Application of a convolutional neural network for seismic phase picking of mining-induced seismicity.

ABSTRACT: Monitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rock-bursting, more safely and efficiently. Unfortunately, processing MIS data usually requires significant effort from human analysts, which can result in substantial costs and time commitments. The problem is exacerbated for operations that produce copious amounts of MIS, such as mines with high-stress and/or extraction ratios. Recently, deep learning methods have shown the ability to significantly improve the quality of automated arrival-time picking on earthquake data recorded by regional seismic networks. However, relatively li

SUBMITTER: Johnson SW 

PROVIDER: S-EPMC8455167 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature