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Abstract Our study is to build an aftershock catalog with a low magnitude of completeness for the 2020 Mw 6.5 Stanley, Idaho, earthquake. This is challenging because of the low signal-to-noise ratios for recorded seismograms. Therefore, we apply convolutional neural networks (CNNs) and use 2D time–frequency feature maps as inputs for aftershock detection. Another trained CNN is used to automatically pick P-wave arrival times, which are then used in both nonlinear and double-difference earthquake location algorithms. Our new one-month-long catalog has 4644 events and a completeness magnitude (Mc) 1.9, which has over seven times more events and 0.9 lower Mc than the current U.S. Geological Survey National Earthquake Information Center catalog. The distribution and expansion of these aftershocks improve the resolution of two north-northwest-trending faults with different dip angles, providing further support for a central stepover region that changed the earthquake rupture trajectory and induced sustained seismicity. 

One important feature of the Greenland Ice Sheet (GrIS) change is its strong seasonal fluctuation. Taking advantage of deployed seismographic stations in Greenland, we apply cross‐component auto‐correlation of seismic ambient noise to measure in‐situ near surface relative velocity change (dv/v) in different regions of Greenland. Our results demonstrate thatdv/vmeasurements for most stations have less than 3 months lag times in comparison to the surface mass change. These various lag times may provide us constraints for the thickness of the subglacial till layer over different regions in Greenland. Moreover, in southwest Greenland, we observe a change in the long‐term trend ofdv/vfor three stations, which might be consistent with the mass change rate (dM/dt) due to the “2012–2013 warm‐cold transition.” These observations suggest that seismic noise auto‐correlation technique may be used to monitor both seasonal and long‐term changes of the GrIS.

 

Significant imbalances in terrestrial water storage (TWS) and severe drought have been observed around the world as a consequence of climate changes. Improving our ability to monitor TWS and drought is critical for water‐resource management and water‐deficit estimation. We use continuous seismic ambient noise to monitor temporal evolution of near‐surface seismic velocity,dv/v, in central Oklahoma from 2013 to 2022. The deriveddv/vis found to be negatively correlated with gravitational measurements and groundwater depths, showing the impact of groundwater storage on seismic velocities. The hydrological effects involving droughts and recharge of groundwater occur on a multi‐year time scale and dominate the overall derived velocity changes. The thermoelastic response to atmospheric temperature variations occurs primarily on a yearly timescale and dominates the superposed seasonal velocity changes in this study. The occurrences of droughts appear simultaneously with local peaks ofdv/v, demonstrating the sensitivity of near‐surface seismic velocities to droughts.