Automated Multi-well DAS Microseismic Localization via Interferometric-RANSAC and Double-Difference Time Reverse Imaging

Distributed Acoustic Sensing (DAS) offers high-density seismic data with immense potential for microseismic monitoring, yet extracting reliable signals from massive, noisy recordings remains a challenge. To address this, we present a novel end-to-end automated algorithm for microseismic localization using multi-well DAS systems. First, we introduce a foundational workflow termed ‘Interferometric-RANSAC’. By computing all-to-all cross-correlations between receivers, this method transforms weak signals into coherent virtual gathers. It then leverages the spatial continuity of DAS data through a robust Random Sample Consensus (RANSAC) fitting strategy to automatically extract high-fidelity relative travel times, eliminating the need for manual picking or supervised model training. Second, building on these robust inputs, we employ Double-Difference Time Reverse Imaging (DD-TRI) to determine source locations. This imaging condition effectively mitigates the impact of velocity model inaccuracies by utilizing differential travel times. We validate the proposed framework using both synthetic data and field data from a six-well monitoring network. The results demonstrate that our approach achieves high localization precision and maintains strong robustness even in the presence of picking errors and velocity model discrepancies, offering a practical solution for industrial-scale DAS monitoring.