LAUSR

With the goal of obtaining high-quality downhole distributed acoustic sensing (DAS) data from multiple types of complex noise, denoising plays an important role. However, conventional denoising methods cannot achieve a satisfactory effect toward multiple types of complex noise. Recently, convolutional neural networks (CNNs) exhibit dramatic improvements over conventional methods. The existing CNN-based methods typically operate through incorporation with conventional methods for adaptive threshold, additional multiscale idea, or attentional mechanism for more features extraction. In these cases, serious signal loss or artificial events are usually generated. Maybe, the recovered events are not continuous with some breakpoints and derangement. To resolve these problems in current networks, we propose a novel iterative parallel-attention guided multibranch residual network (PA-MRNet) with the collective goals of suppressing multiple types of complex noise and recovering buried weak signals. The core of our method is an attentional multibranch residual block (AMRB) containing: parallel multiresolution convolution streams for multiresolution features extraction, proposed parallel attention block (PAB) design for interested features capture, and novel parallel-attention guided fusion block for features fusion. In a word, our method can learn an enriched set of features to suppress multiple types of complex noise and simultaneously recover weak signals continuously with less energy loss. Experiments on synthetic and field DAS data demonstrate the better performance of the proposed iterative PA-MRNet.