Denoising Method for Microseismic Signals with Convolutional Neural Network Based on Transfer Learning
Xuegui Li, Shuo Feng, Yuantao Guo, Hanyang Li, Yingjie Zhou
Abstract
Abstract Microseismic signals contain various information for oil and gas developing. Increasing the signal-to-noise ratio of microseismic signals can successfully improve the effectiveness of oil and gas resource exploration. The lack of sufficient labeled microseismic signals makes it difficult to train neural network model. Transfer learning can solve this problem using image data sets to pre-train the denoising model and the learned knowledge can be transferred into microseismic signals denoising. In addition, a convolutional neural network (CNN) model with 16 layers is designed for noise reduction. Considering the strong similarity between noisy signals and denoising signals, residual learning is utilized to optimize the denoising model. The simulation experiment results show that the proposed denoising model eliminates the noise in the microseismic signals effectively and quickly, restores the amplitude of the microseismic signals with high accuracy, and has excellent effect in denoising on the information at the edge.