Ambient Noise Interferometry Research Articles

Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System

AbstractEnhanced geothermal systems (EGS) offer a sustainable energy source but face challenges in accurately locating microearthquakes induced during reservoir stimulation. Locating these microearthquakes provides reliable feedback on the stimulation progress. Current deep learning methods for locating earthquakes require extensive data sets for training, which is problematic as detected microearthquakes are often limited. To address the scarcity of training data, we propose a practical workflow using probabilistic multilayer perceptron (PMLP) which predicts microearthquake locations from cross‐correlation time lags in waveforms. Utilizing a 3D velocity model of Newberry site derived from ambient noise interferometry, we generate numerous synthetic microearthquakes and 3D acoustic waveforms for PMLP training. Accurate synthetic tests prompt us to apply the trained network to the 2012 and 2014 stimulation field waveforms. To enhance the accuracy of source localization, we carefully handpick the P‐arrival times. Predictions on the 2012 stimulation data set show major microseismic activity at depths of 0.5–1.2 km, correlating with a known casing leakage scenario. In the 2014 data set, the majority of predictions concentrate at 2.0–2.9 km depths, consistent with results obtained from conventional physics‐based inversion, and align with the presence of natural fractures from 2.0 to 2.7 km. We validate our findings by comparing the synthetic and field picks, demonstrating a satisfactory match for the first arrivals. By combining the benefits of quick inference speeds and accurate location predictions, we demonstrate the feasibility of using realistic synthetic data set to locate microseismicity for EGS monitoring.

Frequency‐Bessel Transform Method for Multimodal Dispersion Measurement of Surface Waves From Distributed Acoustic Sensing Data

AbstractThe array‐based frequency‐Bessel transform method has been demonstrated to effectively extract dispersion curves of higher‐mode surface waves from the empirical Green's functions (EGFs) of displacement fields reconstructed by ambient noise interferometry. Distributed acoustic sensing (DAS), a novel dense array observation technique, has been widely implemented in surface wave imaging to estimate subsurface velocity structure in practice. However, there is still no clear understanding in theory about how to accurately extract surface‐wave dispersion curves directly from DAS strain (or strain rate) data. To address this, we extend the frequency‐Bessel transform method by deriving Green’s functions (GFs) for horizontal strain fields, making it applicable to DAS data. First, we test its performance using synthetic GFs and verify the correctness of extracted dispersion spectrograms with theoretical results. Then, we apply it to three field DAS ambient‐noise data sets, two recorded on land and one in the seabed. The reliability and advantages of the method are confirmed by comparing results with the widely used phase shift method. The results demonstrate that our extended frequency‐Bessel transform method is reliable and can provide more abundant and higher‐quality dispersion information of surface waves. Moreover, our method is also adaptable for active‐source DAS data with simple modifications to the derived transform formulas. We also find that the gauge length in the DAS system significantly impacts the polarity and value of extracted dispersion energy. Overall, our study provides a theoretical framework and practical tool for multimodal surface wave dispersion measurement using DAS data.

Sustained Co‐Eruptive Increase in Seismic Velocity Below Great Sitkin Volcano Due To Magma Extrusion

AbstractVolcanic eruptions carry essential information on the dynamics of volcanic systems. Studies have documented variable eruption styles and eruptive surface deformation. However, co‐eruptive subsurface structural changes remain poorly understood. Here we characterize the seismic velocity changes from July 2019 to July 2023 at Great Sitkin Volcano in the central Aleutian volcanic arc, using single‐station ambient noise interferometry at five three‐component seismic stations. Coincident with the lava effusion since late July 2021, about two months after the explosive eruption on 26 May 2021, we observe a sustained velocity increase, most prominently to the northwest of the caldera. We attribute this velocity increase to the structural changes with magma extrusion, with the spatial variation controlled by the geometry of the magma system or the property of shallow volcaniclastics. Our findings offer insights into understanding co‐eruptive structural modifications at active volcanoes.

