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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hayward Fault Creep From Coda Wave Interferometry

Understanding creep is essential for assessing seismic hazards along the creeping Hayward Fault, which threatens the San Francisco area. Current 1-D monitoring methods, like creepmeters and theodolites, provide limited measurements with low spatial resolution. InSAR offers higher 2-D spatial sampling but lower temporal resolution. We introduce a novel method using coda wave interferometry (CWI) to monitor creep in 2-D with high resolution. By calculating changes in ambient seismic noise travel times, we relate these measurements to creep offsets, bridging the gap between existing methods. Applied to seismic data from 2021 around the Hayward Fault, our CWI-derived offsets aligned with creepmeter measurements, revealing continuous and episodic creep. A 2-D regionalization identified a spatial boundary between these regimes, corroborating previous findings. This approach could enhance seismic monitoring and improve understanding of fault mechanics and earthquake risks.

Luckie, Thomas William [Sandia National Laboratori↗
DOE OSTI2025

Microseismicity Modulation Due To Changes in Geothermal Production at San Emidio, Nevada, USA

Brief cessations of geothermal production can induce seismicity, a phenomenon that has drawn increasing attention in recent years. Such observations are rare, and the underlying mechanism requires careful analysis. In April 2022, a dense seismic and hydrologic monitoring system was deployed at the San Emidio geothermal field, Nevada, to accompany a planned power plant shutdown. Using the dense seismic array data, we detected and located ∼1,800 microseismic events (MSEs) and developed a high-resolution tomographic P-wave velocity model. We observed substantially increased microseismicity during shutdown. Most MSEs occurred on pre-existing normal faults, which are contained within extremely low-velocity zones that are likely damaged, fluid-filled, and hydraulically connected to nearby production wells. Hydrologic data show rapid fluid pressure increases of <60 kPa following the shutdown. We suggest that the cessation of production rapidly increased fluid pressures along pre-existing fault zones, activating critically stressed fault patches and fractures and producing microseismicity.

Guo, Hao [University of Wisconsin-Madison, WI (Uni↗
DOE OSTI2023

Multi-Scale Seismic Measurements for Site Characterization and CO2 Monitoring in an Enhanced Oil Recovery/Carbon Capture, Utilization, and Sequestration Project, Farnsworth Field, Texas

To address the challenges of climate change, significantly more geologic carbon sequestration projects are beginning. The characterization of the subsurface and the migration of the plume of supercritical carbon dioxide are two elements of carbon sequestration that can be addressed through the use of the available seismic methods in the oil and gas industry. In an enhanced oil recovery site in Farnsworth, TX, we employed three separate seismic techniques. The three-dimensional (3D) surface seismic survey required significant planning, design, and processing, but produces both a better understanding of the subsurface structure and a three-dimensional velocity model, which is essential for the second technique, a timelapse vertical seismic profile, and the third technique, cross-well seismic tomography. The timelapse 3D Vertical Seismic Profile (3D VSP) revealed both significant changes in the reservoir between the second and third surveys and geo-bodies that may represent the extent of the underground carbon dioxide. The asymmetry of the primary geo-body may indicate the preferential migration of the carbon dioxide. The third technique, cross-well seismic tomography, suggested a strong correlation between the well logs and the tomographic velocities, but did not observe changes in the injection interval.

Energy & Fuels↗
DOE OSTI2024

A Frequency Domain Methodology for Quantitative Evaluation of Diffuse Wavefield With Applications to Seismic Imaging

Abstract Ambient Noise Imaging (ANI) of subsurface structures relies on seismic interferometry of diffuse seismic wavefields. However, the lack of effective methods to quantify and identify highly diffuse waves hampers applications of ANI, particularly in evaluating seismic attenuation and monitoring structural changes with high temporal resolution. Conventional ANI approaches require data normalization, which effectively suppresses the non‐diffuse component with large amplitude but also results in significant loss of amplitude and phase information in the continuous seismic records. In this study, we propose a frequency domain method to quantitatively evaluate the degree of diffuseness of seismic wavefields by analyzing their statistical characteristics of modal amplitudes for stationarity and randomness. Tests on synthetic waveform and field nodal records show that the proposed method can effectively distinguish between diffuse and non‐diffuse waveforms for either single‐ or three‐component data. As an application, we identify a 60‐s‐long diffuse coda of a local M 2.2 earthquake recorded by a dense nodal array on the San Jacinto Fault Zone, and successfully extract high‐quality dispersion curve andQ‐value without performing data normalization. These results are consistent with those obtained by conventional methods that assess the correlation between coherency and the Green's function, and by modeling ballistic waves generated by road traffic. Our proposed method can advance the imaging of subsurface velocity and attenuation structures as well as monitoring temporal changes for scientific studies and engineering applications.

