Denoising distributed acoustic sensing data using unsupervised deep learning

Abstract

Distributed acoustic sensing (DAS) technology has been widely used in seismic exploration to acquire high-quality data due to its noteworthy advantages, such as high coverage, high resolution, low cost, and strong environmental friendliness. However, the seismic signals acquired in DAS are often masked by various types of noise (e.g., high-frequency random, high-amplitude erratic, horizontal, and coupled noise), which seriously decreases the signal-to-noise ratio. We develop a fully connected neural network with dense and residual connections to attenuate various complex noises in real DAS data. The network is designed to learn the features of useful reflection signals and remove various noises in an unsupervised way, therefore enjoying the convenience of label-free processing. Our network uses several encoders and decoders to compress and reconstruct the abstract waveform features, respectively. Each encoder/decoder consists of one dense block with stacked fully connected blocks (FCBs). To transfer the shallow-level features to the deep level for reuse, we add the skip connections with one FCB between the corresponding encoders and decoders. Our method provides encouraging results when applied to synthetic and real DAS data sets. Compared with several traditional and advanced deep-learning methods, our method can more effectively attenuate strong noise and better extract hidden signals.