Characterizing South Pole Firn Structure With Fiber Optic Sensing

AbstractThe firn layer covers 98% of Antarctica's ice sheets, protecting underlying glacial ice from the external environment. Accurate measurement of firn properties is essential for assessing cryosphere mass balance and climate change impacts. Characterizing firn structure through core sampling is expensive and logistically challenging. Seismic surveys, which translate seismic velocities into firn densities, offer an efficient alternative. This study employs Distributed Acoustic Sensing technology to transform an existing fiber‐optic cable near the South Pole into a multichannel, low‐maintenance, continuously interrogated seismic array. The data resolve 16 seismic wave propagation modes at frequencies up to 100 Hz that constrain P and S wave velocities as functions of depth. Using co‐located geophones for ambient noise interferometry, we resolve very weak radial anisotropy. Leveraging nearby SPICEcore firn density data, we find prior empirical density‐velocity relationships underestimate firn air content by over 15%. We present a new empirical relationship for the South Pole region.

Regional noise source location based on the time delays between station pairs from ambient noise interferometry

Identifying the location of a potential noise source assists in understanding the characteristics of the seismic or volcanic activity and provides valuable information for hazard assessment. Unlike the conventional waveform-based techniques that rebuild the source energy into the possible source region, we apply a simplified method to determine the absolute location of the noise source based on the station-pair time-delays from ambient noise interferometry. Synthetic tests demonstrate the robustness of the method and the locating precision is mainly influenced by the signal-to-noise ratio of the synthetic waveforms, and the higher frequency bandwidth source signals are more likely to result in accurate detection of the source. An application at the Central Tien Shan indicates that our method is capable of locating the known virtual source from the empirical Green’s functions. Furthermore, assuming a surface wave velocity, the depth of the source can be generally recovered from ambient noise interferometry in a simplified 3-D homogeneous model. The new method sheds light on applications of ambient noise interferometry for locating potential sources, making it suitable for detecting time-dependent behavior.

Urban subsurface exploration improved by denoising of virtual shot gathers from distributed acoustic sensing ambient noise

SUMMARY Ambient noise tomography on the basis of distributed acoustic sensing (DAS) deployed on existing telecommunication networks provides an opportunity to image the urban subsurface at regional scales and high-resolution. This capability has important implications in the assessment of the urban subsurface’s potential for sustainable and safe utilization, such as geothermal development. However, extracting coherent seismic signals from the DAS ambient wavefield in urban environments at low cost remains a challenge. One obstacle is the presence of complex sources of noise in urban environments, which may not be homogeneously distributed. Consequently, long recordings are required for the calculation of high-quality virtual shot gathers, which necessitates significant time and computational cost. In this paper, we present the analysis of 15 d of DAS data recorded on a pre-existing fibre optic cable (dark fibres), running along an 11-km-long major road in urban Berlin (Germany), hosting heavy traffic including vehicles and trains. To retrieve virtual shot gathers, we apply interferometric analysis based on the cross-correlation approach where we exclude low-quality virtual shot gathers to increase the signal-to-noise ratio of the stacked gathers. Moreover, we modify the conventional ambient noise interferometry workflow by incorporating a coherence-based enhancement approach designed for wavefield data recorded with large-N arrays. We then conduct multichannel analysis of surface waves to retrieve 1-D velocity models for two exemplary fibre subsegments, and compare the results of the conventional and modified workflows. The resulting 1-D velocity models correspond well with available lithology information. The modified workflow yields improved dispersion spectra, particularly in the low-frequency band (<1 Hz) of the signal. This leads to an increased investigation depth along with lower uncertainties in the inversion result. Additionally, these improved results were achieved using significantly less data than required using conventional approaches, thus opening the opportunity for shortening required acquisition times and accordingly lowering costs.

