Seismic phase picking is the preliminary work of earthquake location and body-wave travel time tomography.Manual picking is considered as the most accurate way to access the arrival times but time consuming.Many autom...Seismic phase picking is the preliminary work of earthquake location and body-wave travel time tomography.Manual picking is considered as the most accurate way to access the arrival times but time consuming.Many automatic picking methods were proposed in the past decades,but their precisions are not as high as human experts especially for events with low ratio of signal to noise and later arrivals.As the increasing deployment of large seismic array,the existing methods can not meet the requirements of quick and accurate phase picking.In this study,we applied a phase picking algorithm developed on the base of deep convolutional neuron network(PickNet)to pick seismic phase arrivals in ChinArray-Phase III.The comparison of picking error of PickNet and the traditional method shows that PickNet is capable of picking more precise phases and can be applied in a large dense array.The raw picked travel-time data shows a large variation deviated from the traveltime curves.The absolute location residual is a key criteria for travel-time data selection.Besides,we proposed a flowchart to determine the accurate location of the single-station earthquake via dense seismic array and phase arrival picked by PickNet.This research expands the phase arrival dataset and improves the location accuracy of single-station earthquake.展开更多
基金This study was financially supported by National Key R&D Program of China(No.2018YFC1504103)the National Natural Science Foundation of China(No.41774067)the Special Fund of the Institute of Geophysics,China Earthquake Administration(Nos.DQJB19B05 and DQJB20X07).
文摘Seismic phase picking is the preliminary work of earthquake location and body-wave travel time tomography.Manual picking is considered as the most accurate way to access the arrival times but time consuming.Many automatic picking methods were proposed in the past decades,but their precisions are not as high as human experts especially for events with low ratio of signal to noise and later arrivals.As the increasing deployment of large seismic array,the existing methods can not meet the requirements of quick and accurate phase picking.In this study,we applied a phase picking algorithm developed on the base of deep convolutional neuron network(PickNet)to pick seismic phase arrivals in ChinArray-Phase III.The comparison of picking error of PickNet and the traditional method shows that PickNet is capable of picking more precise phases and can be applied in a large dense array.The raw picked travel-time data shows a large variation deviated from the traveltime curves.The absolute location residual is a key criteria for travel-time data selection.Besides,we proposed a flowchart to determine the accurate location of the single-station earthquake via dense seismic array and phase arrival picked by PickNet.This research expands the phase arrival dataset and improves the location accuracy of single-station earthquake.
