Search NASA⌕ Search

SEARCH · Search NASA

Results for “seismic data processing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

LL18-Signal Analysis-NDD2Ad (Final Report)

Under favorable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially-distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection, and scaling with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning, and possible application to studies of repeating events in induced, volcanic, and glacial seismicity. Our results place limits on the widely-held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.

58 GEOSCIENCES↗
DOE OSTI2021

Progressive transfer learning for low-frequency data prediction in full waveform inversion

To effectively overcome the cycle-skipping issue in full waveform inversion (FWI), we developed a deep neural network (DNN) approach to predict the absent low-frequency components by exploiting the hidden physical relation connecting the low- and the high-frequency data. To efficiently solve this challenging nonlinear regression problem, two novel strategies were proposed to design the DNN architecture and to optimize the learning process: (1) dual data feed structure; (2) progressive transfer learning. With the dual data feed structure, not only the high-frequency data, but also the corresponding beat tone data are fed into the DNN to relieve the burden of feature extraction. The second strategy, progressive transfer learning, enables us to train the DNN using a single evolving training dataset. Within the framework of the progressive transfer learning, the training dataset continuously evolves in an iterative manner by gradually retrieving the subsurface information through the physics-based inversion module, progressively enhancing the prediction accuracy of the DNN and propelling the inversion process out of the local minima. Here, the synthetic numerical experiments suggest that, without any a priori geological information, the low-frequency data predicted by the progressive transfer learning are sufficiently accurate for an FWI engine to produce reliable subsurface velocity models free of cycle-skipping artifacts.

02 PETROLEUM↗
DOE OSTI2020

The geometry of signal space: a case study of direct mapping between seismic signals and event distribution

SUMMARY Under favourable circumstances, seismic waveforms corresponding to an ensemble of events related by a common, spatially distributed process collectively exhibit a regular, signal-space geometry. When events in the ensemble have a common, or nearly common, source mechanism, this geometry is a distorted image of the distribution of events in the source region. The signal-space image can be visualized using a relatively simple waveform alignment and projection operation. Ensemble waveform correlation measurements can be inverted to estimate the distribution of the events in the source region, up to an arbitrary rotation, reflection and scaling, with residual distortion. We demonstrate these concepts with synthetic waveforms and with observations of long-wall mining induced seismicity for which substantial ground truth information is available. Our experience with these data has implications for location, correlation detection and machine learning and possible application to studies of repeating events in induced, volcanic and glacial seismicity. Our results place limits on the widely held assumption that waveform correlation is a useful measure of event separation. We suggest that the constraints on event separation need to be evaluated in the context of a population of related events, whose waveforms sample the signal space image of the source region. A better indicator of event separation is the length of the shortest path in signal space along the image.

58 GEOSCIENCES↗
DOE OSTI2025

PySolate : A Python‐Based Thresholding Tool to Denoise or Designal Seismic Waveforms Based on the Continuous Wavelet Transform

PySolate is a Python‐based toolset that implements the continuous wavelet transform and nonlinear thresholding operations to denoise or designal seismic data, following Langston and Mousavi (2019). This filtering approach can remove microseismic noise to isolate intermediate‐period seismic signals that are key to enabling full‐waveform modeling and analysis of smaller‐magnitude regional events. This approach is best for the application to signals with frequency or time separation of signal and noise, in contrast to Fourier analysis, which is effective when signal and noise are separated in frequency. We demonstrate the Python toolset using the six announced Democratic People’s Republic of Korea declared nuclear tests, showing the effectiveness of isolating the seismic signal compared to standard bandpass filtering. In conclusion, we also demonstrate the ease of using the toolset with any Python processing tools.