Geochemistry & Geophysics↗
DOE OSTI2022

Earthquake Phase Association Using a Bayesian Gaussian Mixture Model

Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual seismic events and play an important role in earthquake monitoring and research. Dense seismic networks and improved phase picking methods produce massive seismic phase datasets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. Here, we present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model with earthquake location, origin time, and magnitude estimation. We treat earthquake phase association as an unsupervised clustering problem in a probabilistic framework, where each earthquake corresponds to a cluster of P and S phases with a hyperbolic moveout of arrival times and a decay of amplitude with distance. We use the multivariate Gaussian distribution to model the collection of phase picks of an event; and the mean of the multivariate Gaussian distribution is given by the predicted arrival time and amplitude from the causative event. We carry out the pick assignment to each earthquake and determine earthquake source parameters (i.e., earthquake location, origin time, and magnitude) under the maximum likelihood criterion using the Expectation-Maximization algorithm. The GaMMA method does not require typical association steps of other algorithms, such as grid-search or supervised training. The results for both synthetic tests and for the 2019 Ridgecrest earthquake sequence show that GaMMA effectively associates phases from a temporally and spatially dense earthquake sequence while producing useful estimates of earthquake location and magnitude.

58 GEOSCIENCES↗
DOE OSTI2024

A Complete Machine-Learning-Based Workflow to Illuminate Earthquake Processes

Under this grant we developed, tested, and made available, machine learning models to improve the tasks in the earthquake monitoring workflow (Figure 1). We implemented these models as a part of an end-to-end workflow for seismic network processing and demonstrated, in a variety of settings, that these methods generalize and that they result in dramatically more comprehensive earthquake catalogs. These catalogs illuminate earthquake processes in detail and to an extent that had previously not been possible, and they do so for both tectonic seismicity and seismicity induced by fluid injection related to unconventional hydrocarbon development. This report summarizes the results from the 15 publications that resulted from this grant. Those contributes are divided into: (1) the development of specific tasks related to monitoring (7 publications), (2) the organization of those tasks into workflows for seismic monitoring (2 publications), and (3) applications of those workflows to data (6 publications).

58 GEOSCIENCES↗
DOE OSTI2025

Prediction and Analysis of Utah FORGE Injection Activities using a Coupled Thermo-hydro-mechanical and Earthquake (THM+E) Modeling Workflow

A coupled thermo-hydro-mechanical (THM) numerical workflow that is capable of modeling seismic slip is critical for the successful development of enhanced geothermal systems (EGS). By integrating key physical processes, this workflow enables accurate simulation of temperature and pressure diffusions, stress changes, and induced seismicity. As a result, it serves as a vital tool for predicting induced seismicity and optimizing reservoir stimulation strategies. The Utah FORGE (Frontier Observatory for Research in Geothermal Energy) project, located near Milford, Utah, is a U.S. Department of Energy initiative aimed at advancing EGS technology. In April 2024, eight new stimulation stages (Stages 3R-10) were conducted in well 16A (injection well) subsequent to the first series of stimulation (Stages 1-3) performed in April, 2022. To monitor the induced seismicity, geophones were deployed in wells 58-32, 56-32, and 78B-32, while fiber optic cables were also installed in wells 16B, 78-32, and 78B-32 to collect microseismic data and detect frac hits Preliminary analyses of microseismic catalogs and fiber optic data suggest that the stimulated fractures in Stages 3R–6 closely align with that generated during Stage 3, indicating that the new stimulations were likely reactivating the previously stimulated fracture. To better understand the underlying process, a comprehensive modeling approach that can accurately capture thermal, hydrological, mechanical, and seismic responses is essential. In this work, we propose and utilize a coupled thermo-hydro-mechanical and earthquake (THM+E) simulation workflow to numerically investigate the stimulation activities on well 16A. The specific objective is to confirm whether the new stimulation stages (Stages 3R–6) reactivated fractures previously stimulated during Stage 3. For this purpose, we perform THM+E simulations individually for Stages 3, 3R, 4, and 5, incorporating the discrete fracture networks (DFNs) created by the plane-fitting technique based on the microseismic catalogs. The simulation workflow consists of two separate models: a THM model and an earthquake model, coupled in a one-way manner. Detailed descriptions of the workflow are provided in Section 3. Simulation results are presented in terms of injection pressure, permeability evolution, and predicted seismic catalogs, which are then compared with field data for further analyses. This report is structured as follows. In Section 2, we present detailed analyses of the field data and propose the hypothesis that the new stimulation stages (Stages 3R–6) were probably reactivating the previously stimulated fractures in Stage 3. In Section 3, we introduce the coupled THM+E workflow and the problem setup to validate our hypothesis, followed by the simulation results for each stage in Section 4. Meanwhile, discussions are included to analyze the model predictions and their comparison with field data. Lastly, we conclude the report and outline future plans in Section 5.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Rayleigh Wave Propagation in the Bighorn Mountains Region, Wyoming

