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Earthquake swarms are ubiquitous in volcanic systems, being manifestations of underlying nontectonic processes such as magma intrusions or volatile fluid transport. The Long Valley caldera, California, is one such setting where episodic earthquake swarms and persistent uplift suggest the presence of active magmatism. We quantify the long-term spatial and temporal characteristics of seismicity in the region using cluster analysis on a 25-year high-resolution earthquake catalog derived using leading-edge deep-learning algorithms. Our results show that earthquake swarms beneath the caldera exhibit enlarged families with statistically significant tendency for upward migration patterns. The ascending swarms tend to nucleate at the base of the seismogenic zone with a spatial footprint that is laterally constrained by the southern rim of the caldera. We suggest that these swarms are driven by the transport of volatile-rich fluids released from deep volcanic processes. The observations highlight the potential for extreme spatial segmentation of earthquake triggering processes in magmatic systems. 

We present an updated catalogue of seismicity in the Dallas-Fort Worth basin from 2008 to the end of 2019 using state-of-the-art phase picking and association methods based on machine learning. We then calculate the pore pressure and poroelastic stress changes on a monthly basis between 2000 and 2020 for the whole basin, incorporating fluid injection/extraction histories at 104 saltwater injection and 20576 production wells. These pore pressure and poroelastic stress changes are calculated using coupled analytical solutions for a point source injection in a 3D homogeneous isotropic medium, and are superposed for all wells. We suggest that the poroelastic effects of produced gas and water contribute significantly to fault instability.