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At least 271 records · Page 15

Refining Principal Stress Measurements in Reservoir Underburden in Regions of Induced Seismicity through Seismological Tools, Laboratory Experiments - Final Technical Report

This project developed methodologies to measure the in-situ principal stress in the deep subsurface through use of multiple independent, but complementary, seismic methods, laboratory verification, and development of theoretical frameworks. By leveraging existing regional and local datasets we developed, tested, and refined a set of diagnostic tools for determining the in-situ stress state with reduced uncertainty at and below reservoir depths (1.5-6 km). A set of novel tools was produced that are scale independent, such that their utility is equivalent on regional, field scale, and near borehole monitoring of principal stresses in reservoir underburden for carbon storage projects. During a 4-year Department of Energy (DOE) and Southern Company funded project, carried out by the Electric Power Research Institute (EPRI), Lawrence Livermore National Laboratory (LLNL), the University of Oklahoma (OU), and the U.S. Geological Survey (USGS), the project team developed methodologies to measure the far-field in-situ principal stress in the deep subsurface, leveraging induced seismicity data from waste-water disposal projects. These methodologies consisted in the use of well-established and technically advanced seismic processing methods, such as virtual seismometer method-moment tensor (VSM-MT) and shear wave splitting (SWS), that are adept at recovering the stress orientation and certain components of the stress tensor. These methods were applied to robust seismicity catalogs created with matched filter techniques near sites of active fluid disposal—a proxy for carbon storage sites where such datasets are more limited. Estimates of the stress orientation made with seismic processing tools were considered along with laboratory acoustic emission experiments conducted on rock samples from the region of interest. Stress orientations in the studied region do not vary significantly across distances of ~100 km, nor are they found to rotate through time as a consequence of local wastewater disposal, as previously speculated. Finally, the project team investigated the trade-offs among the different seismic methods and evaluated the range of uncertainty that is generated with these methodologies, which led to a practical use and refinement of the VSM-MT technique when it is applied to field datasets. Understanding the trade-offs between these different methods highlighted the potential benefits of improved quantification of uncertainties on stress field estimations.

58 GEOSCIENCES↗

Porewater Geochemical Assessment of Seismic Indications for Gas Hydrate Presence and Absence: Mahia Slope, East of New Zealand’s North Island

We compare sediment vertical methane flux off the Mahia Peninsula, on the Hikurangi Margin, east of New Zealand’s North Island, with a combination of geochemical, multichannel seismic and sub-bottom profiler data. Stable carbon isotope data provided an overview of methane contributions to shallow sediment carbon pools. Methane varied considerably in concentration and vertical flux across stations in close proximities. At two Mahia transects, methane profiles correlated well with integrated seismic and TOPAS data for predicting vertical methane migration rates from deep to shallow sediment. However, at our “control site”, where no seismic blanking or indications of vertical gas migration were observed, geochemical data were similar to the two Mahia transect lines. This apparent mismatch between seismic and geochemistry data suggests a potential to underestimate gas hydrate volumes based on standard seismic data interpretations. To accurately assess global gas hydrate deposits, multiple approaches for initial assessment, e.g., seismic data interpretation, heatflow profiling and controlled-source electromagnetics, should be compared to geochemical sediment and porewater profiles. A more thorough data matrix will provide better accuracy in gas hydrate volume for modeling climate change and potential available energy content.

03 NATURAL GAS↗

Seismic Characterization of the Blue Mountain Geothermal Field

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

58 GEOSCIENCES↗

Development of Simplified Probabilistic Risk Assessment Model for Seismic Initiating Event

ABSTRACT This paper discusses a simplified method to evaluate seismic risk using a methodology built on dividing the seismic intensity spectrum into multiple discrete bins. The seismic probabilistic risk assessment model uses Nuclear Regulatory Commission’s (NRC’s) full power Standardized Plant Analysis Risk (SPAR) model as the starting point for development. The seismic PRA models are integrated with their respective internal events at-power SPAR model. This is accomplished by combining the modified system fault trees from the full power SPAR model with seismic event tree logic. The peak ground acceleration is divided into five bins. The g-value for each bin is estimated using the geometric mean of lower and upper values of that particular bin and the associated frequency for each bin is estimated by taking the difference between upper and lower values of that bin. The component’s fragilities are calculated for each bin using the plant data, if available, or generic values of median peak ground acceleration and uncertainty values for the components. For human reliability analysis (HRA), the SPAR HRA (SPAR-H) method is used which requires the analysts to complete relatively straight forward worksheets that include the performance shaping factors (PSFs). The results are then used to estimate human error probabilities (HEPs) of interest. This work is expected to improve the NRC’s ability to include seismic hazards in risk assessments for operational events in support of the reactor oversight program (e.g., significance determination process).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Developing a 3-D Seismic-Attribute Framework Model of Yucca Flat, Nevada National Security Site

