasible for practical applications in mine water-inrush hazard assessment. Full article

This paper presents the results of an integrated targeting study covering the central-eastern Archean Yilgarn Craton of Western Australia, a region renowned for its substantial gold endowment (>40 Moz Au). The cornerstones of this study included custom-built geophysical and remote sensing targeting tools, a new lithostructural interpretation of the area, a targeting model based on the mineral systems approach, and a best-practice mineral potential modeling (MPM) workflow employing five complementary modeling techniques. The geophysical targeting tools were used to identify proximity, association, and abundance relationships between gold mineralization and gravity ridges or edges, as well as 95th-percentile K/Th radiometric and remotely sensed goethite–clay–iron feature depth index ratio anomalies. The lithostructural interpretation revealed structural trends oblique or orthogonal to the NNW-SSE-striking greenstone belts, likely representing important structural controls on gold mineralization. Fry analysis, used to assess the spatial distribution of geological point patterns, showed similar directions of maximum gold occurrence alignment. Together, these observations proved to be strong predictors of gold prospectivity in the MPM component of this targeting study. The MPM not only identified most known gold occurrences but also highlighted several underexplored areas with significant potential. The highest-priority MPM targets represent roughly an order-of-magnitude reduction in search space, the hallmark of a well-performing and practically useful targeting methodology. Full article

This review summarizes recent progress and emerging trends in multiparameter optical fiber sensing, emphasizing techniques that enable the simultaneous measurement of temperature, strain, acoustic waves, pressure, and other environmental quantities within a single sensing network. Such capabilities are increasingly important for structural health monitoring, environmental surveillance, industrial diagnostics, and geophysical observation, where multiple stimuli act on the fiber simultaneously. The paper outlines the physical principles and architectures underlying these systems and focuses on strategies for compensating and decoupling cross-sensitivity among measured parameters. Special attention is devoted to advanced distributed sensing schemes based on coherent optical frequency-domain reflectometry (C-OFDR), coherent phase-sensitive time-domain reflectometry (Φ-OTDR), and Brillouin optical time-domain reflectometry (BOTDR). Their theoretical foundations, their signal-processing algorithms, and the design modifications that improve parameter discrimination and accuracy are analyzed and compared. The review also highlights the roles of polarization and mode diversity and the growing application of machine-learning techniques in the interpretation and calibration of data. Finally, current challenges and promising directions for the next generation of fiber-optic multiparameter sensors are outlined, with a view toward high-resolution, low-cost, and field-deployable solutions for real-world monitoring applications. Full article

The Cathedral of San Giorgio, a chief example of Baroque architecture in Sicily (Italy), has been the focus of extensive geophysical investigations aimed at structural and subsoil characterization to support heritage conservation efforts. This study is among the few to apply a high-resolution Ground Penetrating Radar (GPR) survey to the pillars of a Baroque Church, revealing internal structural details not documented in any available historical sources. Using a 2 GHz antenna, parallel radar profiles, spaced 0.05 m apart in both directions, were collected to reconstruct a detailed 3D model of the internal structure. Depth-slice and 3D-view analyses revealed multiple reflector sets corresponding to the different masonry blocks forming the pillars. Distinct internal layers were identified at depths of 0.22–0.30 m and 0.40–0.55 m, indicating blocks approximately 0.20–0.30 m in height and the possible presence of vertical connectors. These results complement previous studies that defined the dynamic parameters of the structure and a 3D velocity model of the subsoil, which suggested anomalies linked to remnants of the ancient Byzantine church of San Nicola. Overall, the findings provide valuable insights into the construction techniques and current condition of the pillars, contributing essential data for the planning of conservation and restoration strategies. Full article

In geophysics, aeromagnetic surveying based on unmanned aerial vehicles (UAV) is a widely employed exploration technique, that can analyze underground structures by conducting data acquisition, processing, and inversion. This method is highly efficient and covers large areas, making it widely applicable in mineral exploration, oil and gas surveys, geological mapping, and engineering and environmental studies. However, during flight, interference from the aircraft’s engine, electronic systems, and metal structures introduces noise into the magnetic data. To ensure accuracy, mathematical models and calibration techniques are employed to eliminate these aircraft-induced magnetic interferences. This enhances measurement precision, ensuring the data faithfully reflect the magnetic characteristics of subsurface geological features. This study focuses on aeromagnetic data processing methods, conducting numerical simulations of magnetic interference for aeromagnetic surveys of UAVs with the Tolles–Lawson (T-L) model. Recognizing the temporal dependencies in aeromagnetic data, we propose a Transformer neural network algorithm for aeromagnetic compensation. The method is applied to both simulated and measured flight data, and its performance is compared with the classical Multilayer Perceptron neural networks (MLP). The results demonstrate that the Transformer neural networks achieve better fitting capability and higher compensation accuracy. Full article

Geophysical techniques are a core toolkit of modern archeology, thanks to their effectiveness in reconstructing important pieces of evidence for buried ruins, which are relics of the past usage of an inspected site. Some methodological approaches and advancements are proposed for investigating the site of Gela, which was one of the most important western Greek colonies, founded in 689–688 BC on the southern coast of Sicily, Italy. The ancient settlement was developed on a hill, mostly flat on the top, and over its sides. The archeological evidence discovered so far in the acropolis of the city can be attributed to two main architectural typologies: urban blocks and archaic temples. Based on these targets, a geophysical protocol has been tested, utilizing passive seismic, electrical resistivity tomography (ERT), and ground-penetrating radar (GPR) methods. Where the lowest physical contrast was expected among possible archeological remains and burying soil (close to the urban blocks area), the three geophysical techniques have been jointly applied, while an innovative support-to-interpretation approach for GPR datasets is proposed and developed over both kinds of archeological targets. Our experimental outcomes underline the effectiveness (and possible weaknesses) of the two geophysical investigation strategies against various targets producing different signal-to-noise responses, thanks to the synergistic contributions from multi-method and multi-depth approaches. The integrated use of GPR, ERT, and passive seismic methods allowed the reconstruction of complementary information, with each method compensating for the limitations of the others. This combined approach provided a more robust and comprehensive understanding of the subsurface features than would have been achieved through the application of any single technique. Full article

This paper represents the results of ten years of monitoring using satellite imagery and aerial reconnaissance, followed by in-depth analysis utilizing LiDAR data and geomagnetic prospection techniques of the first two Neolithic rondels detected in Croatia—Markušica Brošov salaš and Gorjani Topole. Through the exclusive use of satellite and aerial image analysis, we were able to accurately determine the general size, shape, and number of ditches present at the sites under investigation. The wealth of information obtained from these images was sufficient for us to confidently interpret these formations as Neolithic rondels—meeting all the criteria commonly used. The addition of LiDAR data and geomagnetic prospection further enhanced our understanding by revealing a range of additional features and peculiarities across both sites, including within all identified ditch systems. These advanced methods allowed us to uncover details that would otherwise remain invisible through surface observation alone. Our research demonstrates the remarkable power of publicly available satellite imagery as a primary tool for archeological site detection and preliminary interpretation. The results from Markušica and Gorjani emphasize the scientific necessity of combining complementary remote sensing and geophysical techniques to overcome individual methodological limitations, providing robust documentation and interpretation of prehistoric enclosures in highly transformed landscapes. This research contributes novel insights into Neolithic social landscapes, monumentality, and land use strategies in Croatia while offering a methodological model for archeological prospection applicable across Central and Southeastern Europe. Full article