PHASER: A Deep Learning Model for Real-time Downhole Microseismic Event Picking

In this study we introduce PHASER, a seismic event picker developed specifically for downhole microseismic applications. In recent years, DL models have been extensively explored for automating phase-picking and/or event detection for seismic data, with most applications focusing on teleseismic/regional earthquake signals from surface arrays. Surface arrays require a generalizable solution across different array geometries. In contrast, downhole arrays used to monitor industrial activities such as geothermal, CCS and hydraulic fracturing are more standardized in receiver placement, but present unique challenges and opportunities. Seismic phases arrive coherently on closely-spaced downhole geophones. However, access to downhole data is also often limited, and such data are often available only as event-based traces, and labels are often incomplete or inaccurate. To address these constraints, PHASER is trained in a multi-stage framework that remains effective and generalizable even when catalogue labels are incomplete and limited. PHASER incorporates association filtering into its training; pick probabilities are matched with their respective association probabilities based on learned source-related feature embeddings for each P- and S- arrival. By using a learned extraction threshold, PHASER avoids the manual parameter tuning typically required for pick extraction. PHASER demonstrates better continuous monitoring performance on unseen sites than existing DL phase-pickers, achieving a 6-fold performance over PhaseNet in F1 score from 0.107 to 0.584 on an out of sample test dataset.