小数据集的微地震信号震相拾取方法

摘要

近年来，深度卷积神经网络在地震信号震相拾取的研究中得到了广泛的研究，此类模型同时拾取P波和S波震相，在天然地震台网的地震数据震相拾取中应用推广较好。但是由于此类模型需要很大的标签数据量，同时计算复杂度也很高，在标签样本较少的微地震监测中应用时受限较严重。已有研究表明，P波拾取的复杂度比S波拾取要低，只拾取P波震相时，模型复杂度可以较简单，标签需求量也较低。在P波拾取完成后，通过极化分析和旋转可以很好的提取S波特征，从而降低S波拾取的复杂度。基于此种想法，本文联合机器学习、极化分析和时频分析等方法，将P波拾取和S波拾取进行分离，降低深度卷积神经网络的模型复杂性，得到准确率较高的微震信号自动识别及到时拾取的模型，同时降低了训练所需数据量。 In recent years, deep convolutional neural network has been extensively studied in the study of seismic phase pickup of seismic signals. However, such a model can pick up both P wave and S wave seismic phases, and it is well applied and popularized in seismic phase pickup of seismic data of natural seismic network. However, due to the large amount of tag data and high computational complexity, the application of such a model in microseismic monitoring with few tag samples is severely limited. Previous studies have shown that the complexity of P wave pickup is lower than that of S wave pickup. Only when P wave shock phase is picked up, the model complexity can be simpler and the label demand is lower. After the p-wave pickup is completed, the S-wave signature can be well extracted through polarization analysis and rotation, thus reducing the complexity of s-wave pickup. Based on this idea, this paper combines machine learning, polarization analysis, time-frequency analysis and other methods to separate P-wave pickup and S-wave pickup, so as to reduce the model complexity of deep convolutional neural network, obtain a model with high accuracy for automatic identification of microseismic signals and timely pickup, and meanwhile reduce the amount of data required for training.