Short-period Rayleigh waves, Rg , provide strong constraints on the depth of shallow seismic events and are of interest for monitoring small explosions. Characterizing the seismic sources that generate Rg requires an understanding of how shallow crustal structure affects Rayleigh wave propagation. Here, in support of these efforts, this study utilizes observed waveforms from small shallow explosions recorded on temporary seismic network deployments in the Bighorn region, Wyoming. We study regional near-surface geology by measuring changes in surface-wave amplitude and polarization during propagation through basins, foothills, and mountains. We develop additional insight by carrying out surface-wave eigenfunction analyses and numerical-wave simulations, which together reproduce many characteristics seen in the observed waveforms. Our results show how sedimentary basins in the Bighorn region allow for amplified prograde-polarized higher-mode and retrograde-polarized fundamental-mode Rayleigh waves, whereas adjacent mountains only support retrograde motion. These different modes provide distinct constraints on the Earth structure and source characteristics, potentially enabling targeted inversions in future studies. Finally, our findings provide insight into Rg propagation through complex near-surface geology, improving our understanding of shallow propagation and source effects that are relevant to explosion monitoring efforts.

58 GEOSCIENCES↗
DOE OSTI2023

Testing and Design of Discriminants for Local Seismic Events Recorded during the Redmond Salt Mine Monitoring Experiment

The Redmond Salt Mine (RSM) Monitoring Experiment in Utah was designed to record seismoacoustic data at distances less than 50 km for algorithm testing and development. During the experiment from October 2017 to July 2019, six broadband seismic stations were operating at a time, with three of them having fixed locations for the duration, whereas the three other stations were moved to different locations every one-and-half to two-and-half months. RSM operations consist of nighttime underground blasting several times per week. The RSM is located in proximity to a belt of active seismicity, allowing direct comparison of natural and anthropogenic sources. Using the recorded data set, we built 1373 events with local magnitude (M L ) of -2.4 and lower to 3.3. For 75 blasts (RMEs) from the Redmond Salt Mine and 206 tectonic earthquakes (EQs), both M L and the coda duration magnitude (MC) are well constrained. We used these events to test and design discriminants that separate the RMEs from the EQs and are effective at local distances. The discriminants consist of M L -M C , low-frequency Sg to high-frequency Sg, Pg/Sg phase-amplitude ratios, and Rg/Sg spectral amplitude ratios, as well as different combinations of two or more of these classifiers. The areas under the receiver operating characteristic curves (AUCs) of 0.92–1.0 for M L -M C , low-frequency Sg to high-frequency Sg, and Rg/Sg indicate that these discriminants are very effective. Conversely, the AUC of only 0.57 for Pg/Sg suggests that this discriminant is only slightly better than a random classifier. Among the effective classifiers, Rg/Sg, shows the lowest likelihood of misclassification (4.3%) for the populations. In conclusion, results of joint discriminant analyses suggest that even the arguably ineffective single classifier, like Pg/Sg in this case, can provide some value when used in combination with others.

58 GEOSCIENCES↗
DOE OSTI2024

DASEventNet: AI‐Based Microseismic Detection on Distributed Acoustic Sensing Data From the Utah FORGE Well 16A (78)‐32 Hydraulic Stimulation

Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

Lin, Jiun-Ting [Lawrence Livermore National Labora↗
DOE OSTI2023

Monitoring Terrestrial Water Storage, Drought and Seasonal Changes in Central Oklahoma With Ambient Seismic Noise

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 derived dv / v is 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 of dv / v , demonstrating the sensitivity of near‐surface seismic velocities to droughts.

