Search Results (144)

Identifying reliable earthquake precursors remains a major challenge in geophysics. Among the most widely investigated candidates are seismicity-based indicators, such as the Gutenberg–Richter $b$-value, and ionospheric disturbances expressed through Total Electron Content (TEC) anomalies. However, these observables are commonly investigated independently, and their quantitative spatial relationship remains poorly constrained. This study investigates the spatio-temporal evolution of $b$-values and GNSS-derived TEC anomalies preceding the 2019 Ridgecrest (Mw 7.1) and 2010 Baja California (Mw 7.2) earthquakes. Temporal analyses reveal progressive reductions in $b$-values prior to both earthquakes, while spatial mapping identifies localized low-$b$ regions with thresholds of $b\le 0.88$ for Ridgecrest and $b\le 0.83$ for Baja California. Independent TEC analyses reveal negative ionospheric anomalies of approximately 2.34 TECU and 4.27 TECU, occurring 9–10 days and ~2 days before the respective mainshocks under geomagnetically quiet conditions. A multi-scale centroid-based spatial validation framework, incorporating both global and local low-$b$ centroids together with Monte Carlo randomization tests, demonstrates that the dominant low-$b$ regions consistently exhibit the closest spatial correspondence with the TEC depletion. The observed centroid separations occupied only a limited fraction of the theoretical earthquake preparation zone, with normalized distances of 0.33 and 0.24 for the global centroids, decreasing to 0.32 and 0.17, respectively, for the dominant local low-$b$ regions. Overall, the results support a stress-conditioned lithosphere–atmosphere–ionosphere coupling framework and demonstrate that integrating long-term seismic stress evolution with GNSS-derived ionospheric observations provides an objective multi-parameter framework for investigating the spatial organization of earthquake preparation processes. Full article

GNSS ionospheric records are valuable measurements of earthquake-related ionospheric anomalies. The deep exploration of GNSS ionospheric data can help to better understand the seismic–ionospheric coupling effect. Thus, we used GNSS ionospheric data to detect and investigate the pre-earthquake ionospheric anomalies of the 7.6 magnitude earthquake, which occurred in Mexico on 19 September 2022. A 10.7 cm solar radio flux (F10.7), sunspot number (SSN), and geomagnetic activity indices (Dst and Hp30) are employed to reflect solar and geomagnetic activities, respectively. The result of the sliding quartile range method indicates that there was a ionospheric disturbance over the epicenter on the 10th day prior to the earthquake, and that there were no anomalies observed in solar activity and geomagnetic activity on that day. Meanwhile, the ionospheric anomaly over the epicenter on 15th day before the earthquake was the most significant, but the geomagnetic activity was abnormal on that day. To further distinguish whether the ionospheric disturbance on 15th day was caused by geomagnetic activity or the earthquake, this paper adopts the coherent wavelet to analyze the time-varying relationship between total electron content (TEC) and geomagnetic activity in the time-frequency space. The results show that the correlation and phase relationship between TEC, Dst, and Hp30 remained relatively stable on the 15th day before the earthquake, while the strong correlation between TEC, Dst, and Hp30 suddenly disappeared and the phase relationship sharply changed after the 10th day. This may be due to the influence of the seismic–ionospheric coupling effect, where the abnormal disturbances of TEC may disrupt the pre-existing stable relationship between TEC, Dst, and Hp30. Therefore, we conclude that the TEC anomaly on 10th day before the earthquake might be the precursor to the Mw7.6 Mexico earthquake. Full article

Analysis of variations in the components of the Earth’s magnetic field, recorded at the magnetic observatory of the Institute of Ionosphere (Almaty, Kazakhstan) during the earthquakes of 22 January 2024 and 4 March 2024, showed that these fluctuations were not related to intrinsic variations in the magnetic field itself, but rather to mechanical oscillations of the magnetometer together with its support. Evidence supporting the seismokinetic origin of the variations in the vector magnetometer was the discrepancy between the total magnetic field measured by the absolute magnetometer and the total magnetic field calculated from the vector magnetometer readings. Equations were derived that relate variations in the components of the magnetic field to the tilt angles of the sensor, enabling the determination of the direction of arrival of seismic shock waves. Azimuths of earthquake epicenters were calculated from oscillations observed in magnetogram records for several events, and these were compared with azimuths obtained from a network of seismic stations. It is shown that, in order to improve the accuracy of geomagnetic field measurements—required for the study of seismoelectric and seismomagnetic phenomena, as well as for identifying earthquake precursors—the design of magnetic observatories should be supplemented with an inclinometer to enable correction of vector magnetometer measurement results. Full article

