Search Results (118)

Anomalous ionospheric disturbances have been observed as potential precursors to earthquakes. This study utilized data from the CSES satellite to investigate anomalies in the ULF band ionospheric electric field and electron density preceding earthquakes with magnitudes of Ms ≥ 6.0 in China and neighboring regions from 2019 to 2021. Comparative analysis with a randomly generated earthquake catalog indicated that these anomalies were spatially concentrated over the epicenter and temporally clustered on specific dates prior to the events. To assess the global relevance of these findings, the analysis was extended to earthquakes with Ms ≥ 7.0 worldwide during the same period, revealing consistent spatiotemporal patterns of ionospheric anomalies in both regional and global datasets. Furthermore, by combining the two earthquake catalogs and classifying events into oceanic and continental categories, additional statistical analyses were conducted to identify distinct ionospheric disturbance patterns associated with these different tectonic environments. These results provide a solid foundation for future research aimed at identifying and extracting ionospheric anomalies as potential pre-earthquake indicators. Full article

The seismicity levels in Oklahoma and southern Kansas have increased dramatically over the last 15 years. Past studies have identified the massive disposal of wastewater co-produced during oil and gas extraction as the driving force behind some earthquake clusters, with a small number of events directly linked to hydraulic fracturing (HF) stimulations. The present investigation is the first one to examine the role both of these activities played throughout the two states, under the same framework. Our findings confirm that wastewater disposal is the main causal factor, while also identifying several previously undocumented clusters of seismicity that were triggered by HF. We were able to identify areas where both causal factors spatially coincide, even though they act at distinct depth intervals. Overall, oil and gas operations are probabilistically linked at high confidence levels with more than 7000 felt earthquakes (M ≥ 2.5), including 46 events with M ≥ 4.0 and 4 events with M ≥ 5. Our analysis utilized newly compiled regional earthquake catalogs and established physics-based principles. It first hindcasts the seismicity rates after 2012 on a spatial grid using either real or randomized HF and wastewater data as the input, and then compares them against the null hypothesis of purely tectonic loading. In the end, each block is assigned a p-value, reflecting the statistical confidence in its causal association with either HF stimulations or wastewater disposal. Full article

The accurate and timely estimation of earthquake magnitude is essential for effective tsunami early warning, particularly in seismically active regions such as Indonesia. Conventional seismic approaches are often hindered by magnitude saturation in significant events (Mw > 7.5), resulting in systematically underestimated magnitudes. To address this limitation, we develop a regional peak ground displacement (PGD) scaling law using high-rate GNSS (HR-GNSS) data from 21 moderate to large earthquakes in Indonesia. Based on 87 PGD observations, we construct a regression model that relates PGD, hypocentral distance, and moment magnitude (Mw). The PGD-derived magnitudes (MPGD) exhibit strong concordance with catalog moment magnitudes, achieving a mean absolute deviation (MAD) of 0.21 and surpassing the accuracy of previously published global models. Retrospective analyses reveal that MPGD estimates converge within 2–3 min for well-recorded events and remain robust, even for great and tsunamigenic earthquakes. These results underscore the potential of HR-GNSS data to complement conventional seismic networks, providing rapid and reliable magnitude estimates for operational tsunami early warning in Indonesia. Full article

Foreshocks, observed before some large earthquakes, remain debated in terms of their origins and predictive value. While aftershocks fit well within bottom-up triggering models like ETAS, foreshocks may arise from distinct preparatory processes. Observations suggest real seismic catalogs exhibit more foreshocks than ETAS predicts, and laboratory experiments show that fault heterogeneity enhances foreshock activity. Here, I use a numerical model that reproduces key statistical properties of seismicity to investigate the role of fault heterogeneity. My simulations confirm that increasing interface disorder promotes foreshocks, aligning with laboratory findings and suggesting that fault complexity influences seismic precursors. Full article

A timely, high-resolution earthquake catalog is crucial for estimating seismic evolution and assessing hazards. This study aims to introduce a deep-learning-based real-time microearthquake monitoring system (RT-MEMS) for Taiwan, designed to provide rapid and reliable earthquake catalogs. The system integrates continuous data from high-quality seismic networks via SeedLink with deep learning models and automated processing workflows. This approach enables the generation of an earthquake catalog with higher resolution and efficiency than the standard catalog announced by the Central Weather Administration, Taiwan. The RT-MEMS is designed to capture both background seismicity and earthquake sequences. The system employs the SeisBlue deep learning model, trained with a local dataset, to process continuous waveform data and pick P- and S-wave arrivals. Earthquake events are then associated and located using a modified version of PhasePAPY. Three stable RT-MEMS have been established in Taiwan: one for monitoring background seismicity along a creeping fault segment and two for monitoring mainshock–aftershock sequences. The system can provide timely information on changes in seismic activity following major earthquakes and generate long-term catalogs. The refined catalogs from RT-MEMS contribute to a more detailed understanding of seismotectonic structures and serve as valuable datasets for subsequent research. Full article

Interferometric synthetic aperture radar (InSAR) technology has been widely employed in the rapid monitoring of earthquakes and associated geological hazards. With the continued advancement of InSAR technology, the growing volume of satellite-acquired data has opened new avenues for applying deep learning (DL) techniques to the analysis of earthquake-induced surface deformation. Although DL holds great promise for processing InSAR data, its development progress has been significantly constrained by the absence of large-scale, accurately annotated datasets related to earthquake-induced deformation. To address this limitation, we propose an automated method for constructing deep learning training datasets by integrating the Global Centroid Moment Tensor (GCMT) earthquake catalog with Sentinel-1 InSAR observations. This approach reduces the inefficiencies and manual labor typically involved in InSAR data preparation, thereby significantly enhancing the efficiency and automation of constructing deep learning datasets for coseismic deformation. Using this method, we developed and publicly released a large-scale training dataset consisting of coseismic InSAR samples. The dataset contained 353 Sentinel-1 interferograms corresponding to 62 global earthquakes that occurred between 2015 and 2024. Following standardized preprocessing and data augmentation (DA), a large number of image samples were generated for model training. Multidimensional analyses of the dataset confirmed its high quality and strong representativeness, making it a valuable asset for deep learning research on coseismic deformation. The dataset construction process followed a standardized and reproducible workflow, ensuring objectivity and consistency throughout data generation. As additional coseismic InSAR observations become available, the dataset can be continuously expanded, evolving into a comprehensive, high-quality, and diverse training resource. It serves as a solid foundation for advancing deep learning applications in the field of InSAR-based coseismic deformation analysis. Full article

This study analyzes the temporal dynamics of instrumental seismicity recorded in the Pertusillo reservoir area (Southern Italy) between 2001 and 2018. The Gutenberg–Richter analysis of the frequency–magnitude distribution reveals that the seismic catalog is complete for events with magnitudes $M\ge 1.1$. The time-clustering of the sequence is at both global and local levels with a coefficient of variation $C_v$ and $L_v$ significantly beyond the 95% confidence band. The Allan Factor method, applied to the series of earthquake occurrence times, corroborates the found time-clustering, showing a bi-fractal behavior indicated by the co-existence of two scaling regimes with a cutoff time scale ${\tau }_c\approx 45$ days and two different fractal exponents, $\alpha \approx 0.3$ for time scales less than ${\tau }_c$ and $\alpha \approx 1.2$ for larger ones. The application of the correlogram-based periodogram to both the monthly number of events and the monthly mean water level of the Pertusillo reservoir identifies the yearly cycle as the most significant in both variables. The connection between seismicity and the water level is also demonstrated by the value above 0.5 of the Average Edge Overlap (AEO), a topological metric derived from the Visibility Graph method applied to both the monthly variables. Furthermore, the variation in the AEO between the monthly mean water level and the monthly number of events, along with the time delay between them, indicates that the first leads the second by 1 month. Full article

Characterized by frequent earthquakes and a dense population, Yunnan Province, China, faces significant seismic hazards and is a hot place for earthquake forecasting research. In a previous study, we evaluated the performance of the b value for 5-year seismic forecasting during 2000–2019 and made a forward prediction of M ≥ 5.0 earthquakes in 2020–2024. In this study, with the forecast period having passed, we first revisit the results and assess the forward prediction performance. Then, the background seismicity rate, which may also offer valuable long-term forecasting information, is incorporated into earthquake prediction for Yunnan Province. To assess the effectiveness of the prediction, the Molchan Error Diagram (MED), Probability Gain (PG), and Probability Difference (PD) are employed. Using a 25-year catalog, the spatial b value and background seismicity rate across five temporal windows are calculated, and 86 M ≥ 5.0 earthquakes as prediction samples are examined. The predictive performance of the background seismicity rate and b value is comprehensively tested and shown to be useful for 5-year forecasting in Yunnan. The performance of the b value exhibits a positive correlation with the predicted earthquake magnitude. The synergistic effect of combining these two predictors is also revealed. Finally, using the threshold corresponding to the maximum PD, we integrate the forecast information of background seismicity rates and the b value. A forward prediction is derived for the period from January 2025 to December 2029. This study can be helpful for disaster preparedness and risk management in Yunnan Province, China. Full article