Asia↗
DOE OSTI2020

Screening IMS Seismic Detections using Dynamic Correlation Processor: Progress report for 2020

LLNL completed the FY20 workplan entailing application of the Dynamic Correlation Processor (DCP) to International Monitoring System (IMS) primary seismic data in 2019 and 2020. The effective analysis period was curtailed to January 1 through November 12, 2019 due to a data compression change that rendered later data unusable. Fixing the data compression error is a priority for our data source, and we plan to extend the period of data analysis when the fix is implemented. We found that the percentage overlap between detections generated by the International Data Centre (IDC) DFX software and detections generated by DCP varied widely by station. Overlap is 30-40% at some stations, suggesting that a large percentage of DFX detections could be screened (removed) from the detection-event association algorithm. However, detection overlap was much lower, a few percent, at many stations and the percentage overlap was low at stations known to have many mines and other local sources of seismicity. Unexpectedly low detection overlap at many stations prompted us to examine results for the ARCES station in detail. There is considerable mine activity near ARCES, but our initial FY20 analysis found only ~2% DCP/DFX detection overlap. Differences in the pre-processing (e.g. beam recipes) used by DCP and DFX appear to be the cause of low detection overlap at ARCES. DCP uses wideband filters to improve detector robustness and the IDC uses narrow-band pre-filtering to improve sensitivity. After reprocessing ARCES using the IDC beam recipe, DCP/DFX overlap increased to ~74%. This result shows that we must reprocess the IMS network using the IDC station-specific beam recipes if we are to effectively screen DFX detections and improve automatic event building performance at the IDC.

58 GEOSCIENCES↗
DOE OSTI2019

The Necessity for Iteration in the Application of Numerical Simulation to EGS: Examples from the EGS Collab Test Bed 1

The United States Department of Energy, Geothermal Technologies Office (GTO) is funding a collaborative investigation of enhanced geothermal systems (EGS) processes at the meso-scale. This study, referred to as the EGS Collab project, is a unique opportunity for scientists and engineers to investigate the creation of fracture networks and circulation of fluids across those networks under in-situ stress conditions. The EGS Collab project is envisioned to comprise three experiments and the site for the first experiment is on the 4850 Level in phyllite of the Precambrian Poorman formation, at the Sanford Underground Research Facility, located at the former Homestake Gold Mine, in Lead, South Dakota. Principal objectives of the project are to develop a number of intermediate-scale field sites and to conduct well-controlled in situ experiments focused on rock fracture behavior and permeability enhancement. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO's flagship EGS research effort. As noted, outcomes from the EGS Collab project experiments will serve as benchmarks for computer code verification, but numerical simulation additionally plays an essential role in designing these meso-scale experiments. This paper reviews specific numerical simulations supporting the design of experiments within Test Bed 1, a volume of phyllite rock under in-situ stress conditions off the western side of the West Access Drift on the 4850 Level, near Governor's Corner. Numerical simulations were executed prior to the start of hydraulic stimulation activities within Test Bed 1 following standard practices, using best estimates of principal stress conditions, thermal conditions, and the rock petrophysical properties, including geomechanical properties. These simulations indicated notching of the borehole would promote the initiation of transverse fractures, seismic magnitudes during the hydraulic stimulation would be below 0.1 magnitude on the Richter scale, a rock thermal conductivity of 5.0 W/m K yielded agreement with kISMET borehole temperature logs, back pressure on the production borehole would increase circulation across the test bed, and the production borehole would serve to halt propagation of the hydraulic fracture to the drift. Pre-existing natural fractures, heterogeneities in the rock properties, monitoring boreholes, and overlooked mine elements have prompted a second look at numerically modeling stimulation, fluid circulation, tracer migration, and thermal breakthrough. Numerical simulation is an invaluable tool for providing insight and understanding to complex physical processes. The success of simulations, however, often depends on including all of the salient features of the system in the founding conceptual model. This paper takes a retrospective look at examples where the conceptual model and simulation was sufficient to provide accurate forecasts and those where elements were missing, necessitating rethinking of the simulation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

The Necessity for Iteration in the Application of Numerical Simulation to EGS: Examples from the EGS Collab Test Bed 1

