A machine-learning-derived seismic catalog comprising 95,993 earthquakes (−1 ≤ ML ≤ 4.3) in the Raton Basin from 2016 to 2024

This catalog serves as a supplement to the manuscript titled "Evidence for Fluid Pressurization of Fault Zones and Persistent Sensitivity to Injection Rate Beneath the Raton Basin" (Manuscript ID: 2025GL114675R), accepted for publication in Geophysical Research Letters. We employed the deep neural network algorithm PhaseNet (Zhu & Beroza, 2019) to perform seismic phase picking on eight years (2016–2024) of continuous waveform data recorded by eight broadband stations in the Raton Basin. To associate P- and S-wave arrivals and determine preliminary earthquake locations, we used the Rapid Earthquake Association and Location method (REAL; Zhang et al., 2019). This approach yielded a seismic catalog containing 95,993 earthquakes with local magnitudes ranging from −1.0 to 4.3. Zhang, M., Ellsworth, W. L., & Beroza, G. C. (2019). Rapid Earthquake Association and Location. Seismological Research Letters, 90(6), 2276–2284. https://doi.org/10.1785/0220190052 Zhu, W., & Beroza, G. C. (2019). PhaseNet: A deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261–273. https://doi.org/10.1093/gji/ggy423