Search Results (52)

This study is conducted to investigate the deformation evolution and collapse mechanism of the Rongxing gypsum mine by integrating multi-source monitoring data, including synthetic aperture radar (SAR), global navigation satellite system (GNSS), and microseismic observations. Long-term surface deformation from 2015 to 2025 is retrieved using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), while GNSS data (2021–2022) are used to capture rapid ground displacement during the collapse event. Microseismic monitoring provides insights into the evolution of subsurface fracturing processes. The results show that the pre-collapse stage is characterized by continuous and spatially heterogeneous subsidence. Prior to the collapse, microseismic activity is observed to exhibit clear precursory signals, including an increase in event number, a decrease in b-value, and accelerated cumulative energy release, suggesting that the transition from distributed microcrack development to large-scale fracture coalescence is occurring. The b-value, derived from the Gutenberg–Richter frequency–magnitude relationship, describes the relative proportion of small to large seismic events and reflects variations in the statistical distribution of event magnitudes. During the collapse stage, abrupt, large-magnitude subsidence is observed by GNSS. After the collapse, the deformation is found to enter a long-term adjustment phase characterized by the coexistence of subsidence and uplift, indicating that stress redistribution within the overburden is occurring. Based on these observations, a conceptual model is proposed to describe the progressive failure mechanism of the goaf, with four stages: slow subsidence, accelerated deformation, collapse, and post-collapse adjustment. This study demonstrates the effectiveness of integrating SBAS-InSAR, GNSS, and microseismic monitoring for understanding the full lifecycle of goaf collapse. It provides valuable insights for early warning of mining-induced geohazards. Full article

The Anninghe Fault Zone is an active, deep–large fault in southwestern China, with a history of multiple strong earthquakes. To reveal the temporal patterns of seismicity and improve medium- to short-term earthquake frequency prediction, this study constructs a quarterly seismic frequency sequence (M ≥ 3.0) from May 1972 to September 2025 and applies the SARIMA (seasonal autoregressive integrated moving average) model for modeling and prediction. The hypothesis is that the frequency sequence exhibits modelable seasonality, trends, and nested periodic structures. The ADF test and Ljung–Box test confirm that the sequence is stationary and non-white noise, satisfying the prerequisites for SARIMA modeling. The centered moving average method is used to extract short-term (1 year), medium-term (5 years), and long-term (10 years) periodic components, and corresponding SARIMA models are constructed. Results show that the medium-period model ARIMA(2,0,1) × (1,0,0)₂₀ achieves the best prediction accuracy (RMSE = 0.6868, MAE = 0.6143), followed by the short-period model, while the long-period model yields slightly higher errors. All selected models pass residual white noise tests and parameter significance tests, and exhibit good robustness under different training–test splits. The main innovations are: (1) the first systematic application of SARIMA to earthquake frequency prediction in the Anninghe Fault Zone, and (2) a preliminary physical interpretation of multi-scale periodic components (e.g., seasonal loading, strain accumulation fluctuations). This method offers significant application value in regions with sparse seismic networks or limited precursory data, providing a new statistical tool for regional seismic hazard assessment and disaster mitigation planning. Full article

Forecasting post-seismic landslide displacement is challenged by the difficulty in distinguishing short-term acceleration from creep and the risk of spatiotemporal leakage. To address this, an interpretable deep-learning framework is developed, integrating SBAS-InSAR time series with an Attention-enhanced Gated Recurrent Unit (Attention-GRU). Prior to modeling, a multi-stage preprocessing strategy, including empirical mode decomposition, is applied to mitigate noise and delineate active deformation zones. Unlike standard architectures, the model’s temporal attention mechanism adaptively amplifies critical precursory acceleration phases. Furthermore, a strict landslide-object-based partitioning strategy is employed to rigorously mitigate spatiotemporal leakage. The framework was evaluated in the Le’an Town landslide cluster using multi-source data. Targeting identified hazardous regions, the method achieved an R² of 0.93 and reduced MAPE by 42.7% relative to the SVR baseline. This reflects a location-specific predictive capability, within active zones rather than regional generalization. SHapley Additive exPlanations (SHAP) further confirmed the model captures physical relationships, such as sensitivity to 25–35° slopes and vegetation degradation. Ultimately, the proposed framework offers a transparent, physically interpretable tool for operational hazard mitigation. Full article

