Enhancing Seismic P-Wave Arrival Picking by Target-Oriented Detection of the Local Windows Using Faster-RCNN

Abstract

The accuracy of P-wave arrival picking is essential for seismic analysis. The improvement in the accuracy of P-wave arrival picking is generally achieved through improved algorithms and the processing of waveforms. Therefore, we propose a method that uses deep learning to detect local windows to enhance the accuracy of P-wave arrival picking. The local window is defined as a short time window containing the main components of the signal. The faster-RCNN model is trained on the dataset with the calibrated local window. The trained faster-RCNN model is used for the local window detection of new records, and the existing algorithm is going to work in the local window. As a validation, four kinds of automatic P-wave arrival picking algorithms (wavelet-transform-based approach, PphasePicker algorithm, STAFD/LTAFD algorithm, and deep learning method) are used to conduct experiments in synthetic seismic records and field seismic records, respectively. The field experimental results show that the method proposed in this article can improve the picking capacity of the four methods by 17.5%, 37.6%, 62.4%, and 46.8%, respectively. No matter which algorithm is used, the accuracy of P-wave arrival picking in the local window is generally enhanced. The method presented in this article has a positive effect on improving the accuracy of seismic records.