Earthquake Science最新文献

In recent years, artificial intelligence technology has exhibited great potential in seismic signal recognition, setting off a new wave of research. Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research. In this study, based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center, we constructed an artificial intelligence seismological training dataset (“DiTing”) with the largest known total time length. Data were recorded using broadband and short-period seismometers. The obtained dataset included 2,734,748 three-component waveform traces from 787,010 regional seismic events, the corresponding P- and S-phase arrival time labels, and 641,025 P-wave first-motion polarity labels. All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake. Each three-component waveform contained a considerable amount of descriptive information, such as the epicentral distance, back azimuth, and signal-to-noise ratios. The magnitudes of seismic events, epicentral distance, signal-to-noise ratio of P-wave data, and signal-to-noise ratio of S-wave data ranged from 0 to 7.7, 0 to 330 km, –0.05 to 5.31 dB, and –0.05 to 4.73 dB, respectively. The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection, seismic phase picking, first-motion polarity determination, earthquake magnitude prediction, early warning systems, and strong ground-motion prediction. Such research will further promote the development and application of artificial intelligence in seismology.

Large reservoirs have the risk of reservoir induced seismicity. Accurately detecting and locating microseismic events are crucial when studying reservoir earthquakes. Automatic earthquake monitoring in reservoir areas is one of the effective measures for earthquake disaster prevention and mitigation. In this study, we first applied the automatic location workflow (named LOC-FLOW) to process 14-day continuous waveform data from several reservoir areas in different river basins of Guizhou province. Compared with the manual seismic catalog, the recall rate of seismic event detection using the workflow was 83.9%. Of the detected earthquakes, 88.9% had an onset time difference below 1 s, 81.8% has a deviation in epicenter location within 5 km, and 77.8% had a focal depth difference of less than 5 km, indicating that the workflow has good generalization capacity in reservoir areas. We further applied the workflow to retrospectively process continuous waveform data recorded from 2020 to the first half of 2021 in reservoir areas in multiple river basins of western Guizhou province and identified five times the number of seismic events obtained through manual processing. Compared with manual processing of seismic catalog, the completeness magnitude had decreased from 1.3 to 0.8, and a b-value of 1.25 was calculated for seismicity in western Guizhou province, consistent with the b-values obtained for the reservoir area in previous studies. Our results show that seismicity levels were relatively low around large reservoirs that were impounded over 15 years ago, and there is no significant correlation between the seismicity in these areas and reservoir impoundment. Seismicity patterns were notably different around two large reservoirs that were only impounded about 12 years ago, which may be explained by differences in reservoir storage capacity, the geologic and tectonic settings, hydrogeological characteristics, and active fault the reservoir areas. Prominent seismicity persisted around two large reservoirs that have been impounded for less than 10 years. These events were clustered and had relatively shallow focal depths. The impoundment of the Jiayan Reservoir had not officially begun during this study period, but earthquake location results suggested a high seismicity level in this reservoir area. Therefore, any seismicity in this reservoir area after the official impoundment deserves special attention.

Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth. In this study, we proposed a deep learning (DL) method based on convolutional neural network (CNN), named SfNet, to derive the v_S model from the Rayleigh wave phase and group velocity dispersion curves. Training a network model usually requires large amount of training datasets, which is labor-intensive and expensive to acquire. Here we relied on synthetics generated automatically from various spline-based v_S models instead of directly using the existing v_S models of an area to build the training dataset, which enhances the generalization of the DL method. In addition, we used a random sampling strategy of the dispersion periods in the training dataset, which alleviates the problem that the real data used must be sampled strictly according to the periods of training dataset. Tests using synthetic data demonstrate that the proposed method is much faster, and the results for the v_S model are more accurate and robust than those of conventional methods. We applied our method to a dataset for the Chinese mainland and obtained a new reference velocity model of the Chinese continent (ChinaVs-DL1.0), which has smaller dispersion misfits than those from the traditional method. The high accuracy and efficiency of our DL approach makes it an important method for v_S model inversions from large amounts of surface-wave dispersion data.

Two large earthquakes (an earthquake doublet) occurred in south-central Turkey on February 6, 2023, causing massive damages and casualties. The magnitudes and the relative sizes of the two mainshocks are essential information for scientific research and public awareness. There are obvious discrepancies among the results that have been reported so far, which may be revised and updated later. Here we applied a novel and reliable long-period coda moment magnitude method to the two large earthquakes. The moment magnitudes (with one standard error) are 7.95±0.013 and 7.86±0.012, respectively, which are larger than all the previous reports. The first mainshock, which matches the largest recorded earthquakes in the Turkish history, is slightly larger than the second one by 0.11±0.035 in magnitude or by 0.04 to 0.18 at 95% confidence level.