58 GEOSCIENCES↗
DOE OSTI2020

Evaluation of the Nanometrics Centaur Digitizer

Sandia National Laboratories has tested and evaluated a new digitizer, the Centaur, manufactured by Nanometrics, Inc. This digitizer is used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as power, sensitivity, self-noise, dynamic range, system noise, modified noise power ration, relative transfer function, analog bandwidth, harmonic distortion, common mode, cross talk, timing tag accuracy and timing drift. The Centaur provides six channels of 24 bit digitization, three of which may be transmitted utilizing CD1.1 protocol.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Kinemetrics Q330M+ Digitizer Evaluation

Sandia National Laboratories has tested and evaluated a new digitizer, the Q330M+, manufactured by Quanterra, a division of Kinemetrics Inc. This digitizer is used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as sensitivity, input impedance, power consumption, self noise, dynamic range, system noise, relative transfer function, analog bandwidth, modified noise power ratio, harmonic distortion, common mode, cross talk, timing tag accuracy and timing drift. The Q330M+ provides six channels of 24 bit digitization, all of which may be transmitted utilizing CD1.1 protocol, at multiple sample rates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Guralp Affinity Digitizer Evaluation

Sandia National Laboratories has tested and evaluated a new digitizer, the Affinity, manufactured by Guralp Systems. This digitizer is used to record sensor output for seismic and infrasound monitoring applications. The purpose of the digitizer evaluation was to measure the performance characteristics in such areas as sensitivity, power, self-noise, dynamic range, system noise, relative transfer function, modified noise power ratio, analog bandwidth, harmonic distortion, common mode, cross talk, timing tag accuracy and timing drift. The Affinity provides eight, rather the typical six, channels of 24 bit high sample rate digitization, all of which may be transmitted utilizing the CD1.1 protocol, at multiple sample rates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Comparison of DTRA/NACT Next Generation Digitizer Evaluations

Sandia National Laboratories has tested and evaluated three digitizers, the Affinity, manufactured by Guralp Systems, the Centaur, manufactured by Nanometrics, and the Q330M+, manufactured by Quanterra, a division of Kinemetrics. These digitizers are used to record sensor output for seismic and infrasound monitoring applications. The purpose of this document is to highlight various results and observations collected during comprehensive evaluations conducted on each unit.

42 ENGINEERING↗
DOE OSTI2021

Intelligent Monitoring System for Real-time Geologic Storage, Optimization, and Reservoir Management

The objective of the subtask was to develop a near-real-time monitoring system for seismic data at the Decatur, IL, geologic carbon sequestration (GCS) site and specifically include fiber-optic cable derived distributed acoustic signal (DAS) data in the process. Owing to the large volumes of data, we opted to utilize existing deep borehole conventional seismic sensors for detection and pull DAS and shallow borehole seismic data once a detection has been made. Unfortunately, the horizontal fiber-optic cables did not yield microseismic signals for use in locating events near the GCS site. Various stacking and filtering approaches were tested without any coherent detection becoming apparent. We attribute the insensitivity to local microseismic events to a lack of coupling in downhole, vertical cable and the non-ideal alignment of the horizontal fiber-optic cable to the vertically polarized seismic energy. Despite the inability to detect and utilize the DAS data from the fiber-optic cables, we developed a general processing framework that enables easy adaptation for future deployment of fiber-optic cables with better suited alignment at Decatur or elsewhere.

Kaven, J. Ole↗
DOE OSTI2022

Analysis of the Source Physics Experiment Dry Alluvium Geology Ball Drop Sources

Two balls were dropped approximately 150 m down an open borehole as part of an investigation into the aftershocks from a 51 ton TNT-equivalent chemical explosion. The ball drop sources, a concrete-filled basketball and a bowling ball, were intended to mimic falling debris within an underground explosion borehole and cavity. Falling debris or sloughing of the borehole and cavity walls is thought to be one of the sources of explosion aftershocks. The signals from the ball drop sources were recorded by the same seismic network that monitored the aftershocks of the chemical explosion, so the ball drop recordings were used as templates to search through the continuous stream of aftershocks and detect correlated signals as a proportion of all aftershocks. The search resulted in only four similar event recordings out of the thousands of detected aftershocks. A synthetic test showed that the small, high-frequency ball drop templates were limited to producing highly correlated detections for events occurring in a small region around the impact location at 150 m depth. The high-frequency templates were necessary since the small ball drop signals were only above the noise at frequencies greater than 10 Hz. Unfortunately, the impact location is where the borehole was filled with sand in preparation for the subsequent chemical explosion in the test series, making it 150 m above the explosion created cavity centered at 300 m depth. This puts the ball drop templates 150 m away from the cavity, where many more events from falling debris would be expected. Further synthetic tests confirmed events emanating from the cavity 150 m below the impact site would have correlated with the lower frequency template and been detected had the template been usable below 10 Hz. Therefore, we conclude that the four detected events were most probably falling debris within the open borehole that impacted the floor at 150 m depth just as the ball drop sources had, and that this type of explosion aftershock mechanism comprises less than 1% of all explosion aftershocks.

58 GEOSCIENCES↗