Understanding the spatial and temporal structure of crustal deformation is essential for identifying tectonic blocks, assessing seismic hazard, and detecting precursory deformation associated with major megathrust earthquakes. In this study, we analyze twenty years of continuous GNSS observations from the Japanese Islands to identify coherent deformation domains and anomalous regions using an integrated time-dependent clustering framework. The workflow combines six machine learning algorithms (Hierarchical Agglomerative Clustering, K-means, Gaussian Mixture Models, Spectral Clustering, HDBSCAN and consensus clustering) and constructs a set of deformation-related features including steady-state velocities, strain rates, co-seismic and post-seismic displacements, and spatial distance metrics. Optimal cluster numbers are determined by validity metrics, and the most robust segmentation is obtained using a consensus approach. The resulting spatiotemporal domains reveal clear segmentation associated with major geological structures such as the Fossa Magna graben, the Median Tectonic Line, and deformation belts related to Pacific Plate subduction. The method also highlights deformation patterns potentially associated with the preparation stages of megathrust earthquakes. Our results demonstrate that machine learning-based clustering of long-term GNSS time series provides a powerful data-driven tool for quantifying deformation heterogeneity and improving the understanding of active geodynamic processes in subduction zones. Full article

Modern statistical techniques enable quantitative characterisation of seismic activity. Analysis of the 2011 Tohoku megathrust earthquake revealed clear precursory signals: shortened inter-event intervals, increased magnitude scale (σ), and a pronounced precursory swarm immediately before the mainshock. While unique to this magnitude 9 event, here I present subtler anomalies that may precede magnitude 7-class events, particularly when swarms occur. In such cases, magnitude distributions often differ from background seismicity, frequently showing elevated location (μ) and scale (σ). Conversely, σ is sometimes reduced, particularly in volcanic regions, where large earthquakes may occur without discernible swarms. Detection of swarm activity and analysis of magnitude parameters thus remain central to seismic risk assessment. If swarm characteristics resemble background levels, the likelihood of a major event is presumably low. However, the distinct, immediate precursory swarm observed before the Tohoku earthquake has not been replicated elsewhere. These findings indicate that statistical anomalies may signal elevated risk but are unlikely to enable precise temporal prediction of seismic events. Full article

This study presents a comprehensive analysis of pre- and co-seismic ionospheric disturbances associated with the 2023 Ms6.2 Jishishan earthquake by leveraging the unique observational strengths of BDS, particularly its high-orbit satellites. A multi-parameter space weather index was employed to effectively isolate seismogenic signals from geomagnetic disturbances, confirming that the main shock occurred during geomagnetically quiet conditions. Statistical analysis of 41 historical earthquakes (Mw ≥ 5.5) reveals that 47.2% were associated with detectable Total Electron Content (TEC) anomalies. An inverse correlation between earthquake magnitude and anomaly detectability within a 31-day window suggests prolonged precursor durations for larger events may produce longer-duration precursory signals, which challenge conventional detection methods. The synergistic capabilities of BDS Geostationary Earth Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO) satellites were demonstrated: GEO satellites provide unprecedented temporal stability for continuous TEC monitoring, while IGSO satellites enable high-resolution spatial mapping of Co-seismic Ionospheric Disturbances (CIDs). The detected CIDs propagated at velocities below 1.6 km/s, consistent with acoustic gravity wave (AGW) mechanisms. A case study during a geomagnetically active period further reveals modulated CID propagation characteristics, indicating potential coupling between seismic forcing and space weather. Our findings validate BDS as a powerful and precise tool for ionospheric seismology and provide critical insights into Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) dynamics. Full article