Using Vehicle‐Induced DAS Signals for Near‐Surface Characterization With High Spatiotemporal Resolution

AbstractVehicle‐induced seismic waves, generated as vehicles traverse the ground surface, carry valuable information for imaging the underlying near‐surface structure. These waves propagate differently in the subsurface depending on soil properties at various spatial locations. By leveraging wave propagation characteristics, such as surface‐wave velocity and attenuation, this study presents a novel method for near‐surface monitoring. Our method employs passing vehicles as active, non‐dedicated seismic sources and leverages pre‐existing telecommunication fibers as large‐scale and cost‐effective roadside sensors empowered by Distributed Acoustic Sensing (DAS) technology. A specialized Kalman filter algorithm is integrated for automated DAS‐based traffic monitoring to accurately determine vehicles' location and speed. Then, our approach uniquely leverages vehicle trajectories to isolate space‐time windows containing high‐quality surface waves. With known vehicle (i.e., seismic source) locations, we can effectively mitigate artifacts associated with suboptimal distribution of sources in conventional ambient noise interferometry. Compared to ambient noise interferometry, our approach enables the synthesis of virtual shot gathers with a high signal‐to‐noise ratio and spatiotemporal resolution at reduced computational costs. We validate the effectiveness of our method using the Stanford DAS‐2 array, with a focus on capturing spatial heterogeneity and monitoring temporal variations in soil seismic properties during rainfall events. Specifically, in non‐built‐up areas, we observed an evident decrease in phase velocity and group velocity and an increase in attenuation due to the rainfall. Our findings illustrate our method's sensitivity and resolution in discerning variations across different spatial locations and demonstrate that our method is a promising advancement for high‐resolution near‐surface imaging in urban settings.

Using time-lapse seismic velocity changes to monitor the Domo de San Pedro Geothermal field, Mexico

The Domo de San Pedro Geothermal field, located in the westernmost part of the trans-Mexican volcanic belt, is part of a silicic volcanic complex. Geothermal energy is produced since 2015 from 9 wells as deep as 3 km. We monitor geothermal operations using a continuous seismic dataset from a temporary network of 20 broadband stations from March 2021 to January 2022. The well logs describing the geothermal operations allow us to investigate the seismic response of the geothermal field to fluid extraction and re-injection across the geothermal wells. We produced a catalog of 217 local earthquakes and derive an optimized 1D velocity model for the geothermal field between 1.2 m above the sea level (masl) and 12 km depth. Apparent velocity changes (dv/v) are measured using seismic ambient noise interferometry from the coda of noise cross-correlations. Next, we identify and locate subsurface velocity anomalies, combining the methods of station collapse sensitivity and ambient noise imaging applied to the continuous seismic noise records. We distinguish three periods of seismic activity where prominent changes in velocity coincide with the re-injection of fluids and significant variation in the production rate of the geothermal wells. We determine the distribution of these velocity transients inside the geothermal field and propose that they are associated with variations in reservoir fluid content and effective stress changes beneath the volcanic system and are responsible for induced microseismicity. Our study shows that implementing dv/v quantification as an additional tool for geothermal seismic monitoring can lead to an improved understanding of the behavior of the geothermal system over time and ultimately de-risk geothermal operations.

Rayleigh wave attenuation tomography based on ambient noise interferometry: methods and an application to Northeast China

SUMMARY Although ambient noise interferometry has been extensively utilized for seismic velocity tomography, its application in retrieving attenuation remains limited. This study presents a comprehensive workflow for extracting Rayleigh wave amplitude and attenuation from ambient noise, which consists of three phases: (1) retrieval of empirical Green's functions (EGFs), (2) selection and correction of amplitude measurements and (3) inversion of attenuation, site amplification and noise intensity terms. Throughout these processes, an ‘asynchronous’ temporal flattening method is used to generate high-quality EGFs while preserving relative amplitudes between stations. Additionally, a novel ‘t-symmetry’ criterion is proposed for data selection along with the signal-to-noise ratio. Furthermore, 2-D sensitivity kernels are utilized to estimate the focusing/defocusing effect, which is then corrected in amplitude measurements. These procedures are designed to deliver reliable attenuation measurements while maintaining flexibility and automation. To validate the effectiveness of the proposed noise-based attenuation tomography approach, we apply it to a linear array, NCISP-6, located in NE China. The obtained results correlate reasonably well with known geological structures. Specifically, at short periods, high attenuation anomalies delineate the location of major sedimentary basins and faults; while at longer periods, a notable rapid increase of attenuation is observed beneath the Moho discontinuity. Given that attenuation measurements are more sensitive to porosity, defect concentration, temperature, melt and volatile ratio than seismic velocities, noise-based attenuation tomography provides important additional constraints for exploring the crustal and upper mantle structures.

