Exploring wavefield-based location imaging in heterogeneous media: a borehole DAS example

Earthquake localization using Distributed Acoustic Sensing (DAS) is challenging due to the single-component directional sensitivity of DAS systems. We propose a novel approach for localization that is based on dense DAS recordings and constraints from known structural heterogeneity of the subsurface. Our method employs full-waveform simulations to generate synthetic DAS wavefield images for a range of potential earthquake source locations. A deep convolutional neural network (CNN), based on a U-Net architecture, is trained on these images to map DAS-recorded wavefield patterns to earthquake source coordinates, without the need for identification and picking of P- and S-wave arrivals. We evaluate this wavefield-based localization technique using a challenging synthetic case study involving DAS recordings in a single vertical borehole, representative of monitoring configurations commonly deployed at geothermal platforms. We consider different velocity models of varying geological complexity. The results show that the CNN effectively learns location-specific wavefield signatures influenced by subsurface heterogeneity. Uncertainties can be reduced significantly by adding recordings from a second borehole. While the results are based on idealized 2D synthetic modeling, the method offers a promising approach for improving microseismic monitoring when detailed information on the heterogeneous velocity structure is available (such as that derived from seismic surveys).