Clustering distributed acoustic sensing signals via curvelet transform and unsupervised deep learning

Distributed acoustic sensing (DAS), a technology that exhibits great potential for subsurface monitoring and imaging, has been regarded as a preeminent instrument for vibration measurements. In light of the tremendous amount of seismic data, numerous channels, and elevated noise levels, it becomes imperative to suggest an appropriate denoise procedure that is compatible with DAS data. In this regard, unsupervised deep learning with data clustering generally exhibits superior performance in facilitating the efficient analysis of sizable unlabeled data sets devoid of human bias. In addition, the clustering method is capable of detecting seismic waves, microseismic turbulence, and even unidentified new types of negligible seismic events, in contrast to a number of conventional denoising techniques. While current approaches reliant on f-k analysis remain valuable, they fail to fully exploit the information present in the wavefield due to their inability to identify the characteristic moveout observed in seismic data. In order to denoise DAS data more effectively, we investigate the capacity of the curvelet transform to extend existing deep scattering network methodologies. In this paper, we propose a novel clustering approach for the denoise processing of DAS data that utilises the Gaussian Mixture Model (GMM), curvelet transform, and unsupervised deep learning. The DAS data are initially subjected to the curvelet transform in order to derive the curvelet coefficients at various scales and orientations, which can be regarded as the first layer of extracted features. Following this, a deeper layer of features is obtained by applying the curvelet transform to the coefficients in the first layer. The aforementioned process continues in this manner until the depth of the layer satisfies the algorithm-determined expectation. By concatenating the curvelet coefficients from each layer, the original DAS data's features are generated. Afterwards, the signal is reduced to two dimensions using principal component analysis (PCA), which simplifies its interpretation by projecting the high-dimensional features onto two principal components, which facilitates the clustering of the features by GMM for achieving the final clustered results. This methodology operates without the need for labels of DAS data and is highly appropriate for managing the substantial quantity and numerous channels of DAS. We used a variety of approaches, such as Bayesian information criteria and silhouette analysis, to determine the optimal number of clusters in GMM and evaluate the algorithm's clustering performance. We demonstrate the method on downhole data acquired during stimulation of the Utah FORGE enhanced geothermal system, and the results appear quite satisfactory, indicating that it can be utilised effectively to denoise DAS signals.