Ambient Noise Interferometry Using Ocean Bottom Seismometer Data From Active Source Experiments Conducted in the Southernmost Mariana Trench

AbstractOcean bottom seismometers (OBSs) have been used to detect submarine structural and tectonic information for decades. According to signal source controllability, OBS data have generally been classified into active and passive source data categories. The former mainly focuses on the compressional wave (P‐wave) velocity inversion and always lacks valid information about the shear wave (S‐wave) velocity structure. While the latter provides structural information with limited resolution due to the aperture of the stations. Overcoming the barriers between processing these two data types will allow the reuse of a vast amount of data from active source experiments to explore the submarine S‐wave velocity structural properties. Here, we creatively applied ambient noise interferometry to invert the S‐wave velocity structure using data from active source OBS deployment conducted in the southernmost Mariana subduction zone, which had already been utilized to detect submarine P‐wave velocity structure. Considering the short time duration and relatively low quality of this type of data, a combined method of short‐segment cross‐correlation and selected time‐frequency domain phase‐weighted stacking was adopted to obtain stable cross‐correlation functions, which were subsequently used to invert S‐wave velocity structures. Compared to previous studies using different methods, our result sheds new light on the crust and upper mantle structure of the southernmost Mariana subduction zone. This method could be used to detect more information based on the reutilization of existing active source OBS data.

Exploiting the Potential of Urban DAS Grids: Ambient-Noise Subsurface Imaging Using Joint Rayleigh and Love Waves

Abstract Distributed acoustic sensing (DAS) data become important for seismic monitoring of subsurface structures in urban areas. Different from the previous studies that only focused on Rayleigh waves, we report successful observation and analysis of both Rayleigh and Love waves extracted from ambient-noise interferometry, using orthogonal segments of fiber-optic cables in San Jose, California. Theoretical angular responses of DAS ambient-noise cross correlation, together with numerical experiments, help identify DAS channel pairs expected to record stronger Love waves than Rayleigh waves. Based on these waveforms, we further obtain clear Rayleigh- and Love-wave dispersion maps, including both phase and group velocities, with various channel pair orientations. Finally, we perform a joint inversion of Rayleigh- and Love-wave dispersion curves to obtain depth-dependent subsurface velocity structures of the top 100 m. Our inversion result is consistent with the model from the previous study based on Rayleigh-wave dispersion and horizontal-to-vertical spectral ratio. In addition, the joint inversion of Love and Rayleigh is more robust than that of the independent inversion of either type of wave. Our new study demonstrates the potential of surface-wave analysis on fiber-optic cables with complex geometry, which can further advance the seismic monitoring of urban areas.

Using ocean ambient sound to sense arrival time fluctuations due to temperature

Ambient noise interferometry is a technique that uses coherent ambient sound measured at two separate locations to estimate the Green’s function between the sensors. Specifically, if the ambient sound is diffuse, the cross-correlation between sensors converges to the Green’s function between the sensors. In this talk, we apply the technique of ambient noise interferometry to two Ocean Observatories Initiative hydrophones that are separated by 3.2km and bottom mounted at a depth of 1500 m. It has previously been shown that these hydrophones can passively estimate several multi-path propagation peaks reliably throughout the 8 years that they have been recording ambient sound. We present an algorithm that estimates the arrival time of these peaks using the empirical Green’s function and compare these estimated arrivals to simulated acoustic arrivals. We demonstrate that the arrival times estimated with ambient sound show good agreement with the simulated results and have clear annual fluctuations due to seasonal temperature changes in the water column. We will discuss the future possibilities of extending this work for inversion based oceanographic measurements. [work supported by ONR.]

