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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Microseismic Event Detection Using Multiple Geophone Arrays in Southwestern Utah

We conducted four geophone array experiments in southwestern Utah between July 2016 and October 2017 to characterize regional levels of microseismicity. Each experiment comprised between 48 and 93 geophones and recorded continuously for ~30 days. Using a new frequency domain array-based detector and a traditional STA/LTA detector, we identified 242 earthquakes. We were able to precisely locate 203 of these earthquakes using HYPOINVERSE. During the same time period the University of Utah Seismograph Stations (UUSS) catalog contained 68 earthquakes in the study area, therefore, we were able to locate 135 additional events. We estimate local magnitudes for events using maximum peak-to-trough amplitude and epicentral distance. As expected, our detection threshold becomes lower with higher station density. We find sparse seismicity across the study area apart from two areas that contain clustered and more frequent activity (Dog Valley and Cove Fort), which together account for roughly 70% of the recorded seismicity. A third prominent cluster in the central Mineral Mountains contains about 15% of the recorded seismicity. Lastly, the remainder of the detected seismicity is spread across the study area.

58 GEOSCIENCES↗
DOE OSTI2018

Microseismic Event Relocation Based on PageRank Linkage at the Newberry Volcano Geothermal Site

In this paper, we adapt the PageRank method to define signal-correlation topology for microearthquakes at the Newberry Volcano enhanced geothermal systems (EGS) demonstration in central Oregon. The Newberry EGS was stimulated in 2012 and 2014, producing hundreds of microearthquakes. Event locations based on P-wave and S-wave picks and a double-difference method resulted in diffuse clouds of seismicity for both stimulations (Foulger and Julian, 2013b; Cladouhos et al., 2016). We expand on the seismological application of PageRank (Aguiar and Beroza, 2014) to define event families based on signal-correlation topology and analyze each event family by measuring differential arrival times of P and S waves. We relocate each family using the Bayesloc method (Myers et al., 2007, 2009) and find that, after relocation, event families are tightly clustered spatially, indicating that the rock volume affected by stimulation was more than an order of magnitude smaller than would be inferred from double-difference analyses. Finally, relocation also reveals that most events occurred at the contacts between lithologic units, suggesting preferential fluid flow and hydroshearing along pre-existing weaknesses, as well as microseismic activity progressing away from the well with time.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Inference of Rock Flow and Mechanical Properties from Injection-Induced Microseismic Events During Geologic CO 2 Storage

Monitoring microseismic activities during CO 2 injection into geologic formations is important for ensuring the safety of the storage operations. The resulting data provide insight into the response of the storage formation to CO 2 injection and can be used to infer the underlying rock flow and mechanical properties. In this paper, assimilation of microseismic data is performed for dynamic characterization of the storage formation by using a stochastic simulation model to forecast the microseismic response of a geologic formation during CO 2 injection. Two modeling approaches are adopted to predict the space-time distribution of the injection-induced microseismicity. The first model is based on pore pressure relaxation assumption, while the second model uses coupled flow and geomechanics simulation to establish the complex physical relation between the storage formation properties and the corresponding microseismic responses during CO 2 injection. The stochastic predictive models in each case are used in ensemble data assimilation frameworks to estimate rock properties from the observed microseismic data. Two data assimilation methods are considered: (i) a new ensemble-based stochastic point process filter (EnPPF) that can directly integrate discrete microseismic events, and (ii) a variant of ensemble smoother, known as the ensemble smoother with multiple data assimilation (ES-MDA), which requires continuous representation of microseismic events for assimilation. The two methods are successfully applied to a geologically realistic model of the Farnsworth Field in Texas, with complex geologic flow units and interacting fault systems.

