CT理论与应用研究2025，Vol.34Issue(5)：815-825,11.DOI:10.15953/j.ctta.2025.212
基于通道及多头注意力机制的深度学习模型对短周期密集台阵资料进行震相识别
Seismic Phase Identification Using the Records from a Short Period Dense Seismic Array Based on a Deep Learning Model with Channel and Multi-head Attention Mechanisms
摘要
Abstract
Phase identification is the most fundamental and important task in seismology and is crucial to in-depth analyses of regional seismic activity and the precise detection of underground structures.The considerable growth in earthquake observation data and the rapid advances in machine learning technology have meant that various machine learning-based seismic phase recognition methods have emerged,such as Eqtransformer and Phasenet,which have significantly promoted the development of this field.However,these models often face challenges when processing short-period dense array data,such as high missed detection rates for small microseismic events and the incomplete waveform recognition of medium-to-strong earthquakes.To address these issues,this study developed a microseismic phase recognition model that uses channel and multi-head attention mechanisms.It effectively integrates coarse-and fine-grained features through residual connections,optimizes feature representation using a channel attention mechanism,and enhances the ability to focus on key information through a multihead attention mechanism.Training data are derived from multiple sources and have high confidence-to-noise ratios;therefore this study adopted a strategic data augmentation method to construct noisy(including Gaussian random white noise and STEAD dataset noise)and denoised datasets in a 6:4 ratio to improve the generalization ability of the model in complex field noise environments.The experimental results show that compared to existing models,such as Eqtransformer and Phasenet,the proposed model significantly improves the recognition rate for small microseismic events and optimizes the recognition accuracy of P-wave and S-wave arrival times.In addition,the model has a fast recognition speed and can complete the comprehensive recognition of one month of data from a single station within ten minutes,which demonstrates that it can efficiently process big data.This study successfully constructed an efficient and accurate microseismic phase recognition model by introducing advanced attention mechanisms and deep-learning architectures.This model considerably improves the ability to identify small and micro earthquakes,enhances model generalization,accelerates the recognition process,and provides a powerful tool for micro earthquake detection.关键词
震相识别/深度学习/微震识别/机器学习Key words
seismic phase recognition/deep learning/microseismic recognition/machine learning分类
天文与地球科学引用本文复制引用
陈枭,沈旭章,黄昱涵,黄河..基于通道及多头注意力机制的深度学习模型对短周期密集台阵资料进行震相识别[J].CT理论与应用研究,2025,34(5):815-825,11.基金项目
国家自然科学基金(祁连山造山带深部结构与晚新生代构造变形研究(42230305)) （祁连山造山带深部结构与晚新生代构造变形研究(42230305）
第二次青藏高原综合科学考察研究(关键地区岩石圈精细结构与浅部响应(20190ZKK0701)). （关键地区岩石圈精细结构与浅部响应(20190ZKK0701）