A High‐Resolution Shear Velocity Model of the Crust and Uppermost Mantle Beneath Westernmost Mediterranean Including Radial Anisotropy

AbstractUsing seismic data from 1,186 stations deployed across the westernmost Mediterranean, we construct a high‐resolution 3‐D radially anisotropic model from a joint inversion of receiver functions and Rayleigh and Love wave dispersions. The Rayleigh and Love data are extracted from both ambient noise interferograms and earthquake waveforms, and a new three‐station ambient noise interferometry method is used to further improve the data coverage. The obtained crustal thickness map reproduces independently the Moho depth maps compiled from previous data, showing crustal roots beneath the Pyrenean‐Cantabrian range and the Gibraltar Arc and thicker crust beneath the Variscan Iberian Massif than in the area affected by Alpine orogenesis. The Vsv crustal model outlines the Iberian Massif as a high shear wave velocity block and shows extremely low velocities in the Gulf of Cadiz and Gibraltar Arc. At mantle levels, a sharp boundary between the Aquitanean Basin and the Massif Central is imaged, with low Vsv beneath the Massif Central probably reflecting a remaining signature of the magma. To the south of Iberia, the geometry of the Alboran slab is captured by the model, while beneath the Atlas Mountains, widespread low Vsv and positive radial anisotropy is observed, favoring the edge‐driven convection model explaining the lithospheric thinning. The most relevant contribution of this work is mapping, for the first time at this scale, the radial anisotropy anomalies at crustal level, with higher values observed in the Alpine Iberia, dominated by present‐day extensional tectonics.

High‐Resolution Near‐Surface Imaging at the Basin Scale Using Dark Fiber and Distributed Acoustic Sensing: Toward Site Effect Estimation in Urban Environments

AbstractNear‐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.

Spatiotemporal evaluation of Rayleigh surface wave estimated from roadside dark fiber DAS array and traffic noise

Seismic imaging and monitoring of the near-surface structure are crucial for the sustainable development of urban areas. However, standard seismic surveys based on cabled or autonomous geophone arrays are expensive and hard to adapt to noisy metropolitan environments. Distributed acoustic sensing (DAS) with pre-existing telecom fiber optic cables, together with seismic ambient noise interferometry, have the potential to fulfill this gap. However, a detailed noise wavefield characterization is needed before retrievingcoherent waves from chaotic noise sources. We analyze local seismic ambient noise by tracking five-month changes in signal-to-noise ratio (SNR) of Rayleigh surface wave estimated from traffic noise recorded by DAS along the straight university campus busy road. We apply the seismic interferometry method to the 800 m long part of the Penn State Fiber-Optic For Environment Sensing (FORESEE) array. We evaluate the 160 virtual shot gathers (VSGs) by determining the SNR using the slant-stack technique. We observe strong SNR variations in time and space. We notice higher SNR for virtual source points close to road obstacles. The spatial noise distribution confirms that noise energy focuses mainly on bumps and utility holes. We also see the destructive impact of precipitation, pedestrian traffic, and traffic along main intersections on VSGs. A similar processing workflow can be applied to various straight roadside fiber optic arrays in metropolitan areas.

Adaptive Coda‐Wave Imaging With Voronoi Tessellation

AbstractReconstructions of Green's functions by ambient noise interferometry enable the imaging of the Earth's subsurface. Coda waves from reconstructed Green's functions can be utilized to perform time‐dependent imaging of processes that vary substantially at daily to monthly time scales in the crust. Time‐lapse Coda‐Wave Imaging (CWI) can detect tiny changes in seismic velocity with high temporal resolution. While previous studies on CWI have mainly focused on the descriptions of coda waves' propagation, little attention has been paid to choosing effective inversion algorithms that are suitable for CWI. Here we address this issue by developing a pragmatic inversion approach based on Voronoi tessellation with mesh cells adapted to coda‐wave sensitivity kernels. Using seismic stations in Central California, we present both synthetic and real data imaging to demonstrate that this approach stabilizes the inversion, is computationally efficient, and provides spatially adaptive resolution. We further propose a heuristic approach for a quantitative assessment of spatial resolution based on multi‐scale checkerboard tests.

