Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. This paper details the MCI scheme, evaluates its efficacy through synthetic tests, and applies it to a DAS field study in Imperial Valley, California. Our findings demonstrate a refined, higher-resolution S-wave velocity model, offering new insights into the region’s fault system and emphasizing the necessity of improved spatial resolution in large-scale geophysical studies.

Understanding the initiation and arrest of earthquakes is one of the long-standing challenges of seismology. Here we report on direct observations of borehole displacement by a meter-sized shear rupture induced by pressurization of metamorphic rock at 1.5 km depth. We observed the acceleration of sliding, followed by fast co-seismic slip and transient afterslip. Total displacements were about 7, 5.5 and 9.5 micrometers, respectively for the observed pre-slip, co-seismic slip and afterslip. The observed pre-slip lasted about 0.4 seconds. Co-seismic slip was recorded by the 1 kHz displacement recording and a 12-component array of 3-C accelerometers sampled at 100 kHz. The observed afterslip is consistent with analytical models of arrest in a velocity-strengthening region and subsequent stress relaxation. The observed slip vector agrees with the activation of a bedding plane within the phyllite, which is corroborated by relocated seismic events that were observed during the later stages of the injection experiment.

Quantitative prediction of natural and induced phenomena in fractured rock is one of the great challenges in the Earth and Energy Sciences with far-reaching economic and environmental impacts. Fractures occupy a very small volume of a subsurface formation but often dominate flow, transport and mechanical deformation behavior. They play a central role in CO2 sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and hydrocarbon extraction. These applications require prediction of fracture-dependent quantities of interest such as CO2 leakage rate, hydrocarbon production, radionuclide plume migration, and seismicity; to be useful, these predictions must account for uncertainty inherent in subsurface systems. Here, we review recent advances in fractured rock research that cover field- and laboratory-scale experimentation, numerical simulations, and uncertainty quantification. We discuss how these have greatly improved the fundamental understanding of fractures and one’s ability to predict flow and transport in fractured systems. Dedicated field sites provide quantitative measures of fracture flow that can be used to identify dominant coupled processes and to validate models. Laboratory-scale experiments fill critical knowledge gaps by providing direct observations and measurements of fracture geometry and flow under controlled conditions that cannot be obtained in the field. Physics-based simulation of flow and transport provide a bridge in understanding between controlled simple laboratory experiments and the massively complex field-scale fracture systems. Finally, we review the use of machine learning-based emulators to rapidly investigate different fracture property scenarios and to accelerate physics-based models by orders of magnitude to enable uncertainty quantification and near real-time analysis.

Tomographic imaging based on long-term ambient seismic noise measurements, mainly the phase information from surface waves, has been shown to be a powerful tool for geothermal reservoir imaging and monitoring. In this study, we utilize seismic noise data from a dense nodal array (192 3C nodes within 20km2) over a ultra-short observation period (4.7 days) to reconstruct surface waves and determine the high-resolution (0.2km) three-dimensional (3-D) S wave velocity structure beneath a rural town in Zhejiang, China. We report the advantage of cross-coherence over cross-correlation in suppressing pseudo-arrivals caused by persistent sources. We use ambient noise interferometry to retrieve high quality Rayleigh waves and Love waves. Body waves are also observed on the R-R component interferograms. We apply phase velocity dispersion measurements on both Rayleigh waves and Love waves and automatically pick more than 23,000 dispersion curves by using a Machine Learning technique. 3-D surface wave tomographic results after depth inversion indicate low-velocity anomalies (between -1% and -4%) from the surface to 2km depth in the central area. Combined with the conductive characteristics observed on resistivity profile, the low-velocity anomalies are inferred to be a fluid saturated zone of highly fractured rock. Joint interpretation based on HVSR measurements, and existing temperature and fluid resistivity records observed in a nearby well, suggests the existence of the high-temperature geothermal field through the fracture channel. Strong correlation between HVSR measurements and S wave velocity model sheds light on the potential of extraction of both amplitude and phase information from ambient noise.

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly-sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging utilizing an unlit fiber-optic telecommunication infrastructure (dark fiber). The study utilizes passive DAS data acquired from early November 2020 over a ~28-kilometer section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records, and develop a spatial stacking technique to attenuate effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity (Vs) structure to 3 km depth based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high Vs and low Vp/Vs ratio feature beneath the Brawley geothermal system that we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate contexts.