This study investigates spatiotemporal b value variations and seismic interaction networks preceding the M_wg 7.1 Dingri earthquake that struck southern Tibet on 7 January 2025. Using relocated earthquake catalogs (2021–2025) and dual-method analysis combining b value mapping with Granger causality network modeling, we reveal systematic precursory patterns. Spatial analysis shows that the most significant b value reduction (Δb > 0.5) occurred north of the mainshock epicenter at seismogenic depths (5–15 km), closely aligning with subsequent aftershock concentration zones. Granger causality analysis reveals a progressive network simplification: from 73 causal links among 28 nodes during the background period (2021–2023) to 49 links among 34 nodes pre-mainshock (2023–2025) and finally to 6 localized links post-rupture. This transition from distributed system-wide interactions to localized “locked-in” dynamics reflects the stress concentration onto the primary asperity approaching critical failure. The convergence of b value anomalies and network evolution provides a comprehensive framework linking quasi-static stress states with dynamic system behavior. These findings offer valuable insights for understanding earthquake nucleation processes and improving seismic hazard assessment in the Tibetan Plateau and similar complex tectonic environments. Full article

The Greek territory and the surrounding marine area constitute an excellent laboratory for studying moderate-magnitude earthquakes (4–6 M), as such earthquakes occur very frequently in this region. Ten years ago, it was proposed that there is some kind of relation between earthquakes and unusual Schumann Resonance signals one to twenty days prior to an impending earthquake. During the last five years (2020–2025), a fairly large collection of signals has been gathered that may be considered as precursory seismic signals. Unfortunately, individual case studies overestimate their contribution to the final event and may lead to unjustified ‘extended pictures’ of the phenomenon. In the present article, we systematically attempt to evaluate these signals by examining them as a whole, rather than individually as in case studies. We confirmed that while case studies are a reasonable way to start a research project, they do not guarantee the final result. In our case, while individual studies were very hopeful, the present integrated study led to several unresolved issues that need to be addressed. The results of our work will help to determine whether these signals represent a significant part of the broader LAIC scenario, which is currently the only reliable suggestion for triggering and predicting earthquakes, or whether the origin of these signals should be sought elsewhere. Full article

The Tokara Islands, a volcanic archipelago located south of Japan’s main islands, experienced earthquake swarm activity in 2025. Public concern has emerged regarding the potential triggering of the anticipated Nankai Trough earthquake, which the Japan Meteorological Agency has dismissed; however, the underlying mechanisms of this seismic activity remain inadequately explained. This study employs Exploratory Data Analysis (EDA) to characterise the statistical properties of the swarm and compare them with historical patterns. Earthquake intervals followed exponential distributions, but swarm events exhibited distinctive short intervals that clearly distinguished them from background seismicity. Similarly, whilst earthquake magnitudes conformed to normal distributions, swarm events demonstrated low mean values and reduced variability, characteristics markedly different from regional background activity. The frequency and magnitude distributions of the 2025 swarm demonstrate remarkable similarity to two previous swarms that occurred in 2021. All the episodes coincided with volcanic activity at Suwanose Island, located approximately 10 km from the epicentral region, suggesting a causal relationship between magmatic processes and seismic activity. Statistical analysis reveals that the earthquake swarm exhibits exceptionally low magnitude scale, characteristics consistent with magma-driven seismicity rather than tectonic stress accumulation. The parameter contrasted markedly with pre-seismic conditions observed before the 2011 Tohoku earthquake, where it was substantially elevated. Our findings indicate that the current seismic activity represents localised volcanic-related processes rather than precursory behaviour associated with major tectonic earthquakes. These results demonstrate the utility of statistical seismology in distinguishing between volcanic and tectonic seismic processes for hazard assessment purposes. Full article