The “Every Earthquake a Precursor According to Scale” (EEPAS) model uses minor earthquakes in a catalogue to forecast major earthquakes, based on the precursory scale increase phenomenon and associated predictive scaling relations. It has previously been applied to regional earthquake catalogues for medium-term forecasting of earthquakes with target magnitudes exceeding thresholds in the range M 5.0–6.0 using previous earthquakes about two magnitude units below the target threshold, depending on the completeness of the catalogue. Here, we apply it to the ISC-GEM global earthquake catalogue and test its ability to forecast earthquakes with target magnitudes exceeding M 7.5, i.e., the largest and most important earthquakes for assessing earthquake hazard. We use background models based on both smoothed seismicity and spatial variation of strain rate estimates. The models are fitted to the period 1994–2013, with 85 target earthquakes, and tested on the period 2014–2019, with 19 target earthquakes. The EEPAS model shows larger information gains over a smoothed seismicity model than in most regional applications. Also, a hybrid composed of EEPAS, smoothed seismicity and the strain rate outperforms a hybrid consisting only of smoothed seismicity and strain rate components. These preliminary results suggest that EEPAS may be useful for forecasting the largest earthquakes globally but should be followed up by transparent prospective testing. There are several avenues for improvement of EEPAS modeling. Full article

To explore the spatiotemporal evolution characteristics of pre-earthquake GPS TEC anomalies and their correlation with seismic activities, a statistical analysis was performed on pre-earthquake ionospheric GPS TEC anomalies associated with Ms ≥ 6.0 earthquakes in Mainland China from 2012 to 2022 using GPS TEC observational data. Two statistical metrics were adopted, namely average anomaly frequency and anomaly earthquake percentage. Classified statistical analyses were carried out from the perspectives of anomaly polarity, earthquake magnitude, focal depth, and different azimuths of the epicenter to systematically investigate the evolutionary characteristics of TEC anomalies from 30 days pre-earthquake to the earthquake day. The results show that the two core statistical indicators of pre-earthquake TEC anomalies present a significant increasing trend around the 25th, 15th, and 5th days before the earthquake and on the earthquake day. Moreover, the values of the two metrics corresponding to positive pre-earthquake TEC anomalies were higher than those corresponding to negative anomalies. Specifically, the values of the two metrics for pre-earthquake TEC anomalies of strong earthquakes with 6.8–7.6 were higher than those for 6.0–6.8 earthquakes. The spatial distribution of pre-earthquake TEC anomalies is characterized by inhomogeneity and time-dependent characteristics. Compared with earthquakes at a focal depth of 0–10 km, earthquakes at a focal depth of 10–20 km show more significant pre-earthquake TEC anomaly signals. Perturbation characteristics of pre-earthquake ionospheric GPS TEC were statistically analyzed, providing a reference for further elucidating the seismo-ionospheric coupling mechanism and identifying ionospheric precursors of earthquakes. Full article

Radon has long been investigated as a potential earthquake precursor, yet its interpretation remains challenged by meteorological, hydrological, and instrumental variability that can generate apparent departures unrelated to tectonic processes. This review synthesises how artificial intelligence is being applied in radon-based earthquake precursor research, with particular emphasis on anomaly detection and the evaluation of radon seismicity associations. Following a PRISMA-guided workflow, Scopus and the Web of Science Core Collection are searched and screened for eligibility, yielding 26 journal articles, most of which are concentrated in a limited number of tectonically active regions. Across the reviewed literature, a consistent pattern emerges: AI is used primarily to model the expected radon background, while candidate precursors are identified mainly through threshold-based indices derived from residuals or concentration ratios rather than through explicit earthquake-probability outputs. Although pre-seismic departures are reported repeatedly, this review shows that the evidence base remains constrained by heterogeneous operational definitions of anomaly, strong methodological variation across studies, a predominant emphasis on background goodness-of-fit instead of alarm-level performance, and limited use of time-ordered validation. These findings highlight both the promise and the current limitations of AI-enabled radon analysis. The main contribution of the field so far is not direct earthquake prediction but a more structured framework for separating potential tectonic signals from non-seismic variability. In this sense, the review provides an important methodological synthesis for future research and shows that more reproducible and operationally useful radon monitoring will depend on clearer anomaly definitions, stronger confounder control, more rigorous temporal validation, and more standardised performance reporting. Full article

The Schumann resonance (SR) signal has attracted much attention as a potential earthquake precursor indicator. To enable rapid identification of these signals from massive volumes of China Seismo-Electromagnetic Satellite (CSES) data, this paper presents a machine learning-based image recognition algorithm. Firstly, the Ultra-Low Frequency (ULF) band power spectrum data of the ionospheric electric field was standardized to enhance the visual contrast of the signal and generate a spectrogram. A small-image dataset with standardized image size and labeled positive and negative samples was constructed by cropping the original images. High-dimensional features of the image were extracted using the deep convolutional neural network VGG16 algorithm, combined with the support vector machine (SVM) algorithm to classify whether the high-dimensional data contains SR signals. The sliding window recognition algorithm is designed to process large-format power spectrum images. The results showed that this VGG16-SVM hybrid model achieved an accuracy of 95.00% on the independent small-image test set, which was superior to both pure SVM and pure VGG16 models. On the large-format image prediction set, the overall accuracy of the model is 81.48%, and the SR physical properties of the recognition signal are verified through frequency statistics. The hybrid model was applied to the SR detection and recognition of the Yangbi earthquake in Yunnan, China, and achieved ideal results. This indicates that the proposed VGG16-SVM hybrid model can quickly and effectively identify SR signals in CSES data, which has important practical value for automated electromagnetic signal analysis in seismic research. Full article

This work presents the results of an integrated monitoring of soil radon gas and seismic activity at Mt. Etna from August 2023 to May 2025, aimed at enhancing comprehension of magma migration and eruption dynamics. Radon data were collected using a permanent station with an alpha particle probe, aggregated hourly. The INGV-OE network monitored seismic activity at 100 Hz; volcanic tremor was analyzed using Root-Mean-Square (RMS) values from the Serra La Nave station. Earthquakes were located using the Hypoellipse algorithm and a 1D crustal velocity model. A robust correlation was found between radon and RMS anomalies, with the former preceding the latter with increasing probability over time (e.g., 30.1% within 1 day, 46.4% within 3 days). Correlations were also found between radon anomalies and Strombolian activity at the summit craters (e.g., 23.8% within 1 day for the Central Crater), suggesting a potential predictive role for radon. Conversely, correlations with paroxysmal events were weaker in the short term but increased over longer time windows. No clear correlation was found between radon anomalies and seismic strain release, likely due to differing temporal resolutions. These results support the idea that radon plays a role as a short-term precursor in volcanic unrest. Full article

In the last decades, satellite remote sensing has played a key role in Earth Observation, as an effective monitoring tool applied to geo-hazard identification and mitigation. In particular, the differential synthetic aperture radar interferometry technique provides incomparable information on ground movements related to volcanic unrest, co-eruptive deformation, and volcano flank motion. In this work, ground deformation data derived from Sentinel-1 satellites were analyzed over the Cumbre Vieja volcano, located in the southern part of La Palma Island, Canary archipelago. The volcano started to erupt on 19 September 2021, after a seismic swarm. The eruption buried hundreds of buildings and properties, causing severe economic losses. Analyzing the vertical ground displacement of the volcano in the year preceding the eruption, the results show that ground deformation can be considered a precursor of the eruption, which allows us to identify the phases of the magmatic ascent up to the opening of the eruptive vent. Interestingly, after a subsidence phase lasting 4 months, the ground displacement rate reverted and an uplift was observed, lasting 9 months, marking an uplift on the Cumbre Vieja volcano related to volcanic activity. This can be interpreted as the effect of the magma rising from the deeper chamber (15–25 km) to an intermediate stagnation zone (5 km) that provided a measurable anticipation of the eruption by 9 months. In the future, regular monitoring of Cumbre Vieja could adopt uplift detection as an indicator for shallow magma activity and as a possible eruption precursor. Full article

Long-term monitoring of radon (²²²Rn) and thoron (²²⁰Rn) radioactive gases has been used in earthquake forecasting. Seismic activity before earthquakes raises the levels of these gases, causing abnormalities in the baseline values of radon and thoron time series (RTTS) data. This study reports applications of kernel density estimation (KDE) and wavelet-based density estimation (WBDE) to detect anomalies in radon, thoron, and meteorological time-series data. Anomalies appearing in the RTTS data have been assessed for their potential correlation with seismic events. Using KDE and WBDE, radon anomalies were observed on 12 March, 15 August, 17 September, in the year 2017, and 19 January 2018. Thoron anomalies were recorded on 12 March, 15 August, 17 September 2017, and 28 February 2018. Irregularities in RTTS were observed several days before seismic events. Anomalies in RTTS, detected using KDE, successfully correlated five out of nine seismic events while WBDE identified four anomalies in RTTS which were successfully correlated with the corresponding seismic events. The wavelet transform has been used to reduce noise at higher decomposition levels in radon and thoron time series. Findings of the study reveal the potential of radon and thoron time series that can be used as precursors for earthquake forecasting. Full article

Pre-seismic anomalies in solid tidal factors can reveal crustal stress accumulation and predict seismic risk; such disturbance signals associated with earthquake incubation are extremely subtle and easily obscured by environmental noise, instrument errors, and other interference factors, placing heightened demands on the precision of gravity data acquisition and the capability to detect and isolate solid tidal signals effectively. In this paper, we propose a novel method for determining time-varying solid tidal factors based on the normal time–frequency transform (NTFT) theory, an approach allowing us to unbiasedly determine the instantaneous amplitude, frequency, and phase of time-varying signals, while mitigating the influence of edge effects to a certain extent. In the study outlined in this paper, we first design simulation experiments to validate the effectiveness of the new method. Subsequently, utilising high-precision superconducting gravimeter observation data, the proposed method is applied to the detection of pre-seismic disturbances preceding the 2004 Sumatra megathrust earthquake. Our results demonstrate that, compared to traditional harmonic analysis methods, this novel approach more accurately filters out interference signals, effectively captures the faint pre-seismic perturbations of solid tides, and significantly enhances the timeliness of pre-seismic disturbance detection, thus providing more reliable technical support for earthquake precursor monitoring. Full article

