Signal Enhancement in Distributed Acoustic Sensing Data Using a Guided Unsupervised Deep Learning Network

TL;DR

This paper introduces an unsupervised deep learning approach for denoising Distributed Acoustic Sensing data, effectively enhancing seismic signals without requiring labeled training data, and validated on real field datasets.

Contribution

The study presents a novel unsupervised deep learning model incorporating self-attention for DAS data denoising, eliminating the need for labeled datasets and improving signal clarity.

Findings

Outperforms three benchmark denoising methods on field datasets

Effectively suppresses noise while preserving seismic signals

Validated on SAFOD and FORGE fiber-optic DAS data

Abstract

Distributed Acoustic Sensing (DAS) is a promising technology introducing a new paradigm in the acquisition of high-resolution seismic data. However, DAS data often show weak signals compared to the background noise, especially in tough installation environments. In this study, we propose a new approach to denoise DAS data that leverages an unsupervised deep learning (DL) model, eliminating the need for labeled training data. The DL model aims to reconstruct the DAS signal while simultaneously attenuating DAS noise. The input DAS data undergo band-pass filtering to eliminate high-frequency content. Subsequently, a continuous wavelet transform (CWT) is performed, and the finest scale is used to guide the DL model in reconstructing the DAS signal. First, we extract 2D patches from both the band-pass filtered data and the CWT scale of the data. Then, these patches are converted using an unrolling mechanism into 1D vectors to form the input of the DL model. The architecture of the proposed DL network is composed of several fully-connected layers. A self-attention layer is further included in each layer to extract the spatial relation between the band-pass filtered data and the CWT scale. Through an iterative process, the DL model tunes its parameters to suppress DAS noise, with the band-pass filtered data serving as the target for the network. We employ the log cosh as a loss function for the DL model, enhancing its robustness against erratic noise. The denoising performance of the proposed framework is validated using field examples from the San Andreas Fault Observatory at Depth (SAFOD) and Frontier Observatory for Research in Geothermal Energy (FORGE) datasets, where the data are recorded by a fiber-optic cable. Comparative analyses against three benchmark methods reveal the robust denoising performance of the proposed framework.