The lower reaches of the Jinsha River are rich in hydropower resources because of the high mountains, deep valleys, and swift currents in this area. This region also features complex tectonic structures and frequent earthquakes. After the impoundment of the reservoirs, seismic activity increased significantly. Therefore, it is necessary to study the P-wave velocity structure and earthquake locations in the lower reaches of the Jinsha River and surrounds, thus providing seismological support for subsequent earthquake prevention and disaster reduction work in reservoir areas. In this study, we selected the data of 7,670 seismic events recorded by the seismic networks in Sichuan, Yunnan, and Chongqing and the temporary seismic arrays deployed nearby. We then applied the double-difference tomography method to this data, to obtain the P-wave velocity structure and earthquake locations in the lower reaches of the Jinsha River and surrounds. The results showed that the Jinsha River basin has a complex lateral P-wave velocity structure. Seismic events are mainly distributed in the transition zones between high- and low-velocity anomalies, and seismic events are particularly intense in the Xiluodu and Baihetan reservoir areas. Vertical cross-sections through the Xiangjiaba and Xiluodu reservoir areas revealed an apparent high-velocity anomaly at approximately 6 km depth; this high-velocity anomaly plays a role in stress accumulation, with few earthquakes distributed inside the high-velocity body. After the impoundment of the Baihetan reservoir, the number of earthquakes in the reservoir area increased significantly. The seismic events in the reservoir area north of 27° N were related to the enhanced activity of nearby faults after impoundment; the earthquakes in the reservoir area south of 27° N were probably induced by additional loads (or regional stress changes), and the multiple microseismic events may have been caused by rock rupture near the main faults under high pore pressure.

Earthquakes result from continuous geodynamic processes. A topic of significant interest for the scientific community is to elaborate on the phenomena governing the faulting and fracturing of crustal rocks. Therefore, in this study, uniaxial compressive shear failure experiments were conducted on Fangshan marble rock samples with a prefabricated slot to simulate thrust faulting. The center of each marble plate (105 mm × 80 mm × 5 mm) was engraved with a 30-mm long double-sided nonpenetrating slot (depth: 2 mm, width: 0.5 mm). The deformation and destruction processes of the rock surface were recorded using a high-speed camera. The digital image correlation method was used to calculate the displacement and strain distribution and variation at different loading stages. The accumulative and incremental displacement fields u and v, strain field e_x and e_y, and shear strain e_xy were analyzed. When the loading level reached its ultimate value, the strain field was concentrated around the prefabricated slot. The concentration reached a maximum at the ends of the prefabricated slot. The magnitude of shear strain reached 0.1. This experiment contributes to our understanding of the dynamic process of active faulting.

The Shimian area of Sichuan sits at the junction of the Bayan Har block, Sichuan-Yunnan rhombic block, and Yangtze block, where several faults intersect. This region features intense tectonic activity and frequent earthquakes. In this study, we used local seismic waveform data recorded using dense arrays deployed in the Shimian area to obtain the shear wave splitting parameters at 55 seismic stations and thereby determine the crustal anisotropic characteristics of the region. We then analyzed the crustal stress pattern and tectonic setting and explored their relationship in the study area. Although some stations returned a polarization direction of NNW-SSE, a dominant polarization direction of NW-SE was obtained for the fast shear wave at most seismic stations in the study area. The polarization directions of the fast shear wave were highly consistent throughout the study area. This orientation was in accordance with the direction of the regional principal compressive stress and parallel to the trend of the Xianshuihe and Daliangshan faults. The distribution of crustal anisotropy in this area was affected by the regional tectonic stress field and the fault structures. The mean delay time between fast and slow shear waves was 3.83 ms/km, slightly greater than the values obtained in other regions of Sichuan. This indicates that the crustal media in our study area had a high anisotropic strength and also reveals the influence of tectonic complexity resulting from the intersection of multiple faults on the strength of seismic anisotropy.

Whether the September 5, 2022, Luding M_S6.8 earthquake is an ‘expected’ event in the context of earthquake forecast? This commentary discusses this issue mainly using the recently proposed ‘earthquake nowcasting’ approach.

The margin of the Tibetan Plateau of Southwest China is one of the most seismically active regions of China and is the location of the China Seismic Experimental Site (CSES). Many studies have developed seismic velocity models of Southwest China, but few have compared and evaluated these models which is important for further model improvement. Thus, we compared six published seismic shear-wave velocity models of Southwest China on absolute velocity and velocity perturbation patterns. The models are derived from different types of data (e.g., surface waves from ambient noise and earthquakes, body-wave travel times, receiver functions) and inversion methods. We interpolated the models into a uniform horizontal grid (0.5° × 0.5°) and vertically sampled them at 5, 10, 20, 30, 40, and 60 km depths. We found significant differences between the six models. Then, we selected three of them that showed greater consistency for further comparison. Our further comparisons revealed systematic biases between models in absolute velocity that may be related to different data types. The perturbation pattern of the model is especially divergent in the shallow part, but more consistent in the deep part. We conducted synthetic and inversion tests to explore possible causes and our results imply that systematic differences between the data, differences in methods, and other factors may directly affect the model. Therefore, the Southwest China velocity model still has considerable room for improvement, and the impact of inconsistency between different data types on the model needs further research. Finally, we proposed a new reference shear-wave velocity model of Southwest China (SwCM-S1.0) based on the three selected models with high consistency. We believe that this model is a better representation of more robust features of the models that are based on different data sets.