MRANet: An Encoder-Decoder Network with Multi-Scale Residual Atrous-Spatial Pyramid Pooling for Seismic Phase Picking

Abstract

Seismic phase picking is one of the critical challenges in seismic data processing. With the advancement of deep learning, numerous neural network architectures have been employed to explore the correlations between seismic waveforms and the underlying information. However, existing methods predominantly rely on convolutional neural network (CNN)-based feature extraction techniques, which often overlook the denoising of input data. Additionally, CNN-based feature extraction methods face limitations in seismic phase picking tasks, particularly in capturing long-range dependencies. Inspired by the Atrous-Spatial Pyramid Pooling (ASPP) framework, this paper proposes an attention mechanism-based multi-scale residual Atrous-Spatial Pyramid Pooling module (ResASPP), which effectively captures detailed information across different scales, enabling efficient extraction of seismic signal features. Based on this module, a multi-scale residual ASPP neural network (MRANet) with multi-scale feature extraction capabilities is constructed for seismic phase picking tasks. Moreover, to improve the quality of seismic data and enhance interpretability, denoising techniques are applied to the input data. Extensive experiments conducted on the DiTing dataset demonstrate that our model achieves higher F1 scores (89.45% for P-waves and 80.40% for S-waves), outperforming existing state-of-the-art models.