With the 2008 Ms6.1 Panzhihua earthquake as a case study, we demonstrate that the focal depth of the main shock can be well constrained with two approaches： （1） using the depth phase sPL and （2） using full wavefo...With the 2008 Ms6.1 Panzhihua earthquake as a case study, we demonstrate that the focal depth of the main shock can be well constrained with two approaches： （1） using the depth phase sPL and （2） using full waveform inversion of local and teleseismic data. We also show that focal depths can be well constrained using the depth phase sPL with single broadband seismic station. Our study indicates that the main shock is located at a depth of ii kin, much shallower than those from other studies, confirming that the earthquake occurs in upper crust. Aftershocks are located in the depth range of 11 16 kin, which is consistent with a ruptured near vertical fault whose width is about 10 km, as expected for an Ms6.1 earthquake.展开更多
In this paper we propose some waveform relaxation (WR) methods for solving large systems of initial value problems. Nonlinear ODEs, linear ODEs, semi-explicit DAEs and linear DAEs are discussed. The accuracy increase ...In this paper we propose some waveform relaxation (WR) methods for solving large systems of initial value problems. Nonlinear ODEs, linear ODEs, semi-explicit DAEs and linear DAEs are discussed. The accuracy increase for WR methods is investigated.展开更多
Microseismic source localization plays a critical role in monitoring mining-induced dynamic disasters,assessing rock mass stability,and analyzing excavation-induced disturbances.With increasing monitoring accuracy and...Microseismic source localization plays a critical role in monitoring mining-induced dynamic disasters,assessing rock mass stability,and analyzing excavation-induced disturbances.With increasing monitoring accuracy and data volume,various localization techniques have emerged to suit different scenarios.We systematically review the development of current microseismic location methods,which can be broadly categorized into three types:(1)Pickingbased methods,such as the Geiger and double-difference algorithms,which are suitable for well-constrained velocity models;(2)Waveform stacking-based localization methods,such as the source scanning algorithm(SSA)and cross-correlation stacking,which eliminate the need for arrival-time picking.These techniques exhibit strong noise resistance and are particularly well-suited for environments with low signal-to-noise ratios(SNR);and(3)Deep learning-based automatic localization approaches,such as PhaseNet and LOCFLOW,which are suitable for large-scale,intelligent monitoring.Furthermore,key factors affecting localization accuracy,such as sensor array geometry,arrival-time picking errors,and velocity model uncertainties,are discussed,along with optimization strategies including 3D velocity tomography,non-predefined velocity inversion,and time-varying velocity modeling.Finally,we explore future directions,including multi-station collaborative deep learning models,intelligent denoising techniques,and risk prediction frameworks constrained by statistical seismology,aiming to advance microseismic localization toward higher precision and robustness.展开更多
基金financial supported by Joint Seismological Science Foundation of China (No.200808078)National Natural Science Foundation of China (Nos.40821160549 and 41074032)
文摘With the 2008 Ms6.1 Panzhihua earthquake as a case study, we demonstrate that the focal depth of the main shock can be well constrained with two approaches： （1） using the depth phase sPL and （2） using full waveform inversion of local and teleseismic data. We also show that focal depths can be well constrained using the depth phase sPL with single broadband seismic station. Our study indicates that the main shock is located at a depth of ii kin, much shallower than those from other studies, confirming that the earthquake occurs in upper crust. Aftershocks are located in the depth range of 11 16 kin, which is consistent with a ruptured near vertical fault whose width is about 10 km, as expected for an Ms6.1 earthquake.
文摘In this paper we propose some waveform relaxation (WR) methods for solving large systems of initial value problems. Nonlinear ODEs, linear ODEs, semi-explicit DAEs and linear DAEs are discussed. The accuracy increase for WR methods is investigated.
基金funded by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(2024ZD1004505)Gansu Provincial Joint Research Fund for the Year 2024(24JRRA803)+1 种基金Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology(2022B1212010002)the National Natural Science Foundation of China(42174128).
文摘Microseismic source localization plays a critical role in monitoring mining-induced dynamic disasters,assessing rock mass stability,and analyzing excavation-induced disturbances.With increasing monitoring accuracy and data volume,various localization techniques have emerged to suit different scenarios.We systematically review the development of current microseismic location methods,which can be broadly categorized into three types:(1)Pickingbased methods,such as the Geiger and double-difference algorithms,which are suitable for well-constrained velocity models;(2)Waveform stacking-based localization methods,such as the source scanning algorithm(SSA)and cross-correlation stacking,which eliminate the need for arrival-time picking.These techniques exhibit strong noise resistance and are particularly well-suited for environments with low signal-to-noise ratios(SNR);and(3)Deep learning-based automatic localization approaches,such as PhaseNet and LOCFLOW,which are suitable for large-scale,intelligent monitoring.Furthermore,key factors affecting localization accuracy,such as sensor array geometry,arrival-time picking errors,and velocity model uncertainties,are discussed,along with optimization strategies including 3D velocity tomography,non-predefined velocity inversion,and time-varying velocity modeling.Finally,we explore future directions,including multi-station collaborative deep learning models,intelligent denoising techniques,and risk prediction frameworks constrained by statistical seismology,aiming to advance microseismic localization toward higher precision and robustness.