The United States Department of Energy, Geothermal Technologies Office (GTO) is funding a collaborative investigation of enhanced geothermal systems (EGS) processes at the meso-scale. This study, referred to as the EGS Collab project, is a unique opportunity for scientists and engineers to investigate the creation of fracture networks and circulation of fluids across those networks under in-situ stress conditions. The EGS Collab project is envisioned to comprise three experiments and the site for the first experiment is on the 4850 Level in phyllite of the Precambrian Poorman formation, at the Sanford Underground Research Facility, located at the former Homestake Gold Mine, in Lead, South Dakota. Principal objectives of the project are to develop a number of intermediate-scale field sites and to conduct well-controlled in situ experiments focused on rock fracture behavior and permeability enhancement. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO’s flagship EGS research effort. As noted, outcomes from the EGS Collab project experiments will serve as benchmarks for computer code verification, but numerical simulation additionally plays an essential role in designing these meso-scale experiments. This paper reviews specific numerical simulations supporting the design of experiments within Test Bed 1, a volume of phyllite rock under in-situ stress conditions off the western side of the West Access Drift on the 4850 Level, near Governor’s Corner. Numerical simulations were executed prior to the start of hydraulic stimulation activities within Test Bed 1 following standard practices, using best estimates of principal stress conditions, thermal conditions, and the rock petrophysical properties, including geomechanical properties. These simulations indicated notching of the borehole would promote the initiation of transverse fractures, seismic magnitudes during the hydraulic stimulation would be below 0.1 magnitude on the Richter scale, a rock thermal conductivity of 5.0 W/m K yielded agreement with kISMET borehole temperature logs, back pressure on the production borehole would increase circulation across the test bed, and the production borehole would serve to halt propagation of the hydraulic fracture to the drift. Pre-existing natural fractures, heterogeneities in the rock properties, monitoring boreholes, and overlooked mine elements have prompted a second look at numerically modeling stimulation, fluid circulation, tracer migration, and thermal breakthrough. Numerical simulation is an invaluable tool for providing insight and understanding to complex physical processes. The success of simulations, however, often depends on including all of the salient features of the system in the founding conceptual model. This paper takes a retrospective look at examples where the conceptual model and simulation was sufficient to provide accurate forecasts and those where elements were missing, necessitating rethinking of the simulation.

numerical simulation, EGS Collab, enhanced geother↗
DOE OSTI2015

Prestack reverse time migration for 3D marine reflection seismic data

Prestack reverse time migration (RTM) is a method for imaging the subsurface using the inner product of wavefield extrapolation in shot domain and in receiver domain. It is well known that RTM is better for preserving amplitudes and phases than other prestack migrations. Since 3D seismic data is huge data volume and it needs heavy computing works, it requires parallel computing in order to have a meaningful depth image of the 3D subsurface. We implemented a parallelized version of 3D RTM for prestack depth migration. The results of numerical example for 3D SEG/EAGE salt model showed good agreement with the original geological model. We applied RTM to offshore 3D seismic reflection data. The study area is 12 × 25 km with 120 survey lines. Shot and receiver spacing is 25 m and 12.5 m. The line spacing is 100 m. Shot gathers were preprocessed to enhance signal to noise ratio and velocity model was calculated from conventional stack velocity. Both of them were used to obtain 3D image using RTM. The results show reasonable subsurface image.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

Abstract The regional seismic travel time (RSTT) model and software were developed to improve travel-time prediction accuracy by accounting for three-dimensional crust and upper mantle structure. Travel-time uncertainty estimates are used in the process of associating seismic phases to events and to accurately calculate location uncertainty bounds (i.e. event location error ellipses). We improve on the current distance-dependent uncertainty parameterization for RSTT using a random effects model to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The random effects model separates the error between observed slowness and model predicted slowness into bias and random components. The path-specific travel-time uncertainty is calculated by integrating these mean squared errors along a seismic-phase ray path. We demonstrate that event location error ellipses computed for a 90% coverage ellipse metric (used by the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (IDC)), and using the path-specific travel-time uncertainty approach, are more representative (median 82.5% ellipse percentage) of true location error than error ellipses computed using distance-dependent travel-time uncertainties (median 70.1%). We also demonstrate measurable improvement in location uncertainties using the RSTT method compared to the current station correction approach used at the IDC (median 74.3% coverage ellipse).

