A multi-scale feature fusion network based on semi-channel attention for seismic phase picking

Abstract

In the field of seismic data processing, deep learning technologies have been widely used for seismic phase picking. However, it is difficult to take full advantage of the features extracted at different stages in existing models. In this paper, a multi-scale feature fusion network was proposed for seismic phase picking to address this problem. In the stage of feature extraction, semi-channel attention is introduced. It improves the representation ability of the model by efficiently utilizing the feature information extracted from the encoder. In the stage of decoding, a channel compression module is designed to reduce the number of feature channels. It improves the receptive field of channels. Additionally, a multi-feature fusion module is presented to integrate features at multiple scales. It reduces the loss of useful information and improves the accuracy of phase picking. The effectiveness of our network is validated on Stanford earthquake dataset, where the picking errors for phase picking are 2 ms. The parameter of our network is only 52,100. Compared with earthquake transformer, it has 42.1% fewer time costs to process 12,656 test samples on Graphics Processing Unit.