As the forefront of inland extension on the Indian plate, the northeastern Tibetan Plateau, marked by low strain rates and high stress levels, is one of the regions with the highest seismic risk. Analyzing seismicity through statistical methods holds significant scientific value for understanding tectonic conditions and assessing earthquake risk. However, seismic monitoring capacity in this region remains limited, and earthquake frequency is low, complicating efforts to improve earthquake catalogs through enhanced identification and localization techniques. Bi-scale empirical probability integral transformation (BEPIT), a statistical method, can address these data gaps by supplementing missing events shortly after moderate to large earthquakes, resulting in a more reliable statistical data set. In this study, we analyzed six earthquake sequences with magnitudes of M_S ≥ 6.0 that occurred in northeastern Tibet since 2009, following the upgrade of the regional seismic network. Using BEPIT, we supplemented short-term missing aftershocks in these sequences, creating a more complete earthquake catalog. ETAS model parameters and b values for these sequences were then estimated using maximum likelihood methods to analyze parameter variability across sequences. The findings indicate that the b value is low, reflecting relatively high regional stress. The background seismicity rate is very low, with most mainshocks in these sequences being background events rather than foreshock-driven events. The p-parameter of the ETAS model is high, indicating that aftershocks decay relatively quickly, while the α-parameter is also elevated, suggesting that aftershocks are predominantly induced by the mainshock. These conditions suggest that earthquake prediction in this region is challenging through seismicity analysis alone, and alternative approaches integrating non-seismic data, such as electromagnetic and fluid monitoring, may offer more viable solutions. This study provides valuable insights into earthquake forecasting in the northeastern Tibetan Plateau. Full article

We propose the HOSA algorithm to pick P-wave arrival times on seismic arrays. HOSA comprises two stages: a single-trace stage (STS) and a multi-channel stage (MCS). STS seeks deviations in higher-order statistics from background noise to identify sets of potential onsets on each trace. STS employs various thresholds and identifies an onset only for solutions that are gently variable with the threshold. Uncertainty is assigned to onsets based on their variation with the threshold. MCS verifies that detected onsets are consistent with the array geometry. It groups onsets within an array by hierarchical agglomerative clustering and selects only groups whose maximum differential times are consistent with the P-wave travel time across the array. HOSA needs a set of P-onsets to be calibrated. These sets may be already available (e.g., preliminary catalogs) or retrieved from picking (manually/automatically) a subset of traces in the target area. We tested HOSA on 226 microearthquakes recorded by 20 temporary arrays of 10 stations each, deployed in the Irpinia region (Southern Italy), which, in 1980, experienced a devastating 6.9 Ms earthquake. HOSA parameters were calibrated using a preliminary catalog of onsets obtained using an automatic template-matching approach. HOSA solutions are more reliable, less prone to false detection, and show higher inter-array consistency than template-matching solutions. Full article

Macroseismic intensity data are the only source of information for historical earthquakes; it is therefore necessary to devise methods that allow us to retrieve as many source parameters as possible on the basis of these data. We present the inversion of macroseismic data as a first validation of an improved version of the kinematic function, KF. Following the previous results of some earthquakes on Italian territory and several validations by Californian events provided with instrumental solutions, we have now simplified the KF by reducing some degrees of freedom of the parameters and rearranging the code for parallel calculation. This approach will allow for a more extensive application of the KF technique. We present the inversion of the macroseismic intensity pattern of the M_wp6 earthquake of 27 March 1928 (8:32 GMT), which occurred in Northeastern Italy (Carnia), and we retrieved source parameters that are compatible with the solutions of other authors who independently treat instrumental data. The 1928 event is located a few tens of kilometers west of the more destructive M_w6.5 of 6 May 1976 and northeast of the subsequent earthquake M_wp6.1 of 18 October 1936. The inversion was performed as a blind test, without prior knowledge for fault plane solutions and tectonic information; it resulted in a minimum variance model with a strike of 62°, a dip of 10°, and a rake of 101°. This solution is not consistent with the entire tectonic framework of the eastern Southalpine chain, but it is in agreement with the But-Chiarsò line. This result encourages us to test further improvements to the KF method and to treat other cases from the Italian macroseismic catalog. Full article

