微地震监测技术是监测水力压裂过程、评价压裂效果的重要手段。对于地面监测，P波极性能够直接、快速地反演震源机制，同时极性校正能够提高绕射叠加定位方法的成像精度。因此，准确而迅速地确定P波极性对地面微地震实时监测具有重要意义。卷积神经网络是一种深度学习算法，具有强大的特征学习与分类能力，可用来确定微地震事件的P波极性。地面监测多采用星型、网格型等规则观测系统，本文使用目标道及其相邻检波器记录作为输入样本，构建基于卷积神经网络的多道P波极性分类网络模型。实际数据应用结果表明，相比于单道记录的网络模型，多道的网络模型能够将目标道与相邻道相结合来预测目标道的极性，提高规则观测系统下地面微地震P波极性分类的准确率。

Microseismic monitoring technique is an important tool for hydraulic fracturing process monitoring and fracturing effect evaluation. For surface monitoring,the P-wave polarity can directly and quickly invert the focal mechanism,while the polarity correction can also improve the imaging accuracy of the diffraction-based location method. Therefore,accurate and rapid determination of P-wave polarity is of great significance for real-time monitoring at surface. Convolutional neural network(CNN)is a deep learning algorithm with powerful feature learning and classification capabilities. It can also be used to determine the P-wave polarity of microseismic events. Since microseismic monitoring at surface mostly uses star,grid,or other regular acquisitions,in this paper,we use the target trace and its neighboring seismograms as input sample to build a multi-trace P-wave polarity classification network model based on convolutional neural network. The results from field data application show that,in comparison to the single-trace based CNN,the multi-trace based CNN model can combine the target trace with the neighboring traces to predict the polarity of the target trace,and improve the accuracy of polarity classification for surface microseismic data from a regular observation system.

P315

国家自然科学基金（42004040、U1901602、41704040、41904044）资助