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The Imperial Valley is a seismically active basin occupying the southern end of the Salton trough, an area of rapid extension, high heat flow, and abundant geothermal resources. This report describes an ongoing large-scale distributed acoustic sensing (DAS) recording study acquiring high-density seismic data on an array between Calipatria and Imperial, California. This 27 km array, operating on dark fiber since 9 November 2020, has recorded a wealth of local seismic events as well as ambient noise. The goal of the broader Imperial Valley Dark Fiber project is to evaluate passive DAS as a tool for geothermal exploration and monitoring. This report is intended to provide installation information, noise characteristics, and metadata for future studies utilizing the data set. Because of the relatively small number of basin-scale DAS studies that have been conducted to date, we also provide a range of lessons learned during the deployment to assist future researchers exploring this acquisition strategy.

Identifying and monitoring the reactivation of faults and opening of fractures affecting low permeability, sealing formations in natural underground storage complexes such as Carbon Capture and Storage projects and Nuclear Waste repositories is essential to ensure storage integrity and containment. Although passive seismic monitoring can be effective for detecting induced failure, stress accumulation and fault reactivation can occur aseismically in clay-rich formations, preventing early failure to be recognized. Here, we investigate the potential of applying strain monitoring with fiber-optics sensing technologies to assess in-situ changing stress conditions at high spatial and temporal resolution.We present results of fiber-optic sensing monitoring during the FS-B experiment, a controlled activation of a fault zone affecting the Opalinus Clay Formation in the Mont Terri underground Laboratory (Switzerland). Six constant flowrate water injections induced the hydraulic opening of the fault. A hydraulic connection between the injector and a monitoring borehole occurred, developing a flow path sub-parallel to the fault strike. A 2 km long fiber-optic cable looped through 10 monitoring boreholes surrounding and crossing the fault zone was used for distributed acoustic and strain sensing (DAS and DSS) before, during and after injection. Continuous low-frequency (< 1 Hz) DAS data reveals mechanical strain associated with fault reactivation. Increasing extensional strain is recorded near the point of injection and near the newly formed hydraulic flow path, reaching a value of ~150 μɛ. Post-activation residual strain of ~60 μɛ suggests irreversible fault zone deformation. Smaller strain changes are recorded above and below the high pressure flow path, suggesting a mechanically disturbed zone larger than the leakage zone. Low-frequency DAS data are consistent with co-located DSS strain data, local, 3D displacement measurements of fault movements and P-wave velocity anomalies derived from Continuous Active Source Seismic Monitoring (CASSM). Our results are promising and demonstrate the potential of fiber-optic sensing as a powerful tool for monitoring spatio-temporal evolution of fault reactivation processes and leakage in clay formations induced by fluid pressurization.

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.

We assess the performance of the joint inversion of receiver functions (RF) and surface-wave dispersion in the characterization of the sedimentary package comprising the Parnaíba basin. This procedure is routinely utilized in passive-source crustal studies to retrieve S-wave velocity variations with depth, and has seldom been used with higher-frequency datasets to investigate fine sedimentary structure. The Parnaíba basin is a Paleozoic cratonic basin composed of five supersequences, accumulating ∼3.5 km of sedimentary rocks interbedded by Late Cretaceous diabase sills. The dataset used for this research was acquired between 2015 and 2017 through deployment of 10 short-period and one broadband seismic stations distributed along an approximately 100-kilometer-long linear array in the center of the basin. The deployment was carried out under the Parnaíba Basin Analysis Project, a multi-institutional and multidisciplinary effort funded by BP Energy do Brasil. High-frequency RFs (f

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.

Author(s): Rodriguez Tribaldos, V; Ajo-Franklin, JB | Abstract: 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, ∼6nkm 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 ∼1.5nm. In contrast, regions away (2.5nkm) 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 10–30nm. 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.

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. Distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of these data at very high spatial and temporal resolution for tens of kilometers. One major challenge when utilizing the DAS technology is the large volume of data that is produced, thereby presenting a significant Big Data challenge to find regions of useful energy. In this work, we present a highly scalable and efficient approach to process real, complex DAS data by integrating physics knowledge acquired during a data exploration phase followed by deep supervised learning to identify "useful" coherent surface waves generated by anthropogenic activity, a class of seismic waves that is abundant on these recordings and is useful for geophysical imaging. Data exploration and training were done on 130~Gigabytes (GB) of DAS measurements. Using parallel computing, we were able to do inference on an additional 170~GB of data (or the equivalent of 10 days' worth of recordings) in less than 30 minutes. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors., Accepted at the IEEE BigData 2020 conference

Recently developed distributed acoustic sensing (DAS) technologies convert fiber-optic cables into large arrays of subsurface sensors, enabling a variety of applications including earthquake detection and environmental characterization. However, DAS systems produce voluminous datasets sampled at high spatial-temporal resolution and consequently, discovering useful geophysical knowledge within these large-scale data becomes a nearly impossible task for geophysicists. It is appealing to use supercomputers for DAS data analysis, as modern supercomputers are capable of performing over a hundred quadrillion FLOPS operations and have access to exabytes of storage space. Unfortunately, the majority of geophysical data processing libraries are not geared towards these supercomputer environments. This paper introduces a parallel DAS Data Storage and Analysis (DASSA) framework to enable easy-to-use and parallel DAS data analysis on modern supercomputers. DASSA uses a hybrid (i.e., MPI and OpenMP) data analysis execution engine that supports a user-defined function (UDF) interface for various operations and automatically parallelizes them for supercomputer execution. DASSA also provides novel data storage and access strategies, such as communication-avoiding parallel I/O, to reduce the cost of retrieving large DAS data for analysis. Compared with existing data analysis pipelines used by the geophysical community, DASSA is 16× faster and can efficiently scale up to 1456 computing nodes with 11648 CPU cores.