Earthquake prediction remains a significant challenge in seismology, and advancements in signal processing techniques have opened new avenues for improving prediction accuracy. This paper explores the application of Time–Frequency Distributions (TFDs) to seismic signals to identify diagnostic precursory patterns of major earthquakes. TFDs provide a comprehensive analysis of the non-stationary nature of seismic data, allowing for the identification of precursory patterns based on energy concentration features. Current earthquake prediction models primarily focus on long-term forecasts, predicting events by identifying a cycle in historical data, or on nowcasting, providing alerts seconds after a quake has begun. However, both approaches offer limited utility for disaster management, compared to short-term earthquake prediction methods. This paper proposes a new possible precursory pattern of major earthquakes, tested through analysis of recent major earthquakes and their respective prior minor earthquakes for five earthquake-prone countries, namely Türkiye, Indonesia, the Philippines, New Zealand, and Japan. Precursors in the time–frequency domain have been consistently identified in all datasets within several hours or a few days before the major earthquakes occurred, which were not present in the observation and analysis of the earthquake catalogs in the time domain. This research contributes towards the ongoing efforts in earthquake prediction, highlighting the potential of quadratic non-linear TFDs as a significant tool for non-stationary seismic signal analysis. To the best of the authors’ knowledge, no similar approach for consistently identifying earthquake diagnostics precursors has been proposed, and, therefore, we propose a novel approach in reliable earthquake prediction using TFD analysis. Full article

The recently proposed concept of “precursory fingerprint” is a logical consequence of the commonsense statement that seismic structures are unique and that their expected preshock behaviors, including precursory phenomena, are also unique. Our new prediction-related research strategy is conceptually based on the principles of (1) the uniqueness of seismogenic structures, (2) interconnected and interacting geospheres, and (3) non-equivalence of Earth’s surface spots in terms of precursory signal receptivity. The precursory fingerprint of a given seismic structure is a unique assemblage of precursory signals of various natures (seismic, physical, chemical, and biological), detectable in principle by using a system of proper monitoring equipment that consists of a matrix of n sensors placed on the ground at “sensitive” spots identified beforehand and on orbiting satellites. In principle, it is composed of a combination of signals that are emitted by the “responsive sensors”, in addition to the “non-responsive sensors”, coming from the sensor matrix, monitoring as many virtual precursory processes as possible by continuously measuring their relevant parameters. Each measured parameter has a pre-established (by experts) threshold value and an uncertainty interval, discriminating between background and anomalous values that are visualized similarly to traffic light signals (green, yellow, and red). The precursory fingerprint can thus be viewed as a particular configuration of “precursory signals” consisting of anomalous parameter values that are unique and characteristic to the targeted seismogenic structure. Presumably, it is a complex entity that consists of pattern, space, and time components. The “pattern component” is a particular arrangement of the responsive sensors on the master board of the monitoring system yielding anomalous parameter value signals, that can be re-arranged, after a series of experiments, in a spontaneously understandable new pattern. The “space component” is a map position configuration of the signal-detecting sensors, whereas the “time component” is a characteristic time sequence of the anomalous signals including the order, occurrence time before the event, transition time between yellow and red signals, etc. Artificial intelligence using pattern-recognition algorithms can be used to follow, evaluate, and validate the precursory signal assemblage and, finally, to judge, together with an expert board of human operators, its “precursory fingerprint” relevance. Signal interpretation limitations and uncertainties related to dependencies on sensor sensibility, focal depth, and magnitude can be established by completing all three phases (i.e., experimental, validation, and implementation) of the precursory fingerprint-based earthquake prediction research strategy. Full article

The Wenchuan earthquake (Ms8.0), which struck Sichuan Province, China, on 12 May 2008, was one of the most devastating seismic events in recent Chinese history. It resulted in the deaths of nearly 90,000 people, left millions homeless, and caused widespread destruction of infrastructure across a vast area. In addition to the severe ground shaking and surface rupture, a variety of unusual atmospheric/ionospheric and geophysical phenomena were reported in the days and hours leading up to the earthquake. Notably, iridescent clouds were observed just before the earthquake at three distinct locations approximately 450–550 km northeast of the epicenter. These clouds appeared as fragmented rainbows located beneath the sun and were characterized by their short lifespan, lasting only 1–10 min. Moreover, they exhibited striped patterns within the iridescent regions, suggesting the influence of an external electric field. These features cannot be adequately explained by the well-known meteorological phenomenon of circumhorizontal arcs, raising the possibility of a different origin. The formation mechanism of these clouds remains unclear. In this study, we explore the hypothesis that the iridescent clouds were precursory phenomena associated with the impending earthquake. Specifically, we examine a potential causal relationship between the appearance of these clouds and the geological environment of the earthquake source. We propose a novel model in which electrical disturbances generated along the fault system immediately before the mainshock propagated upward and interacted with the ionosphere, resulting in the creation of a localized electric field. This electric field, in turn, induced electro-optic effects that altered the scattering of sunlight and projected iridescent patterns onto cirrus clouds, leading to the observed phenomena. Full article

