SUMMARY
It is essential to pick P-wave and S-wave arrival times rapidly and accurately for the microseismic monitoring systems. Meanwhile, it is not easy to identify the arrivals at a true phase automatically using traditional picking method. This is one of the reasons that many researchers are trying to introduce deep neural networks to solve these problems. Convolutional neural networks (CNNs) are very attractive for designing automatic phase pickers especially after introducing the fundamental network structure from semantic segmentation field, which can give the probability outputs for every labelled phase at every sample in the recordings. The typical segmentation architecture consists of two main parts: (1) an encoder part trained to extracting coarse semantic features; (2) a decoder part responsible not only for recovering the input resolution at the output but also for obtaining sparse representation of the objects. The fundamental segmentation structure performs well; however, the influence of the parameters in the structure on the pickers has not been investigated. It means that the structure design just depends on experience and tests. In this paper, we solve two main questions to give some guidance on network design. First, we show what sparse features will learn from the three-component microseismic recordings using CNNs. Second, the influence of two key parameters in the network on pickers, namely, the depth of decoder and activation functions, is analysed. Increasing the number of levels for a certain layer in the decoder will increase the burden of demand on trainable parameters, but it is beneficial to the accuracy of the model. Reasonable depth of the decoder can balance prediction accuracy and the demand of labelled data, which is important for microseismic systems because manual labelling process will decrease the real-time performance in monitoring tasks. Standard rectified linear unit (ReLU) and leaky rectified linear unit (Leaky ReLU) with different negative slopes are compared for the analysis. Leaky ReLU with a small negative slope can improve the performance of a given model than ReLU activation function by keeping some information about the negative parts.
ERDNN: Error-Resilient Deep Neural Networks With a New Error Correction Layer and Piece-Wise Rectified Linear Unit