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Abstract This study demonstrates a novel nondestructive evaluation (NDE) method that combines geometric phase sensing with cross-correlation processed acoustical responses, enabling the detection of structural anomalies even when the acoustic excitation is stochastic and the source characteristics are variable. Traditional ultrasonic techniques, which depend on impulse responses from known source locations to capture wave transmission behavior, often fail under stochastic excitations due to incoherent phase alignment and unpredictable wave paths. The proposed method applies cross-correlation between a fixed reference site and other sensor locations to refine the acoustic field representation, enabling physically meaningful geometric phase extraction through the dot product of two state vectors representative of the acoustic field in a high-dimensional complex Hilbert space. This allows detection of both excitation-induced field asymmetries and subtle nonlinearities. Experimental validation using laser Doppler vibrometry on a circular IN625 plate demonstrates that this approach preserves excitation-induced field asymmetries while remaining sensitive to structural perturbations such as mass defects. The cross-correlation geometric phase change (CC-Δφ) spectra reveal modal differences across excitation conditions, even under white noise, where geometric phase without cross-correlation (Δφ) remains centered near 90 deg, obscuring structural insights. The method also detects mass-induced effects, showing increased average CC-Δφ compared to the no-mass case under the same excitation condition. These results establish a foundation for a robust, non-contact, source-independent NDE technique, suitable for operation under variable and uncontrolled excitation scenarios. 

Abstract Relative seismic velocity changes are being increasingly used to monitor changes in groundwater. However, it remains challenging to verify its implementation in watersheds without direct groundwater well measurements. In this study, we conduct a 12‐year observation in a watershed of the Yellowstone National Park (YNP). We find that the seasonal fluctuations and long‐term trend of the measured are highly correlated with the estimated baseflow, which serves as a constraint for groundwater changes. We integrate the estimated baseflow into a poroelastic mechanism and conduct two simulations based on pressure diffusion. These simulations closely match with our observed variations. In addition, our analysis suggest that the measured is primarily influenced by hydrologic pressure diffusion rather than surface air temperature. We conclude that the baseflow analysis can further enhance the seismic monitoring of groundwater changes. 

Abstract 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. 

Abstract 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. 

Abstract We leverage ambient seismic noise to implement a novel geometric phase sensing method for investigating the effects of environmental conditions on near‐surface ground properties. The geometric phase, derived from topological acoustics, characterizes the geometry of a wavefield by incorporating cross‐correlation information between seismic sensors. Changes in geometric phase, , are expressed as changes in vectorial orientation, describing the wavefield evolution over time. To demonstrate the method, we designed an end‐to‐end workflow by applying an open access temporal high‐resolution data from a seismic array in southwest Iceland and measured over a 2‐year period. We observe that the seasonal fluctuations of are highly correlated with surface air temperature, reflecting changes in ground properties during the freeze‐thaw cycle. We assess the seasonal stability of the noise source distribution and conduct a numerical test to verify that the seasonal pattern in is minimally affected by shifts in noise source direction. Several advantages of geometric phase measurements, including the elimination of lag window selection and reduced computational costs, suggest their strong effectiveness in monitoring changes in ground properties with time. We suggest that the geometric phase can play a significant role in the future of environmental monitoring. 

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.