Thermal anomalies serve as potential earthquake precursors and are crucial for understanding the mechanisms underlying seismogenic mechanisms and geodynamic perturbations. To address the limited understanding of the polarity evolution of thermal anomalies, we developed a dynamic spatiotemporal adaptive framework to quantify global thermal anomaly responses. Four parameters—the coefficient of determination (R²), spatiotemporal uncertainty (SU), temporal–spatial uncertainty ratio (TSUR), and spatiotemporal correlation coefficient (SCC)—were established to characterize the spatiotemporal patterns of thermal anomaly responses. Additionally, the Anomaly Emphasis Proximity (AEP) was introduced to identify statistically significant thermal anomaly events. The results indicate that the spatiotemporal evolution of thermal anomalies exhibits a transition from pre-earthquake mixed anomalies (both positive and negative) to post-earthquake unipolar anomalies (TIB decreased from 92% to 49%), accompanied by pronounced sea–land differentiation (SST increased from 0.3% to 98.7%). The AEP reveals significant thermal anomaly clustering highly consistent with earthquake activity (e.g., the 2008 Mw 8.0 Wenchuan earthquake in the Qinghai–Tibet Plateau), showing strong correlations in structurally active regions (e.g., SCA and SWS; FDR < 18.5%, STCW > 3.7%) but weaker ones in stable regions (e.g., CNA and ECA). Overall, this framework significantly enhances the robustness and reliability of seismic thermal anomaly detection. Full article

Effective mitigation of geotechnical risk and safety management of underground mine requires accurate estimation of rockburst damage potential. The inherent complexity of the rockburst phenomena due to nonlinear, high dimensional, and interdependent nature of the geological factors involved, however, makes predictive modeling a difficult task. The proposed research is based on the use of the K-Nearest Neighbor (KNN) algorithm to predict the risk of rockbursts with the use of microseismic monitoring data. Several key features like the ratio of total maximum principal stress to uniaxial compressive strength, energy capacity of support system, excavation span, geology factor, Richter magnitude of seismic event, distance between rockburst location and microseismic event, and rock density were applied as input parameters to extract critical rockburst precursor activities. In the test stage, the proposed KNN model recorded an accuracy of 75.50%, a precision of 0.913, a recall value of 0.509, and F1 Score of 0.576. The model is reliable with a significant performance indicating its efficacy in practice. The KNN model showed better classification results as compared to recently available models in literature and provided better generalization and interpretability. The model exhibited high prediction in classified low-risk incidents and had strong indicative capabilities towards high-risk situations, attributed to being a useful tool in rockburst hazard measurement. Full article

Earthquake prediction remains one of the central unsolved problems in geophysics, and ionospheric variability offers a promising yet debated window into the earthquake preparation process through lithosphere–atmosphere–ionosphere coupling. Progress has been hindered by methodological limitations in prior studies, including the use of inappropriate performance metrics for highly imbalanced seismic data, the reliance on geographically and temporally narrow data, and inclusion of inherent spatial or temporal features that artificially inflate model performance while preventing the discovery of genuine ionospheric precursors. To address these challenges, we introduce a global, temporally validated machine learning framework grounded in thirty-eight years of ionospheric observations from more than a hundred ionosonde stations. We eliminate lookahead bias through strict temporal partitioning, prevent overlapping precursor windows across samples to eliminate autocorrelation artifacts and apply sophisticated feature selection to exclude spatial and temporal identifiers, enabling prevention of data leakage and coincidence effects. We investigate whether spatiotemporally invariant ionospheric precursors exist across diverse seismic regions, addressing the field’s reliance on geographically isolated case studies. Cross-regional validation shows that our models yield modest classification skill above chance levels, with our best-performing model achieving a weighted F1 score of 71% though performance exhibits pronounced sensitivity to temporal validation configuration, suggesting these results represent an upper bound on operational accuracy. While multimodal fusion with complementary precursor channels could possibly improve performance, our focus remains on establishing whether ionospheric observations alone contain learnable, region-independent seismic signatures. These findings suggest that ionospheric precursors, if they exist as universal phenomena, exhibit weaker cross-regional consistency than previously reported in case studies, raising questions about their standalone utility for earthquake prediction while indicating potential value as one component within multimodal observation systems. 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