Zhiyi Zeng;Peng Han;Wei Zhang;Yong Zhou;Jianzhong Zhang;Ying Chang;Da Zhang;Rui Dai;Hu Ji
{"title":"微震监测中的自适应概率成像定位方法","authors":"Zhiyi Zeng;Peng Han;Wei Zhang;Yong Zhou;Jianzhong Zhang;Ying Chang;Da Zhang;Rui Dai;Hu Ji","doi":"10.1109/TGRS.2025.3529931","DOIUrl":null,"url":null,"abstract":"Microseismic event locations provide critical insights into fracture locations and stress conditions within rock formations, which are essential for seismic hazard monitoring. In practice, pick-based location methods are widely utilized due to their high computational efficiency. However, the accuracy of picking is compromised when the signal-to-noise ratio (SNR) is low. Source location utilizing equal differential time (EDT) surfaces between station pairs represents an effective strategy for mitigating picking errors. Typically, EDT surfaces are constructed using either a fixed width or a fixed probability density function (pdf), which presents challenges in simultaneously achieving high resolution and accuracy. To address this issue, we propose an adaptive probabilistic imaging location method that constructs EDT surfaces by incorporating an adaptive pdf linked to the SNR of arrival picking data. The location probability imaging function is defined as the product of the independent EDT surfaces used for locating sources. For high-SNR data, where picking errors are typically small, the adaptive pdf converges more rapidly than the Gaussian distribution, yielding higher resolution by assigning lower probabilities to locations distant from true positions. For low-SNR data, where picking errors are typically large, the adaptive pdf exhibits heavy-tailed characteristics with a slower rate of decrease, enhancing the accuracy by assigning higher probabilities to likely true locations. The effectiveness and stability of the method are validated through theoretical analyses and synthetic data tests. Application to mine microseismic data indicates that the proposed method improves the accuracy and resolution of microseismic event locations relative to other methods.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-16"},"PeriodicalIF":8.6000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Adaptive Probabilistic Imaging Location Method for Microseismic Monitoring\",\"authors\":\"Zhiyi Zeng;Peng Han;Wei Zhang;Yong Zhou;Jianzhong Zhang;Ying Chang;Da Zhang;Rui Dai;Hu Ji\",\"doi\":\"10.1109/TGRS.2025.3529931\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Microseismic event locations provide critical insights into fracture locations and stress conditions within rock formations, which are essential for seismic hazard monitoring. In practice, pick-based location methods are widely utilized due to their high computational efficiency. However, the accuracy of picking is compromised when the signal-to-noise ratio (SNR) is low. Source location utilizing equal differential time (EDT) surfaces between station pairs represents an effective strategy for mitigating picking errors. Typically, EDT surfaces are constructed using either a fixed width or a fixed probability density function (pdf), which presents challenges in simultaneously achieving high resolution and accuracy. To address this issue, we propose an adaptive probabilistic imaging location method that constructs EDT surfaces by incorporating an adaptive pdf linked to the SNR of arrival picking data. The location probability imaging function is defined as the product of the independent EDT surfaces used for locating sources. For high-SNR data, where picking errors are typically small, the adaptive pdf converges more rapidly than the Gaussian distribution, yielding higher resolution by assigning lower probabilities to locations distant from true positions. For low-SNR data, where picking errors are typically large, the adaptive pdf exhibits heavy-tailed characteristics with a slower rate of decrease, enhancing the accuracy by assigning higher probabilities to likely true locations. The effectiveness and stability of the method are validated through theoretical analyses and synthetic data tests. Application to mine microseismic data indicates that the proposed method improves the accuracy and resolution of microseismic event locations relative to other methods.\",\"PeriodicalId\":13213,\"journal\":{\"name\":\"IEEE Transactions on Geoscience and Remote Sensing\",\"volume\":\"63 \",\"pages\":\"1-16\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2025-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Geoscience and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10843286/\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Geoscience and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10843286/","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
An Adaptive Probabilistic Imaging Location Method for Microseismic Monitoring
Microseismic event locations provide critical insights into fracture locations and stress conditions within rock formations, which are essential for seismic hazard monitoring. In practice, pick-based location methods are widely utilized due to their high computational efficiency. However, the accuracy of picking is compromised when the signal-to-noise ratio (SNR) is low. Source location utilizing equal differential time (EDT) surfaces between station pairs represents an effective strategy for mitigating picking errors. Typically, EDT surfaces are constructed using either a fixed width or a fixed probability density function (pdf), which presents challenges in simultaneously achieving high resolution and accuracy. To address this issue, we propose an adaptive probabilistic imaging location method that constructs EDT surfaces by incorporating an adaptive pdf linked to the SNR of arrival picking data. The location probability imaging function is defined as the product of the independent EDT surfaces used for locating sources. For high-SNR data, where picking errors are typically small, the adaptive pdf converges more rapidly than the Gaussian distribution, yielding higher resolution by assigning lower probabilities to locations distant from true positions. For low-SNR data, where picking errors are typically large, the adaptive pdf exhibits heavy-tailed characteristics with a slower rate of decrease, enhancing the accuracy by assigning higher probabilities to likely true locations. The effectiveness and stability of the method are validated through theoretical analyses and synthetic data tests. Application to mine microseismic data indicates that the proposed method improves the accuracy and resolution of microseismic event locations relative to other methods.
期刊介绍:
IEEE Transactions on Geoscience and Remote Sensing (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.