Earthquake Science最新文献

{"title":"Generalization of PhaseNet in Shandong and its application to the Changqing M4.1 earthquake sequence","authors":"Zonghui Dai ,&nbsp;Lianqing Zhou ,&nbsp;Xuhui Hu ,&nbsp;Junhao Qu ,&nbsp;Xia Li","doi":"10.1016/j.eqs.2023.04.003","DOIUrl":"10.1016/j.eqs.2023.04.003","url":null,"abstract":"<div><p>Waveforms of seismic events, extracted from January 2019 to December 2021 were used to construct a test dataset to investigate the generalizability of PhaseNet in the Shandong region. The results show that errors in the picking of seismic phases (P- and S-waves) had a broadly normal distribution, mainly concentrated in the ranges of −0.4–0.3 s and −0.4–0.8 s, respectively. These results were compared with those published in the original PhaseNet article and were found to be approximately 0.2–0.4 s larger. PhaseNet had a strong generalizability for P- and S-wave picking for epicentral distances of less than 120 km and 110 km, respectively. However, the phase recall rate decreased rapidly when these distances were exceeded. Furthermore, the generalizability of PhaseNet was essentially unaffected by magnitude. The <em>M</em>4.1 earthquake sequence in Changqing, Shandong province, China, that occurred on February 18, 2020, was adopted as a case study. PhaseNet detected more than twice the number of earthquakes in the manually obtained catalog. This further verified that PhaseNet has strong generalizability in the Shandong region, and a high-precision earthquake catalog was constructed. According to these precise positioning results, two earthquake sequences occurred in the study area, and the southern cluster may have been triggered by the northern cluster. The focal mechanism solution, regional stress field, and the location results of the northern earthquake sequence indicated that the seismic force of the earthquake was consistent with the regional stress field.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 3","pages":"Pages 212-227"},"PeriodicalIF":1.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43259084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of the wave gradiometry method for seismic imaging","authors":"Chuntao Liang ,&nbsp;Feihuang Cao ,&nbsp;Zhijin Liu ,&nbsp;Yingna Chang","doi":"10.1016/j.eqs.2023.04.002","DOIUrl":"10.1016/j.eqs.2023.04.002","url":null,"abstract":"<div><p>As dense seismic arrays at different scales are deployed, the techniques to make full use of array data with low computing cost become increasingly needed. The wave gradiometry method (WGM) is a new branch in seismic tomography, which utilizes the spatial gradients of the wavefield to determine the phase velocity, wave propagation direction, geometrical spreading, and radiation pattern. Seismic wave propagation parameters obtained using the WGM can be further applied to invert 3D velocity models, <em>Q</em> values, and anisotropy at lithospheric (crust and/or mantle) and smaller scales (e.g., industrial oilfield or fault zone). Herein, we review the theoretical foundation, technical development, and major applications of the WGM, and compared the WGM with other commonly used major array imaging methods. Future development of the WGM is also discussed.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 3","pages":"Pages 254-281"},"PeriodicalIF":1.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46614335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"USTC-Pickers: a Unified Set of seismic phase pickers Transfer learned for China","authors":"Jun Zhu ,&nbsp;Zefeng Li ,&nbsp;Lihua Fang","doi":"10.1016/j.eqs.2023.03.001","DOIUrl":"10.1016/j.eqs.2023.03.001","url":null,"abstract":"<div><p>Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China. To mitigate this problem, we build a unified set of customized seismic phase pickers for different levels of use in China. We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet. This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China. Then, using different subsets of the DiTing data, we fine-tune the base picker to better adapt to different regions. In total, we provide 5 pickers for major tectonic blocks in China, 33 pickers for provincial-level administrative regions, and 2 special pickers for the Capital area and the China Seismic Experimental Site. These pickers show improved performance in respective regions which they are customized for. They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets. We anticipate that this picker set will facilitate earthquake monitoring in China.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 2","pages":"Pages 95-112"},"PeriodicalIF":1.2,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45799466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network","authors":"Ziye Yu ,&nbsp;Weitao Wang ,&nbsp;Yini Chen","doi":"10.1016/j.eqs.2022.10.001","DOIUrl":"10.1016/j.eqs.2022.10.001","url":null,"abstract":"<div><p>Seismic phase pickers based on deep neural networks have been extensively used recently, demonstrating their advantages on both performance and efficiency. However, these pickers are trained with and applied to different data. A comprehensive benchmark based on a single dataset is therefore lacking. Here, using the recently released DiTing dataset, we analyzed performances of seven phase pickers with different network structures, the efficiencies are also evaluated using both CPU and GPU devices. Evaluations based on <em>F</em>₁-scores reveal that the recurrent neural network (RNN) and EQTransformer exhibit the best performance, likely owing to their large receptive fields. Similar performances are observed among PhaseNet (UNet), UNet++, and the lightweight phase picking network (LPPN). However, the LPPN models are the most efficient. The RNN and EQTransformer have similar speeds, which are slower than those of the LPPN and PhaseNet. UNet++ requires the most computational effort among the pickers. As all of the pickers perform well after being trained with a large-scale dataset, users may choose the one suitable for their applications. For beginners, we provide a tutorial on training and validating the pickers using the DiTing dataset. We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users. All of our models are open-source and publicly accessible.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 2","pages":"Pages 113-131"},"PeriodicalIF":1.2,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45683422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface to the special issue of Artificial Intelligence in Seismology","authors":"Lihua Fang ,&nbsp;Zefeng Li","doi":"10.1016/j.eqs.2023.03.003","DOIUrl":"10.1016/j.eqs.2023.03.003","url":null,"abstract":"","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 2","pages":"Pages 81-83"},"PeriodicalIF":1.2,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47190671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology","authors":"Ming Zhao ,&nbsp;Zhuowei Xiao ,&nbsp;Shi Chen ,&nbsp;Lihua Fang","doi":"10.1016/j.eqs.2022.01.022","DOIUrl":"10.1016/j.eqs.2022.01.022","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 2","pages":"Pages 84-94"},"PeriodicalIF":1.2,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49410916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based automatic construction of earthquake catalog for reservoir areas in multiple river basins of Guizhou province, China","authors":"Longfei Duan ,&nbsp;Cuiping Zhao ,&nbsp;Xingzhong Du ,&nbsp;Lianqing Zhou","doi":"10.1016/j.eqs.2023.03.002","DOIUrl":"10.1016/j.eqs.2023.03.002","url":null,"abstract":"<div><p>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 <em>b</em>-value of 1.25 was calculated for seismicity in western Guizhou province, consistent with the <em>b</em>-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.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 2","pages":"Pages 132-146"},"PeriodicalIF":1.2,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42909813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep-learning-based approach for seismic surface-wave dispersion inversion (SfNet) with application to the Chinese mainland","authors":"Feiyi Wang ,&nbsp;Xiaodong Song ,&nbsp;Mengkui Li","doi":"10.1016/j.eqs.2023.02.007","DOIUrl":"10.1016/j.eqs.2023.02.007","url":null,"abstract":"<div><p>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 <em>v</em>_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 <em>v</em>_S models instead of directly using the existing <em>v</em>_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 <em>v</em>_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 <em>v</em>_S model inversions from large amounts of surface-wave dispersion data.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 2","pages":"Pages 147-168"},"PeriodicalIF":1.2,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45073552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moment magnitudes of two large Turkish earthquakes on February 6, 2023 from long-period coda","authors":"Xinyu Jiang ,&nbsp;Xiaodong Song ,&nbsp;Tian Li ,&nbsp;Kaixin Wu","doi":"10.1016/j.eqs.2023.02.008","DOIUrl":"https://doi.org/10.1016/j.eqs.2023.02.008","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 2","pages":"Pages 169-174"},"PeriodicalIF":1.2,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49704086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"P-wave velocity structure beneath reservoirs and surrounding areas in the lower Jinsha River","authors":"Changzai Wang,&nbsp;Jianping Wu,&nbsp;Lihua Fang,&nbsp;Yaning Liu,&nbsp;Jing Liu,&nbsp;Yan Cai,&nbsp;Poren Li","doi":"10.1016/j.eqs.2023.02.003","DOIUrl":"10.1016/j.eqs.2023.02.003","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 1","pages":"Pages 64-75"},"PeriodicalIF":1.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44483793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}