Using data collected during underground nuclear testing in Yucca Flat at the Nevada National Security Site, an existing 3-D hydrostratigraphic framework model (HFM) that was developed for groundwater modeling is being converted to a 3-D seismic-attribute framework model (SFM). The resulting SFM can be used to better understand seismic wave propagation from underground chemical explosive tests planned for Yucca Flat as part of the Source Physics Experiment. The Yucca Flat HFM covers 1,250 square kilometers and depicts the distribution of subsurface geologic units, called hydrostratigraphic units, according to their ability to transmit groundwater. Many of the rock properties and geologic structures that control groundwater flow also influence seismic wave transmission, so the team reasoned that the HFM could be modified to create an SFM. Existing geologic and geophysical data, including interval velocity measurements from down-hole seismic surveys conducted in 188 holes across Yucca Flat, were used to evaluate the HFM and develop a 3-D velocity property model. The velocity model was integrated with the HFM so that unit volumes within the HFM could be evaluated according to their seismic character. Results to date show, as anticipated, that most of the 3-D hydrostratigraphic unit volumes in the HFM can function as seismic-stratigraphic unit volumes. Thus, with relatively minor modifications, the HFM can be converted to an SFM.

seismic, Yucca Flat, model↗

Seismic and LOCA safety testing and analysis program for Westinghouse PWR fuel under end of life conditions

The NRC issued Information Notice - IN 2012-09 in June 2012 which challenges approved assumptions based on the beginning of life (BOL) conditions in seismic and LOCA analysis. Westinghouse manufactures a variety of PWR fuel designs, including 14*14, 15*15, 16*16 and 17*17 array, also different fuel lengths from 12 feet to 14 feet. The fuels are supplied to the cores designed by Westinghouse and Combustion Engineering, and other cores around world. To address IN 2012-09 efficiently and economically, Westinghouse and PWR Owner Groups launched a testing and analysis program funded by PWROG. The program planed testing and analysis matrices based on types of fuel designs. The testing plan includes grid crush tests, fuel assembly mechanical tests and flowing water damping tests. Applying modelling technique, the program will reduce the number of test and cost. The ultimate goal of the program is to generate fuel assembly EOL models for all fuel designs manufactured by Westinghouse and to be ready for any plant specific seismic and LOCA analysis to address NRC IN for EOL effect. The phase I of PWROG for EOL conditions has been completed and several conclusions are obtained. First, the gapped grids reduce grid dynamic stiffness, local impact stiffness and crush strength. Also, due to gapped grids, both the fuel assembly natural frequency and stiffness are reduced. Secondly, applying higher fuel assembly damping due to the flowing water effect can significantly reduce fuel assembly impact forces. However, during a seismic event the reactor coolant pumps may trip resulting in a coast-down. As such, reduced flow rates that occur during the pump coast-down must be considered. Thirdly, the analytical model representation of a reactor core adequately characterizes the core response under seismic and LOCA excitations. Static and dynamic structural test results of a fuel assembly compared with analytical predictions indicate that the fuel assembly lateral and axial structural behavior can be fully determined with finite element models. Fourthly, analyses for EOL fuel assemblies under seismic and LOCA loads demonstrate EOL effect and flowing water damping effect. With flowing water damping benefit, significant margin can be recovered for fuel assemblies with EOL conditions and under seismic loads.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Seismic fragility analysis for lateral supports of HTR-PM RPV - 18521

Since Fukushima accident, there has been increasing interest in China to study the risks of nuclear power plants under seismic events that are beyond the design basis. Two methodologies are commonly being adopted for this purpose, namely the seismic probabilistic risk assessment (SPRA) and the seismic margin assessment (SMA). Fragility analysis plays an important role as it not only provides fragility curves as input for the SPRA, but also can deduce HCLPF values for the SMA. In this paper, seismic fragilities of the lateral supports of the HTR-PM reactor pressure vessel (RPV) are studied. The lateral supports are crucial to the safety of HTR-PM RPV under seismic events. As they are located at a high elevation in the reactor building, it is expected they are one of the major contributors to the seismic risk of HTR-PM. The lateral supports are first briefly introduced in terms of structural features, load conditions and stress acceptance criteria in the engineering design. Their fragilities are then analyzed by identifying basic fragility analysis variables and determining values in terms of the median and standard deviations. The associated fragility curves are finally calculated and a HCLPF value is reported. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Pore-pressure diffusion, enhanced by poroelastic stresses, controls induced seismicity in Oklahoma

Induced seismicity linked to geothermal resource exploitation, hydraulic fracturing, and wastewater disposal is evolving into a global issue because of the increasing energy demand. Moderate to large induced earthquakes, causing widespread hazards, are often related to fluid injection into deep permeable formations that are hydraulically connected to the underlying crystalline basement. Using injection data combined with a physics-based linear poroelastic model and rate-and-state friction law, we compute the changes in crustal stress and seismicity rate in Oklahoma. This model can be used to assess earthquake potential on specific fault segments. The regional magnitude–time distribution of the observed magnitude (M) 3+ earthquakes during 2008–2017 is reproducible and is the same for the 2 optimal, conjugate fault orientations suggested for Oklahoma. At the regional scale, the timing of predicted seismicity rate, as opposed to its pattern and amplitude, is insensitive to hydrogeological and nucleation parameters in Oklahoma. Poroelastic stress changes alone have a small effect on the seismic hazard. However, their addition to pore-pressure changes can increase the seismicity rate by 6-fold and 2-fold for central and western Oklahoma, respectively. The injection-rate reduction in 2016 mitigates the exceedance probability of M5.0 by 22% in western Oklahoma, while that of central Oklahoma remains unchanged. A hypothetical injection shut-in in April 2017 causes the earthquake probability to approach its background level by ∼2025. We conclude that stress perturbation on prestressed faults due to pore-pressure diffusion, enhanced by poroelastic effects, is the primary driver of the induced earthquakes in Oklahoma.

58 GEOSCIENCES↗

EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on Geothermal Reservoir Engineering. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained), S-wave velocity (km/s), and S-wave velocity quality indicator (1 for well-constrained; 0 for poorly constrained). The Updated_MEQ_catalog text file contains event origin time, x(m), y(m), z(m), error in x (m), error in y (m), error in z (m), and RMS misfit (millisecond). The 3D_seismic_P-wave_velocity_model animation file shows slices of the 3D P-wave velocity model. The 3D_seismic_S-wave_velocity_model animation file shows slices of the 3D S-wave velocity model. The Interactive_MEQ_locations API file is an interactive visualization of the updated microseismic event locations. The visualization allows users to view the event locations by dragging, rotating, and zooming in. References: Chai, C., Maceira, M., Santos-Villalobos, H. J., Venkatakrishnan, S. V., Schoenball, M., and EGS Collab Team, 2020, Automatic Seismic Phase Picking Using Deep Learning for the EGS Collab Project, in PROCEEDINGS, 45th Workshop on Geothermal Reservoir Engineering, edited, Stanford University, Stanford, California, 45, 1266-1276.

15 GEOTHERMAL ENERGY↗

Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings

Abstract We develop a machine learning approach to detect and discriminate elephants from other species, and to recognise important behaviours such as running and rumbling, based only on seismic data generated by the animals. We demonstrate our approach using data acquired in the Kenyan savanna, consisting of 8000 h seismic recordings and 250 k camera trap pictures. Our classifiers, different convolutional neural networks trained on seismograms and spectrograms, achieved 80%–90% balanced accuracy in detecting elephants up to 100 m away, and over 90% balanced accuracy in recognising running and rumbling behaviours from the seismic data. We release the dataset used in this study: SeisSavanna represents a unique collection of seismic signals with the associated wildlife species and behaviour. Our results suggest that seismic data offer substantial benefits for monitoring wildlife, and we propose to further develop our methods using dense arrays that could result in a seismic shift for wildlife monitoring.

Szenicer, Alexandre↗

Geophysical monitoring using active seismic techniques at the Citronelle Alabama CO 2 storage demonstration site

Between August 2012 and September 2014, about 114,000 metric tonnes of CO 2 was captured from the coal-fired Plant Barry Power Station at Bucks Alabama and injected into the Paluxy Formation above the oil pool in the southeast unit of the Citronelle Oilfield. Various monitoring methods were deployed at land surface and in project wells to measure system performance, comply with permit requirements and test new and innovative monitoring tools. The monitoring program relied heavily on active seismic methods for subsurface imaging of geologic structure and time-lapse seismic techniques to track the CO 2 migration in the injection interval. Both conventional geophone/hydrophone and fiber-optic based Distributed Acoustic Sensing (DAS) arrays were deployed and tested, allowing a side by side comparison of the equipment and techniques. Geophysical imaging of the subsurface was successful using DAS in the offset vertical seismic profile (OVSP) survey configuration. A high resolution OVSP image of the subsurface was obtained in 2014 with DAS, which exceeded project expectations in comparison to a lower resolution image obtained in 2012 using a conventional 80-level geophone array. A time-lapse image of the redistribution of CO 2 after injection ended in September 2014 was obtained with two DAS OVSP surveys from June 2014 and December 2015, thus successfully demonstrating its proof-of-concept. Unfortunately, a pre-injection baseline survey with DAS, which was in its initial stage of technology development in 2012, did not have sufficient quality for use, making it difficult to interpret the acquired DAS time-lapse difference. Additional research in this area has since demonstrated the utility of time-lapse DAS OVSP. DAS data were also acquired during a cross-well seismic survey conducted in 2014. Unfortunately, the DAS technique was not success in the cross-well survey configuration because the system noise level was too high in the crosswell frequency output range (100–1200 Hz) of the piezoelectric source (increasing by a factor of ten compared to VSP frequency band). Additionally, the cross-well geometry causes sub-horizontal (broadside) incidence on the vertical DAS fiber cable, which is known to be problematic. Current research is focused on improving the DAS cable response to broadside acoustic energy. Time-lapse seismic surveys using commercially available conventional arrays were also acquired. In contrast to the DAS acquired data, the cross-well seismic results obtained with the conventional array was highly successful and clearly showed the CO 2 remained in zone at the end of injection. Time-lapse differencing of the OSVP surveys acquired with the conventional arrays proved to be inconclusive. Finally, changes in wellbore conditions between surveys and unavoidable changes in equipment (the array used for the baseline survey was retired) affected data quality, making it difficult to interpret the OVSP results.

58 GEOSCIENCES↗

Large scale centrifuge test of a geomembrane-lined landfill subject to waste settlement and seismic loading

Highlights: • A large centrifuge model of a geomembrane-lined landfill was seismically loaded. • The scaled peak acceleration of the strongest input motion was 0.6 g. • Laboratory testing was conducted to obtain properties of the model materials. • Collected data included liner strains due to downdrag and seismic loading. • Results show that settlement and seismic loading can potentially damage geomembranes. • Results can be used to validate numerical models for geomembrane performance. •The laboratory and centrifuge testing data is publicly available. - Abstract: A large scale centrifuge test of a geomembrane-lined landfill subject to waste settlement and seismic loading was conducted to help validate a numerical model for performance based design of geomembrane liner systems. The test was conducted using the 240 g-ton centrifuge at the University of California at Davis under the U.S. National Science Foundation Network for Earthquake Engineering Simulation Research (NEESR) program. A 0.05 mm thin film membrane was used to model the liner. The waste was modeled using a peat-sand mixture. The side slope membrane was underlain by lubricated low density polyethylene to maximize the difference between the interface shear strength on the top and bottom of the geomembrane and the induced tension in it. Instrumentation included thin film strain gages to monitor geomembrane strains and accelerometers to monitor seismic excitation. The model was subjected to an input design motion intended to simulate strong ground motion from the 1994 Hyogo-ken Nanbu earthquake. Results indicate that downdrag waste settlement and seismic loading together, and possibly each phenomenon individually, can induce potentially damaging tensile strains in geomembrane liners. The data collected from this test is publically available and can be used to validate numerical models for the performance of geomembrane liner systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Using Ambient Noise Seismic Interferometry and Local and Teleseismic Earthquakes to Determine Crustal Thickness and Moho Structure of the Northwestern Gulf of Mexico Margin

Abstract The northwestern part of the Gulf of Mexico has undergone two episodes of continental rifting and collision and produced structural artifacts that are now buried under many kilometers of sediments, complicating investigations of the region. The deep sedimentary package precludes outcrops and points to a need for the application of seismic techniques, but low rates of seismicity in the region and sparse seismic monitoring limit the utility of traditional seismic methods. We therefore use diverse data to perform two‐dimensional seismic tomography across the dry land portion of the margin. Data are gleaned from teleseismic and regional earthquakes, postcritical SsPmp arrivals, and direct P wave energy identified with seismic interferometry that were recorded by a broadband, three‐component array and partially overlapping short‐period, vertical‐component array. The Pn and postcritical SsPmp phase help constrain the Moho discontinuity, which a previous receiver function study suggested was absent beneath the seaward portion of this transect. A high‐velocity body is observed in the crust at the same location as the Houston Magnetic Anomaly, possibly marking rocks from the Alleghenian continental assembly. The crust thins from NW to SE, indicating that extension occurred mostly to the south of the Ouachita orogeny. Our model indicates that the margin's sediment package reaches a maximum thickness of ~15 km at the coast and becomes unresolvably thin near the Llano Uplift.

58 GEOSCIENCES↗

Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

Abstract Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.

Johnson, Christopher W.↗

Modeling Seismic Recordings of High-Frequency Guided Infrasound on Mars

NASA's Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission records several high-frequency (>0.5 Hz) dispersive seismic signals on Mars. These signals are due to the acoustic-to-seismic coupling of infrasound generated by the entry and impact of meteorites. This dispersion property is due to infrasound propagating in a structured atmosphere, and we refer to this dispersive infrasound as guided infrasound. We propose to model the propagation of guided infrasound and the seismic coupling to the ground analytically; we use a 1D layered atmosphere on a three-layer solid subsurface medium. The synthetic ground movements fit the observed dispersive seismic signals well and the fitting indicates that the regolith beneath InSight is about 40-m in thickness. We also examine and validate the previously-published subsurface models derived from InSight ambient seismic vibration data.

58 GEOSCIENCES↗

Probing the evolution of fault properties during the seismic cycle with deep learning

We use seismic waves that pass through the hypocentral region of the 2016 M6.5 Norcia earthquake together with Deep Learning (DL) to distinguish between foreshocks, aftershocks and time-to-failure (TTF). Binary and N-class models defined by TTF correctly identify seismograms in test with > 90% accuracy. We use raw seismic records as input to a 7 layer CNN model to perform the classification. Here we show that DL models successfully distinguish seismic waves pre/post mainshock in accord with lab and theoretical expectations of progressive changes in crack density prior to abrupt change at failure and gradual postseismic recovery. Performance is lower for band-pass filtered seismograms (below 10 Hz) suggesting that DL models learn from the evolution of subtle changes in elastic wave attenuation. Tests to verify that our results indeed provide a proxy for fault properties included DL models trained with the wrong mainshock time and those using seismic waves far from the Norcia mainshock; both show degraded performance. Our results demonstrate that DL models have the potential to track the evolution of fault zone properties during the seismic cycle. If this result is generalizable it could improve earthquake early warning and seismic hazard analysis.

58 GEOSCIENCES↗

Integrating 3D seismic curvature and curvature gradient attributes for fracture characterization: Methodologies and interpretational implications

In 3D seismic interpretation, curvature is a popular attribute that depicts the geometry of seismic reflectors and has been widely used to detect faults in the subsurface; however, it provides only part of the solutions to subsurface structure analysis. This study extends the curvature algorithm to a new curvature gradient algorithm, and integrates both algorithms for fracture detection using a 3D seismic test data set over Teapot Dome (Wyoming). In fractured reservoirs at Teapot Dome known to be formed by tectonic folding and faulting, curvature helps define the crestal portion of the reservoirs that is associated with strong seismic amplitude and high oil productivity. In contrast, curvature gradient helps better define the regional northwest-trending and the cross-regional northeast-trending lineaments that are associated with weak seismic amplitude and low oil productivity. In concert with previous reports from image logs, cores, and outcrops, the current study based on an integrated seismic curvature and curvature gradient analysis suggests that curvature might help define areas of enhanced potential to form tensile fractures, whereas curvature gradient might help define zones of enhanced potential to develop shear fractures. In certain fractured reservoirs such as at Teapot Dome where faulting and fault-related folding contribute dominantly to the formation and evolution of fractures, curvature and curvature gradient attributes can be potentially applied to differentiate fracture mode, to predict fracture intensity and orientation, to detect fracture volume and connectivity, and to model fracture networks.

3D, attributes, fractures, visualization↗

Alternate Null Hypothesis Correlation: A New Approach to Automatic Seismic Event Detection

This paper describes a new method of seismic signal detection that improves upon the conventional waveform correlation method. Recent studies suggested that a significant limiting factor in the application of waveform correlation to regional and global scale monitoring is the false alarm rate. The false alarms do not originate from detections on noise but rather from seismic arrivals with unrelated source locations. This article presents results from an approach to waveform correlation that exploits techniques from signal processing and machine learning to improve the accuracy of detecting seismic arrivals. We modify the detection model for waveform correlation such that transient signals from noncollocated seismicity are considered when designing the detectors. The new approach uses waveform templates from known catalog events to train a supervised machine learning algorithm that derives a new set of detectors to represent the unique characteristics of the template waveforms; these new detectors maximize the likelihood of detecting only the desired events, thereby minimizing false alarms. We train a waveform correlation template library for a single three-component seismic monitoring station. We then review results from applying the new detectors, known as alternate null hypothesis correlation (ANCorr) templates, to a test set of seismic waveforms. Finally, we compare ANCorr results with those from application of the conventional waveform correlation matched filter technique.

58 GEOSCIENCES↗