Conditional Diffusion Model for Robust 3-D Microseismic Event Localization from Noisy Waveforms

Locating microseismic events is crucial to monitoring fracking activities, CO2 injection, and reservoirs in general. However, the process of locating such events is challenging, especially in low signal-to-noise scenarios. Thus, we introduce a conditional diffusion model for direct inversion of 3-D microseismic event locations from relatively irregularly spaced noisy waveform gathers, eliminating the need for manual arrival-time picking and reducing sensitivity to noise and velocity model uncertainty. The influence of the unknown event origin time on the waveform gathers is eliminated by cross-correlating every trace from the gather with a reference trace from the same gather. The 3-D microseismic event location coordinates (x, y, z) are projected onto two 2-D (X-Z and Y-Z) Gaussian heatmaps, enabling the use of a 2-D conditional diffusion model for the inversion task in contrast to traditional localization techniques. The model is trained to generate accurate microseismic event locations conditioned on microseismic waveform gathers. Tests on semi-synthetic data from the ToC2ME project demonstrate the model’s accuracy, robustness to noise, and computational efficiency. Furthermore, results from a real field microseismic event indicate high stability in the predicted event coordinates. These findings confirm the feasibility of using conditional diffusion models for reliable field-scale event localization.