Spatio-temporal velocity variations observed during the pre-eruptive episode of La Palma 2021 eruption inferred from ambient noise interferometry

On Sept. 19th, 2021, a volcanic eruption began on the island of La Palma (Canary Islands, Spain). The pre-eruptive episode was characterized by seismicity and ground deformation that started only 9.5 days before the eruption. In this study, we applied seismic interferometry to the data recorded by six broadband seismic stations, allowing us to estimate velocity variations during the weeks preceding the eruption. About 9.5 days before the eruption, we observed a reduction in the seismic velocities is registered next to the eruptive centers that opened later. Furthermore, this zone overlaps with the epicenters of a cluster of volcano-tectonic earthquakes located at shallow depth (< 4 km) and detached from the main cluster of deeper seismicity. We interpret the decrease in seismic velocities and the occurrence of such a shallow earthquake cluster as the effect of hydrothermal fluid released by the ascending magma batch and reaching the surface faster than the magma itself.

Processing Ambient Noise Data Using Phase Cross‐Correlation and Application Toward Understanding Spatiotemporal Environmental Effects

AbstractTypical use of ambient noise interferometry focuses on longer period (&gt;1 s) waves for exploration of subsurface structure and other applications, while very shallow structure and some environmental seismology applications may benefit from use of shorter period (&lt;1 s) waves. We explore the potential for short‐period ambient noise interferometry to determine shallow seismic velocity structures by comparing two methodologies, the conventional amplitude‐based cross‐correlation and linear stacking (TCC‐Lin) and a more recently developed phase cross‐correlation and time‐frequency phase‐weighted‐stacking (PCC‐PWS) method with both synthetic and real data collected in a heterogeneous karst aquifer system. Our results suggest that the PCC‐PWS method is more effective in extracting short‐period wave velocities than the TCC‐Lin method, especially when using data collected in regions containing complex shallow structures such as the karst aquifer system investigated here. In addition to the different methodologies for computing the cross correlation functions, we also examine the relative importance of signal‐to‐noise ratio and number of wavelengths propagating between station pairs to determine data/solution quality. We find that the lower number of wavelengths of 3 has the greatest impact on the network‐averaged group velocity curve. Lastly, we test the sensitivity of the number of stacks used to create the final empirical Green's function, and find that the PCC‐PWS method required about half the number of cross‐correlation functions to develop reliable velocity curves compared to the TCC‐Lin method. This is an important advantage of the PCC‐PWS method when available data collection time is limited.

Spatiotemporal characteristics of upper-crust seasonal velocity changes in North–South Seismic Belt and South China block in China

Investigating mechanisms and characteristics of the non-tectonic deformation has crucial implications for evaluating the subsurface stress evolution. In this study, we continuously tracked the seasonal velocity changes of Rayleigh wave at 2–10 s with the ambient noise interferometry and discrete wavelet decomposition, in different regions along the actively deforming North–South Seismic Belt (NSSB) and the stable Guangdong region in the South China block in China. The seasonal velocity changes exhibit strong lateral variations across different regions, with much larger seasonality (annual periodicity) and amplitude along the NSSB than those in the stable Guangdong region. There are also small-scale lateral variations inside of each region and generally larger amplitudes of the seasonal velocity changes at greater depth, but the phases (peaks, mostly in winter or summer) at different depths are consistent. The mechanism of the seasonal changes is most likely the direct loading effect of the precipitation, as indicated by consistency in their phases. However, the amplitudes of the seasonal velocity changes seem not controlled by the amount of precipitation, but are more dependent on the tectonic deformation at the different scales, from the correlation between the amplitude of the velocity change and the absolute strain rate. We demonstrate that the crustal media in the regions with larger tectonic background stress are more sensitive to the seasonal loadings. The new finding also highlights the role of the external seasonal loadings in the modulation of seismic activities and risk evaluations. Keypoints•Seasonal seismic velocity variations are found in different regions in China, likely from direct hydrological loadings of precipitation.•The spatial pattern of the seasonal velocity changes is consistent with the distribution of tectonic deformation at multi-scales.•The amplitude of the seasonal velocity changes is more dependent on the magnitude of local deformation than the amount of precipitation.

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

AbstractThe 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.