A deep learning-based approach with anti-noise ability for identification of rock microcracks using distributed fibre optic sensing data

Most of the existing deep learning-based crack identification models can achieve high accuracy when being trained and tested using data split from the same dataset with minimal noise, while perform poorly on field monitoring data with certain level of noise. This research developed a hybrid attention convolutional neural network (HACNN) for rock microcrack identification with enhanced anti-noise ability for distributed fibre optic sensing data. A hybrid attention module was designed and placed next to some certain convolutional layers to enhance the nonlinear representation ability of the proposed model. Two training interference strategies, namely small mini-batch training and adding dropout in the first convolutional layer, were employed to interfere with the training of the HACNN to enhance its robustness against noise. A series of experiments are designed based on the properties of the two training interference strategies to optimize the model parameters. Results showed that the optimized HACNN achieved higher accuracy on datasets with different signal-to-noise ratios compared to other machine learning algorithms, including the support vector machine, the multilayer perceptron, and an existing one-dimensional convolutional neural network. This research demonstrates the potential of establishing a robust DL-based model for identification of rock microcracks from noisy distributed fibre sensing optic data, even when training the model only with a smoothed dataset.