Earthquake Science最新文献

{"title":"A review of the influencing factors on teleseismic traveltime tomography","authors":"Yang Pan ,&nbsp;Shaolin Liu ,&nbsp;Dinghui Yang ,&nbsp;Wenshuai Wang ,&nbsp;Xiwei Xu ,&nbsp;Wenhao Shen ,&nbsp;Mengyang Li","doi":"10.1016/j.eqs.2022.12.006","DOIUrl":"10.1016/j.eqs.2022.12.006","url":null,"abstract":"<div><p>Teleseismic traveltime tomography is an important tool for investigating the crust and mantle structure of the Earth. The imaging quality of teleseismic traveltime tomography is affected by many factors, such as mantle heterogeneities, source uncertainties and random noise. Many previous studies have investigated these factors separately. An integral study of these factors is absent. To provide some guidelines for teleseismic traveltime tomography, we discussed four main influencing factors: the method for measuring relative traveltime differences, the presence of mantle heterogeneities outside the imaging domain, station spacing and uncertainties in teleseismic event hypocenters. Four conclusions can be drawn based on our analysis. (1) Comparing two methods, i.e., measuring the traveltime difference between two adjacent stations (M1) and subtracting the average traveltime of all stations from the traveltime of one station (M2), reveals that both M1 and M2 can well image the main structures; while M1 is able to achieve a slightly higher resolution than M2; M2 has the advantage of imaging long wavelength structures. In practical teleseismic traveltime tomography, better tomography results can be achieved by a two-step inversion method. (2) Global mantle heterogeneities can cause large traveltime residuals (up to about 0.55 s), which leads to evident imaging artifacts. (3) The tomographic accuracy and resolution of M1 decrease with increasing station spacing when measuring the relative traveltime difference between two adjacent stations. (4) The traveltime anomalies caused by the source uncertainties are generally less than 0.2 s, and the impact of source uncertainties is negligible.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 3","pages":"Pages 228-253"},"PeriodicalIF":1.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44189294","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":"Broadband ground motion simulation using a hybrid approach of the May 21, 2021 M7.4 earthquake in Maduo, Qinghai, China","authors":"Yijun Liu ,&nbsp;Xiaofen Zhao ,&nbsp;Zengping Wen ,&nbsp;Jie Liu ,&nbsp;Bo Chen ,&nbsp;Chunyao Bu ,&nbsp;Chao Xu","doi":"10.1016/j.eqs.2023.04.001","DOIUrl":"10.1016/j.eqs.2023.04.001","url":null,"abstract":"<div><p>In this study, the broadband ground motions of the 2021 <em>M</em>7.4 Maduo earthquake were simulated to overcome the scarcity of ground motion recordings and the low resolution of macroseismic intensity map in sparsely populated high-altitude regions. The simulation was conducted with a hybrid methodology, combining a stochastic high-frequency simulation with a low-frequency ground motion simulation, from the regional 1-D velocity structure model and the Wang WM et al. (2022) source rupture model, respectively. We found that the three-component waveforms simulated for specific stations matched the waveforms recorded at those stations, in terms of amplitude, duration, and frequency content. The validation results demonstrate the ability of the hybrid simulation method to reproduce the main characteristics of the observed ground motions for the 2021 Maduo earthquake over a broad frequency range. Our simulations suggest that the official map of macroseismic intensity tends to overestimate shaking by one intensity unit. Comparisons of simulations with empirical ground motion models indicate generally good consistency between the simulated and empirically predicted intensity measures. The high-frequency components of ground motions were found to be more prominent, while the low-frequency components were not, which is unexpected for large earthquakes. Our simulations provide valuable insight into the effects of source complexity on the level and variability of the resulting ground motions. The acceleration and velocity time histories and corresponding response spectra were provided for selected representative sites where no records were available. The simulated results have important implications for evaluating the performance of engineering structures in the epicentral regions of this earthquake and for estimating seismic hazards in the Tibetan regions where no strong ground motion records are available for large earthquakes.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 3","pages":"Pages 175-199"},"PeriodicalIF":1.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42689959","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":"Quality influencing factors of dispersion curves from short period dense arrays based on a convolutional neural network across the north section of the Xiaojiang fault area","authors":"Si Chen ,&nbsp;Rui Gao ,&nbsp;Zhanwu Lu ,&nbsp;Yao Liang ,&nbsp;Wei Cai ,&nbsp;Lifu Cao ,&nbsp;Zilong Chen ,&nbsp;Guangwen Wang","doi":"10.1016/j.eqs.2023.04.004","DOIUrl":"10.1016/j.eqs.2023.04.004","url":null,"abstract":"<div><p>The number of dispersion curves increases significantly when the scale of a short-period dense array increases. Owing to a substantial increase in data volume, it is important to quickly evaluate dispersion curve quality as well as select the available dispersion curve. Accordingly, this study quantitatively evaluated dispersion curve quality by training a convolutional neural network model for ambient noise tomography using a short-period dense array. The model can select high-quality dispersion curves that exhibit a ≤ 10% difference between the results of manual screening and the proposed model. In addition, this study established a dispersion curve loss function by analyzing the quality of the dispersion curve and the corresponding influencing factors, thereby estimating the number of available dispersion curves for the existing observation systems. Furthermore, a Monte Carlo simulation experiment is used to illustrates the station-pair interval distance probability density function, which is independent of station number in the observational system with randomly deployed stations. The results suggested that the straight-line length should exceed 15 km to ensure that loss rate of dispersion curves remains &lt; 0.5, while maintaining the threshold ambient noise tomography accuracy within the study area.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"36 3","pages":"Pages 200-211"},"PeriodicalIF":1.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45172573","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":"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}