As a prevalent geological hazard in underground engineering, the accurate prediction of mine earthquakes is crucial for ensuring operational safety and enhancing mining efficiency. The deformation localization method effectively predicts the instability of disaster rocks, yet the timing of mine earthquakes remains understudied. This study established a correlation between rock deformation localization and seismic activity within mines through theoretical derivations. A predictive model algorithm for forecasting mine earthquake timing was developed based on Saito’s theory, integrating optics, acoustics, and mathematical modeling theories. The “quiet period” was identified as a significant precursor; thus, the model used the initiation of deformation localization to accurately predict rock failure. Using the model, a coal mine in Inner Mongolia was selected as a case study to predict a historical mining earthquake. The results indicated that the following: (1) Deformation localization and the “quiet period” of microseismic (MS) and acoustic emission (AE) activities were identified as two key pre-cursory indicators. The model utilized the initiation time of deformation localization and the inflection point of the “quiet period” in MS and AE activity as primary parameters. (2) For predicting rock failure times, the earliest prediction time deviates from the actual failure time by 143 s. The accuracy rate of predicted time points falling within a 90% confidence interval of the actual failure times is 100%. The model achieved 60% in forecasting the occurrence times of mine earthquakes. (3) The model’s prediction accuracy improved as the starting time parameter more closely approximated the actual initiation time of deformation localization, with the accuracy increasing from 0% to 100%. Full article

It is considered that a part of the inner core surface where iron in the fluid outer core is precipitated may have melted and formed a mushy region, but its position is not well understood seismologically. We recently analyzed seismic waveforms observed at the antipodal station of the seismic source and showed that there are precursors to the PKIIKP phase reflected beneath the inner core boundary. It has been found that this precursory wave can be modeled as a reflection under the liquid/solid interface at a depth of 100 km below the inner core boundary. Here, we use these precursor waves observed at the antipodal station (>179°). The sensitivity kernel of the amplitude of these precursor waves for the shear wave velocity structure on the inner core surface was calculated by the adjoint method, using theoretical seismic waveforms. Our results might be used to locate regions of the inner core surface where the shear wave velocity may be close to zero. Full article

Earthquake prediction is still a large challenge worldwide so far. In this paper, an automatic detection method was put into service immediately after the Wushi M_S 7.1 earthquake on 23 January 2024 to weekly detect possible CSES (China Seismo-Electromagnetic Satellite) precursory information before impending aftershocks. An electron perturbation with an enhanced magnitude of 38.3% was first detected on 24 January 2024 at night orbit 33175 and the corresponding variations in different plasma parameters measured at this orbit presented a typical feature of electron depletion or plasma bubble with an abrupt decrease and then an increase after one minute. The Kp index was also checked during this period and the values reached 3.7 once on 23 and 24 January, which indicates that these ionospheric variations probably originated from solar activities instead of three strong aftershocks with a magnitude more than five in the following three days. However, uncertainties still exist. Then, an electron perturbation with amplitude of 24.6%, as well as an O⁺ one of 27.3%, was successfully searched automatically at the same revisiting orbit 33251 on 3 February 2024 in a magnetically quiet period. These two plasma variations, as well as ones of other ionospheric parameters, were characterized by highly synchronous properties, which increase the availability as seismic precursors. However, no obvious variations were observed at other revisiting orbits or other orbits near the aftershock areas during this period. An aftershock with magnitude of M_S 5.3 and the strongest one of M_S 5.8 took place on 24 and 25 February, respectively, 20 days after and 1000 km away. Full article