Cratonic sedimentary basins are underlain by thick lithosphere and subside over hundreds of millions of years. The driving mechanism for these basins remains enigmatic due to the paucity of crustal-scale constraints. To address this shortcoming, an integrative study of the Parnaiba cratonic basin of NE Brazil is underway. We summarize field observations, well-log information, seismic reflection profiles and subsidence analyses with the objective of shedding light on possible basin-forming mechanisms. Lithological descriptions and borehole logs reveal a stratigraphic succession that consists of interbedded shallow marine and continental deposits. On seismic reflection profiles, this succession is divided into packages of relatively undisturbed reflections bounded by basin-wide disconformities. These disconformities are manifest as bright, rugose reflections that probably represent buried palaeo-landscapes. Backstripped and water-loaded subsidence curves calculated from boreholes distributed throughout the basin show that tectonic subsidence decreases exponentially over c. 350 Ma. A simple model suggests that this subsidence pattern agrees with a thermal time constant of 70–100 Ma, placing a significant constraint on the likely basin-forming mechanism. This background trend is punctuated by minor increases in subsidence that follow basin-wide erosional disconformities. These departures could be indicative of transient epeirogenic uplift events caused by changing patterns of dynamic topography.

Several recent studies have demonstrated that Distributed Acoustic Sensing (DAS) can utilize existing subsurface telecom fiber (i.e. dark fiber) for high quality seismic measurements. Researchers to date have shown that this sensing combination, coupled to ambient noise interferometry techniques, can effectively image the shallow subsurface (< 30 m) using vehicle and infrastructure noise (f = 8 - 30 Hz). We present the first long-offset surface wave inversion study targeting deeper (⁓ 500 m) structure using DAS and dark fiber. This study utilizes a previously acquired dataset collected on a 23 km fiber section between West Sacramento and Woodland, CA, part of the DOE Energy Science Network (ESnet). By targeting noise generated by a co-linear rail line, broadband and rich in low frequencies (down to f=1 Hz), and long array offsets, we generate high-quality interferometric gathers suitable for inversion. Subsequent surface wave inversions using a multimode Monte Carlo (MC) sampling algorithm are consistent with geology and available confirmatory datasets derived from co-located sonic logs. The relatively sparse confirmatory data demonstrates, by comparison, the utility of the high spatial sampling provided by DAS. These results open the door to larger regional DAS studies targeting deeper targets but with resolutions higher than those afforded by the use of persistent low frequency (f

We present one of the first case studies demonstrating the use of distributed acoustic sensing deployed on regional unlit fiber-optic telecommunication infrastructure (dark fiber) for broadband seismic monitoring of both near-surface soil properties and earthquake seismology. We recorded 7 months of passive seismic data on a 27 km section of dark fiber stretching from West Sacramento, CA to Woodland, CA, densely sampled at 2 m spacing. This dataset was processed to extract surface wave velocity information using ambient noise interferometry techniques; the resulting VS profiles were used to map both shallow structural profiles and groundwater depth, thus demonstrating that basin-scale variations in hydrological state could be resolved using this technique. The same array was utilized for detection of regional and teleseismic earthquakes and evaluated for long period response using records from the M8.1 Chiapas, Mexico 2017, Sep 8th event. The combination of these two sets of observations conclusively demonstrates that regionally extensive fiber-optic networks can effectively be utilized for a host of geoscience observation tasks at a combination of scale and resolution previously inaccessible.

This contribution analyses a complex sinkhole cluster buried by urban elements in the mantled evaporite karst of Zaragoza city, NE Spain, where active subsidence has caused significant economic losses (~ 0.3 million Euro). The investigation, conducted after the development of the area, has involved the application of multiple surface and subsurface techniques. A detailed map of modern surface deformation indicates two active coalescing sinkholes, whereas the interpretation of old aerial photographs reveals the presence of two additional dormant sinkholes beneath human structures that might reactivate in the near future. DInSAR (Differential Interferometry Synthetic Aperture Radar) displacement data have limited spatial coverage mainly due to high subsidence rates and surface changes (re-pavement), and the Electrical Resistivity Tomography (ERT) and trenching investigations were severely restricted by the presence of urban elements. Nonetheless, the three techniques consistently indicate that the area affected by subsidence is larger than that defined by surface deformation features. The performance of the Ground Penetrating Radar (GPR) technique was adversely affected by the presence of highly conductive and massive anthropogenic deposits, but some profiles reveal that subsidence in the central sector of one of the sinkholes is mainly accommodated by sagging. The stratigraphic and structural relationships observed in a trench dug across the topographic margin of one of the sinkholes may be alternatively interpreted by three collapse events of around 0.6 m that occurred after 290 yr BP, or by progressive fault displacement combined with episodic anthropogenic excavation and fill. Average subsidence rates of > 6.6 mm/yr and 40 mm/yr have been calculated using stratigraphic markers dated by the radiocarbon method and historical information, respectively. This case study illustrates the need of conducting thorough investigations in sinkhole areas during the pre-planning stage including a geomorphic approach. A sound geomorphic model is essential for the proper design of the site investigation, the interpretation of the data and application of effective mitigation measures. Once sinkhole areas are developed, urban elements largely restrict the applicability and performance of multiple techniques, substantially decreasing the feasibility and benefit/effort ratio of the investigations.