On 5 September 2022, the moment magnitude (Mw) 6.7 Luding earthquake struck in the Xianshuihe Fault system on the eastern edge of the Tibet Plateau, illuminating the seismic gap in the Moxi segment. The fault system geometry and rupture process of this earthquake are relatively complex. To better understand the underlying driving mechanisms, this study first uses the Interferometric Synthetic Aperture Radar (InSAR) technique to obtain static surface displacements, which are then combined with Global Positioning System (GPS) data to invert the coseismic slip distribution. A machine learning approach is applied to extract a high-quality aftershock catalog from the original seismic waveform data, enabling the analysis of the spatiotemporal characteristics of aftershock activity. The catalog is subsequently used for fault fitting to determine a reliable fault geometry. The coseismic slip is dominated by left-lateral strike-slip motion, distributed within a depth range of 0–15 km, with a maximum fault slip > 2 m. The relocated catalog contains 15,571 events. Aftershock activity is divided into four main seismic clusters, with two smaller clusters located to the north and south and four interval zones in between. The geometry of the five faults is fitted, revealing the complexity of the Xianshuihe Fault system. Additionally, the Luding earthquake did not fully rupture the Moxi segment. The unruptured areas to the north of the mainshock, as well as regions to the south near the Anninghe Fault, pose a potential seismic hazard. Full article

Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. Taking the southern part of China’s North–South Seismic Belt (20° N~30° N, 96° E~106° E), where strong earthquakes frequently occur, as an example, we used the sliding time window method to calculate 11 seismicity indicators from the earthquake catalog data as the characteristic parameters of the training model, and compared six machine learning models, including the random forest (RF) and long short-term memory (LSTM) models, to select the best-performing LSTM model for predicting the maximum magnitude of an earthquake in the study area in the coming year. The experimental results show that the LSTM model performs exceptionally well in predicting earthquakes of magnitude 5 < ML ≤ 6 within the time window of the test set, with a prediction success rate of 85%. Additionally, the study explores how different time windows, spatial locations, and parameter choices affect model performance. It found that longer time windows and key seismicity parameters, such as the b-value and the square root of total seismic energy, are crucial for improving prediction accuracy. Finally, we propose a magnitude interval-based assessment method to better predict the actual impacts that different magnitudes may cause. This method demonstrates the LSTM model’s potential in predicting moderate to strong earthquakes and offers new approaches for earthquake early warning and disaster mitigation. Full article

The detection and monitoring of mining-induced seismicity are essential for understanding the mechanisms behind earthquakes and mitigating seismic hazards. However, traditional underground seismic monitoring networks for mining-induced seismicity are challenging to install and operate, which has limited their widespread application. In recent years, an alternative approach has emerged: utilizing dense seismic arrays at the surface to monitor mining-induced seismicity. This paper proposes a rapid and efficient data processing scheme for the detection and monitoring of mining-induced seismicity based on the surface dense array. The proposed workflow includes machine learning-based phase picking and P-wave first-motion-polarity picking, followed by rapid phase association, precise earthquake location, and template matching for detecting small earthquakes to enhance the completeness of the earthquake catalog. Additionally, it also provides focal mechanism solutions for larger mining-induced events. We applied this workflow to the continuous waveform data from 90 seismic stations over a period of 27 days around the Dongchuan Copper Mine, Yunnan Province, China. Our results yielded 1536 high-quality earthquake locations and two focal mechanism solutions for larger events. By analyzing the spatiotemporal distribution of these events, we are able to investigate the mechanisms of the induced seismic clusters near the Shijiangjun and Lanniping deposits. Our findings highlight the excellent monitoring capability and application potential of the workflow based on machine learning and template matching compared with conventional techniques. Full article

We present an updated and validated seismic catalog for the northern Fennoscandian region, focusing on postglacial faults from the Merasjärvi fault system in the southwest to the Iešjávri fault system in the northeast. This work involved a comprehensive review of continuous waveforms derived from open datasets from 2007 to 2015 and processed using the Regressive ESTimator algorithm. The primary objective was to refine the delineation of seismicity along the above-mentioned postglacial faults and highlight their seismic potential. Our analysis revealed distinct waveform patterns originating primarily from two main sources: approximately 15% were associated with areas mapped as postglacial faults, and the remainder of the events outside these areas, 89%, were concentrated in areas with active mines. Compared to previously reported events in the Fennoscandian Earthquake Catalogue (FENCAT), we observed a 22% increase in seismic activity within postglacial fault zones. These results demonstrate that the Regressive ESTimator algorithm not only improves the detection of tectonic seismicity but also effectively identifies seismic signals resulting from mining activities in the study area. The Merasjärvi, Lainio–Suijavaara, Palojärvi, and Maze and Iešjávri fault systems appear to form a continuous deformation complex of approximately 300 km long, which we propose naming the Merasjärvi–Stuoragurra fault complex. Full article