Groundwater is a critical resource for human activities worldwide, and a vital component of many natural ecosystems. However, the state and dynamics of water-bearing aquifers remain uncertain, mostly due to the paucity of subsurface data at high spatial and temporal resolution. Here, we show that analysis of infrastructure-generated ambient seismic noise acquired on distributed acoustic sensing (DAS) arrays has potential as a tool to track variations in seismic velocities (dv/v) caused by groundwater level fluctuations. We analyze 5 months of ambient noise acquired along an unused, 23 km-long telecommunication fiber-optic cable in the Sacramento Valley, CA, a so-called “dark fiber." Three array subsections, ∼6 km apart, are processed and the stretching technique is applied to retrieve daily dv/v beneath each location. Near the Sacramento river, dv/v variations in the order of 2%–3% correlate with precipitation events and fluctuations in river stage of ∼1.5 m. In contrast, regions away (2.5 km) from the river do not experience large dv/v variations. These observations reveal short-scale spatial variability in aquifer dynamics captured by this approach. Dispersion analysis and surface wave inversion of noise gathers reveal that seismic velocity perturbations occur at depths of 10–30 m. Rock physics modeling confirms that observed dv/v are linked to pore pressure changes at these depths, caused by groundwater table fluctuations. Our results suggest that DAS combined with ambient noise interferometry provides a means of tracking aquifer dynamics at high spatial and temporal resolutions at local to regional scales, relevant for effective groundwater resource management.

Near-surface seismic structure, particularly the shear wave velocity (Vs), can strongly affect local site response, and should be accurately estimated for ground motion prediction during seismic hazard assessment. The Imperial Valley (California), occupying the southern end of the Salton Trough, is a seismically active basin with thick surficial lacustrine sedimentary deposits. In this study, we utilize ambient noise records and local earthquake events for high-resolution near-surface characterization and site effect estimation with an unlit fiber-optic telecommunication infrastructure (dark fiber) in Imperial Valley by using the distributed acoustic sensing (DAS) technique. We apply ambient noise interferometry to retrieve coherent surface waves from DAS records, and evaluate performances of three different surface wave methods on DAS ambient noise dispersion imaging. We develop a quality control workflow to improve the dispersion measurement of noisy portions of the DAS data set by using a data selection strategy. Using the joint inversion of both the fundamental mode and higher overtones of Rayleigh waves, a high resolution two-dimensional (2D) Vs structure down to 70 m depth is obtained. We successfully achieve an improved Vs30 (the time-averaged shear-wave velocity in the top 30 m) model with higher spatial-resolution and reliability compared to the existing community model for the area. We also explore the potential for utilizing DAS earthquake events for site amplification estimation. The preliminary results reveal a clear anti-correlation between the approximated site response and the Vs30 profile. Our results indicate the potential utility of DAS deployed on dark fiber for near-surface characterization in appropriate contexts.

Seismic tube waves, produced by flexure of the well boundary, pressure changes in the fluid in the well, and deformation of the material immediately surrounding the well, are particularly sensitive to variations in the state of the well. We evaluate a direct approach for generating and observing tube waves as a means of detecting well damage. While we find that it can be difficult to reliably excite observable tube waves without a very strong surface source, time-frequency techniques can be employed to increase the detectability of tube wave reflections. New technologies, particularly distributed acoustic sensing, hold great promise for evaluating well integrity by monitoring tube waves, temperature changes, and seismic noise due to well deformation and fluid leakage.

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 unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ∼28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the 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, which 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 geological settings.

Distributed Acoustic Sensing (DAS) is an emerging sensing technology that records the strain-rate along fiber optic cables at high spatial and temporal resolution. This technique is becoming a popular tool in seismology, hydrology, and other subsurface monitoring applications. However, due to the large coverage (10’s of km) and high density of measurements (1m spacing at 100’s of Hz), a DAS installation could produce terabytes of data records per day. Because many DAS instruments are deployed in remote locations, this large data size poses significant challenges to its transfer and storage. In this paper, we explore lossless compression methods to reduce the storage requirement in both real-time and post-hoc scenarios. We propose a two-stage compression method to improve the compression ratio and compression speed. This two-stage compression method could reduce the storage requirement by 40%, which is 20% more than other lossless methods, such as ZSTD. We demonstrate that the compression method could complete its operation well before the DAS instrument needs to output the next file, making it suitable for real-time DAS acquisition. We also implement a parallel compression method for a post-hoc scenario and demonstrate that our method could effectively utilize a parallel computer. With 256 CPU cores, our parallel compression method achieves the speed of 26GB/second.

It is crucial to ensure the safety and integrity of underground gas storage (UGS) infrastructure for energy reliability in California, and many other places around the world. To address the risk management need in UGS industry, we take advantage of recent advances in downhole fiber optic monitoring and coupled well-reservoir simulation to provide unprecedented understanding of gas flow in wells at UGS sites. We have combined advanced monitoring and simulation of UGS operations into a decision-support system called the Integrated Risk Management and Decision Support System (IRMDSS). The IRMDSS framework includes three components: (i) mechanistic models, (ii) continuous and frequent monitoring data, and (iii) a supervisory interface for performing analyses using the models and monitoring data. The goal of the IRMDSS is to equip UGS operators with real-time monitoring data and simulation tools that can alert them to potential failures, detect early leakage, and support mitigation decision-making to prevent otherwise larger failures. We demonstrate an application of the IRMDSS by analyzing the temperature and pressure response to a hypothetical leak. Through a review of distributed temperature sensing (DTS) data collected at an operating UGS facility we show that DTS can uniquely and precisely identify the depth of the gas-water-contact in the well annulus, and that DTS can provide an early warning signal of upward gas flow as would occur in a well blowout scenario. When combined with modeling analysis, a rough leak rate can be roughly estimated to understand the severity of the leakage conditions and to support the mitigation decision needed.

We conducted a time-lapse seismic experiment utilizing automated active seismic source and sensor arrays to monitor a reactivated fault within the Opalinus clay formation at the Mont Terri Rock Laboratory (Switzerland), an analog caprock for geologic carbon storage. A series of six brine injections were conducted into the so-called Main Fault to reactivate it. Seismic instrumentation in five monitoring boreholes on either side of the fault was used to continuously probe changes in P-wave travel-times associated with fault displacement and leakage. We performed time-lapse travel-time tomography on five hundred sequential data sets; this revealed a zone of decreased P-wave velocity, up to 16 m/s, during each injection cycle, followed by a velocity increase during shut-in. These results demonstrate varying elastic property perturbations, both spatially and temporally, along the fault plane during reactivation. We then interpreted these velocity changes in terms of fault dilation induced by pressurized fluids along the fault.