A deep learning-based adaptive denoising approach for fine identification of rock microcracks from noisy strain data

Abstract

Most of the existing deep learning approaches about crack identification are difficult to obtain a satisfactory result when confronting highly noisy distributed fibre optic sensing (DFOS) data. To address this limitation, this research develops a hybrid attention residual shrinkage network (HARSNet) to enhance feature extraction ability from highly noisy DFOS data and achieve a high rock (micro) crack identification accuracy. Considering that it is challenging to set appropriate threshold values to eliminate data noise, a hybrid attention module is developed as trainable modules for the HARSNet to automatically and adaptively capture the thresholds relevant to data noise, so that the professional expertise on threshold determination is not required. Then a soft thresholding layer embedded in the HARSNet uses the captured thresholds to automatically eliminate noise to make the crack-related features more discriminative. The effectiveness of the proposed HARSNet in improving crack identification accuracy is examined through experimental comparisons with the other five state-of-the-art deep learning models. Results indicate that the proposed HARSNet outperforms the five deep learning models by yielding a maximal accuracy improvement of 12.9% on the highly noisy DFOS dataset with a signal-to-noise ratio of 0, which is satisfying in rock (micro) crack identification from highly noisy DFOS data.