{"title":"NA- gcap:一种基于改进GRTM和NA方法的全矩张量反演方法","authors":"J. Wen, F. Hu, Xiaofei Chen","doi":"10.1785/0220220259","DOIUrl":null,"url":null,"abstract":"\n Moment tensor inversion plays a crucial role in determining earthquake types, magnitude, and source geometry. Compared to polarity and amplitude methods, full-waveform approaches provide more comprehensive constraints on the complex full moment tensor (FMT). In this study, we propose a novel FMT inversion method named neighborhood algorithm-generalized cut-and-paste (NA-GCAP) method. Similar to the “cut-and-paste” method, our approach divides seismograms into Pnl and surface-wave segments. To enhance inversion efficiency, we employ the NA in conjunction with a newly developed strategy for efficient Green’s functions computation based on the renewed fast generalized reflection and transmission method (GRTM). Our method requires only a single forward computation and stores ten Green’s functions, significantly improving computational efficiency. We validate the robustness of our approach through synthetic tests using a velocity model perturbed by 5% relative to the input model. Furthermore, we apply the NA-GCAP method to the 2019 Changning earthquake sequence comprising 16 earthquakes, where twelve events exhibit double-couple (DC) components larger than 0.95, indicating a simple dislocation source, and four events display significant non-DC components. Our results align well with the previous studies and demonstrate the potential for widespread application to other earthquake sequences in the future.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NA-GCAP: A New Full Moment Tensor Inversion Method Based on the Renewed GRTM and NA Method\",\"authors\":\"J. Wen, F. Hu, Xiaofei Chen\",\"doi\":\"10.1785/0220220259\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Moment tensor inversion plays a crucial role in determining earthquake types, magnitude, and source geometry. Compared to polarity and amplitude methods, full-waveform approaches provide more comprehensive constraints on the complex full moment tensor (FMT). In this study, we propose a novel FMT inversion method named neighborhood algorithm-generalized cut-and-paste (NA-GCAP) method. Similar to the “cut-and-paste” method, our approach divides seismograms into Pnl and surface-wave segments. To enhance inversion efficiency, we employ the NA in conjunction with a newly developed strategy for efficient Green’s functions computation based on the renewed fast generalized reflection and transmission method (GRTM). Our method requires only a single forward computation and stores ten Green’s functions, significantly improving computational efficiency. We validate the robustness of our approach through synthetic tests using a velocity model perturbed by 5% relative to the input model. Furthermore, we apply the NA-GCAP method to the 2019 Changning earthquake sequence comprising 16 earthquakes, where twelve events exhibit double-couple (DC) components larger than 0.95, indicating a simple dislocation source, and four events display significant non-DC components. Our results align well with the previous studies and demonstrate the potential for widespread application to other earthquake sequences in the future.\",\"PeriodicalId\":21687,\"journal\":{\"name\":\"Seismological Research Letters\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seismological Research Letters\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1785/0220220259\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seismological Research Letters","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1785/0220220259","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
NA-GCAP: A New Full Moment Tensor Inversion Method Based on the Renewed GRTM and NA Method
Moment tensor inversion plays a crucial role in determining earthquake types, magnitude, and source geometry. Compared to polarity and amplitude methods, full-waveform approaches provide more comprehensive constraints on the complex full moment tensor (FMT). In this study, we propose a novel FMT inversion method named neighborhood algorithm-generalized cut-and-paste (NA-GCAP) method. Similar to the “cut-and-paste” method, our approach divides seismograms into Pnl and surface-wave segments. To enhance inversion efficiency, we employ the NA in conjunction with a newly developed strategy for efficient Green’s functions computation based on the renewed fast generalized reflection and transmission method (GRTM). Our method requires only a single forward computation and stores ten Green’s functions, significantly improving computational efficiency. We validate the robustness of our approach through synthetic tests using a velocity model perturbed by 5% relative to the input model. Furthermore, we apply the NA-GCAP method to the 2019 Changning earthquake sequence comprising 16 earthquakes, where twelve events exhibit double-couple (DC) components larger than 0.95, indicating a simple dislocation source, and four events display significant non-DC components. Our results align well with the previous studies and demonstrate the potential for widespread application to other earthquake sequences in the future.