42 ENGINEERING↗
DOE OSTI2019

Utah FORGE: Microseismic Events

This archive contains Excel spreadsheets containing microseismic events detected in the study area during Utah FORGE Phase 2C. The Readme file included that describes the meaning of the abbreviations used as field headers in the spreadsheets. Additionally, there is a .dat (text) file containing additional location data and .txt file with field descriptions for the data in the .dat file.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

WHOLESCALE: Microseismic Event Catalog for San Emidio, Nevada 2022

This submission includes a high-precision seismic event catalog estimated from seismic data collected at San Emidio, Nevada from April to May 2022. The catalog lists the precise time, location, and magnitude of microseismic events recorded during this period. Both the seismic data and microseismic event catalog were produced as part of the Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses (WHOLESCALE) project. Attached here are the microseismic event catalog, a link to the raw seismic data, and a detailed description of methods used to create the catalog.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Microseismic Event Denoising: Removal of Borehole Waves

The vertical borehole array at Farnsworth Unit, TX is used to monitor microseismic activity in the subsurface around the Carbon Capture and Sequestration (CCS) reservoir. The array consists of 16 3-component seismometers spaced vertically in a single borehole. Tube or borehole waves traveling up or down the borehole can corrupt signals of interest, such as microseismic events. A denoising convolutional neural network (DCNN) was trained to remove borehole waves from seismic waveforms of microseismic events for the purpose of reducing unwanted signal detections and better characterizing events of interest. This R&D leverages the work of Sandia colleague Rigo Tibi, who used a DCNN developed by Greg Beroza at Stanford University to improve the signal-to-noise ratio (SNR) of teleseismic events detected by the International Monitoring System.

58 GEOSCIENCES↗
DOE OSTI2024

Utah FORGE Project 3-2417: DAS Microseismic Event Catalog from the 16A/16B Circulation Test, 2023

This preliminary data archive includes the relocated microseismic event catalog, 1D velocity model, and methods report from DAS acquisition conducted during the Well 16A and 16B circulation test (July 19th and 20th, 2023) at Utah FORGE. The methods report describes all processing steps, including real-time event detection, hierarchical clustering, joint velocity/hypocenter inversion, and relocation. The resulting work is accepted and will be presented at IMAGE 2024. This dataset was acquired by the FOGMORE R&D project (Fiber Optic MOnitoring for Reservoir Evolution), Utah FORGE R&D Project 3-2417.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Unsupervised Clustering of Microseismic Events and Focal Mechanism Analysis at the CO 2 Injection Site in Decatur, Illinois

Characterization of induced microseismicity at a carbon dioxide (CO 2 ) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO 2 injection. This approach enhances seismic monitoring capabilities at CO 2 injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.

Willis, Rachel Marie [Sandia National Laboratories↗
DOE OSTI2020

Automatic microseismic event picking via unsupervised machine learning

SUMMARY Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

Chen, Yangkang↗
DOE OSTI2017

An Advanced Geomechanics Simulation Framework to Connect Induced Microseismic Events to their Sources. CRADA TC02193 Final Report

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and ION Geophysical. The main goal of the proposed effort was to develop industry-leading state of the art modeling and analysis capabilities to address an important aspect of hydraulic fracturing operations -- the evolution of the fracture network in the subsurface and the associated generation of the microseismic signals associated with the creation of the fracture network. The proposed effort leveraged on-going code development activities at LLNL. The codes used to execute the proposed research were to be transitioned to ION Geophysical through a visitor exchange program whereby ION Geophysical employees would visit LLNL and interact with the code developers and subject matter experts enabling them to develop the technical understanding and the expertise needed to independently perform simulations and analyze results.

42 ENGINEERING↗
DOE OSTI2017

Washington State Play Fairway Analysis - Passive Monitoring of St. Helens Shear Zone for Tomography and Precision Microseismic Event Detection

Data resources were derived from a passive seismic survey of the northern St. Helens Shear Zone on geothermal leases 12-24 km north of Mount St. Helens for phase 2 of the Geothermal Play-Fairway Analysis of Washington State Prospects. A 20 seismic station array of broadband seismometers was deployed with irregular spacing (1-4 km) over an area of 12 km to image seismogenic features and their damage zones in the shallow crust.

15 GEOTHERMAL ENERGY↗