The Delaware Basin, Texas is currently a hot-spot of induced seismicity and ground deformation due to fluid extraction and injection associated with horizontal drilling techniques; however, the driving mechanism behind the seismicity and deformation remains under debate. Using vertical and east-west horizontal surface deformation measurements derived from Sentinel-1 InSAR, we show that the subsurface responds differently to oil and gas activity in the northern and southeastern portions of the basin. In the north, where there is little seismicity, deformation patterns display long-wavelengths and equidimensional patterns. In contrast, the southeast region hosts most of the seismicity and displays spatial deformation patterns with narrow linear features that strike parallel to the maximum principal horizontal stress and to trends in seismicity, suggesting movement along normal faults. We model a linear deformation feature using edge dislocations and show that the InSAR observations can be reproduced by slip on normal faults contained within the Delaware Mountain Group (DMG), the formation that hosts local wastewater injection and the majority of earthquakes. Our model consists of three parallel, high-angle normal faults, with two dipping toward one another in a graben structure. Slip magnitudes reach up to 27.5 cm and are spatially correlated with injection wells. Measured seismicity can only explain ~2% of the fault motion predicted by our fault model, suggesting that slip leading to the deformation is predominantly aseismic. We conclude that seismic and aseismic fault motion in the southeastern Delaware Basin is likely driven by wastewater injection near critically-stressed normal faults within the DMG.

The successful application of deep learning for seismic phase arrival time picking has increased the efficacy of earthquake catalog development workflows. Earthquake catalogs with lower magnitude of completeness and better locational precision than current standard practice can now be generated with very limited need for human review and without the need for earthquake templates, which are not always available. Here, we report on a ‘Deep Earthquake Catalog’ with over 300,000 events from a geographically extensive region spanning Oklahoma and Southern Kansas from January 2010 to December 2020 developed using a workflow that leverages deep learning for phase picking. The increased number of events and improved spatial resolution compared to the previous statewide catalogs reveals numerous discrete faults and both broad trends and localized patterns of seismicity. This rich dataset provides new opportunities for data-driven analyses of induced earthquakes.

Inflationary deformation and very long period (VLP) earthquakes frequently accompany basaltic caldera collapses, yet current interpretations do not reflect physically consistent mechanisms. We present a lumped parameter model accounting for caldera block/magma momentum change, magma chamber pressurization, and ring fault shear stress drop. The effect of pressurizing a spheroidal chamber is represented as a tri-axial expansion source, and the combined caldera block/magma momentum change as a vertical single force. The model is applied to Kīlauea 2018 caldera collapse events, accurately predicting near field static/dynamic ground motions. In addition to the tri-axial expansion source, the single force contributes significantly to the VLP waveforms. For an average collapse event with fully developed ring fault, Bayesian inversion constrains ring fault stress drop to ~0.4 MPa and the pressure increase to ~1.7 MPa. That the predictions fit both geodetic and seismic observations confirms that the model captures the dominant caldera collapse mechanisms.

The 17 May 2012 M4.8 Timpson earthquake is the largest known earthquake in eastern Texas. It is suspected to have been induced by wastewater injection from two nearby, high-volume wells. Its cataloged aftershocks form a NW-SE trend, which unlike other induced earthquakes sequences, is unfavorably oriented for failure in the local stress field. To understand this, we enriched the catalog using PhaseNet, a deep-learning-based picker followed by double-difference relocation with cross-correlation-based differential traveltimes. We clustered the aftershocks based on waveform similarity. Most of the seismicity falls into two-clusters, which define a complex fault structure of two parallel subfaults that are more favorably oriented than the overall trend. We inferred from waveform similarity that the sequence initiated on the northern subfault with a M3.9 foreshock and M4.8 mainshock, then extended to the southern subfault with a M4.1 aftershock, and was finally reactivated on the northern subfault with two more M4 events.

We revisited the June, 2010 - October, 2011 Guy-Greenbrier earthquake sequence in central Arkansas using PhaseNet, a deep neural network trained to pick P and S arrival times. We applied PhaseNet to continuous waveform data and used phase association and hypocenter relocation to locate nearly 90,000 events. Our catalog suggests that the sequence consists of two adjacent earthquake sequences on the same fault and that the second sequence may be associated with the wastewater disposal well to the west of the Guy-Greenbrier Fault, rather than the wells to the north and the east that were previously implicated. We find that each sequence is comprised of many small clusters that exhibit diffusion along the fault at shorter time scales. Our study demonstrates that machine learning based earthquake catalog development is now feasible and will yield new insights into earthquake behavior.