58 GEOSCIENCES↗
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
DOE OSTI2015

Non-intrusive, high-resolution, real-time, two-dimensional imaging of multiphase materials using acoustic array sensors

Two parallel multi-element ultrasonic acoustic arrays combined with sets of focal laws for acoustic signal generation and a classical tomographic inversion algorithm are used to generate real-time two-dimensional micro seismic acoustic images of multiphase materials. Proof of concept and calibration measurements were performed for single phase and two phase liquids, uniform polyvinyl chloride (PVC) plates, and aluminum cylinders imbedded in PVC plates. Measurement artefacts, arising from the limited range of viewing angles, and the compromise between data acquisition rate and image quality are discussed. The angle range of scanning and the image resolution were varied, and the effects on the quality of the reproduction of the speed of sound profiles of model solids and liquids with known geometries and compositions were analysed in detail. The best image quality results were obtained for a scanning angle range of [−35°, 35°] at a step size of 2.5° post processed to generate images on a 40 μm square grid. The data acquisition time for high quality images with a 30 mm × 40 mm view field is 10 min. Representation of two-phase solids with large differences in speed of sound between phases and where one phase is dispersed in the form of macroscopic objects (greater than 1 mm in diameter) proved to be the most difficult to image accurately. Liquid-liquid and liquid-vapor phase boundaries, in micro porous solids by contrast, were more readily defined. Displacement of air by water and water by heptane in natural porous limestone provides illustrative kinetic examples. Measurement results with these realistic cases demonstrate the feasibility of the technique to monitor in real time and on the micrometer length scale local composition and flow of organic liquids in inorganic porous media, one of many envisioned engineering applications. Improvement of data acquisition rate is an area for future collaborative study.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Development of Time Lapse VSP Integration Workflow: A Case Study at Farnsworth CO2-EOR Project

Abstract This study aims to develop a 4D Vertical Seismic Profile (VSP) integration workflow to improve the prediction of subsurface stress changes. The selected study site is a 5-spot pattern within the ongoing CO2-EOR operations at the Farnsworth Field Unit FWU in Ochiltree County, Texas. The specific pattern has undergone extensive geological and geomechanical characterization through the acquisition of 3D seismic data, geophysical well logs, and core. This workflow constrains a numerical hydromechanical model by applying a penalty function formed between "modeled" versus "observed" time-lapse compressional and shear seismic velocity changes. Analyses of geophysical logs and ultra-sonic measurements on core exhibit measurable sensitivities to changes in both fluid saturation and mean effective stress. These data are used to develop a site-specific rock physics model and stress-velocity relationship, which inform the numerical models used to generate the "modeled" portion of the penalty function. The "observed" portion of the penalty function is provided by a novel elastic full-waveform inversion of the available 3D baseline and three monitor surveys to produce high-quality estimates of time-lapse compressional and shear seismic velocity changes. The modeling workflow accounts sequentially for fluid substitution and stress impacts. Hydrodynamic and geomechanical properties of the 3D coupled numerical model are estimated through geostatistical integration of well log and core data with 3D seismic inversion products. Changes in seismic velocities due to fluid substitution are computed using the Biot-Gassmann workflow and site-specific rock physics. Stress impacts on time-lapse seismic velocity changes are modeled from the effective stress output of the hydromechanical model and are initially based on the velocity versus effective stress relationship extracted from core mechanical testing. Based on the principle of superposition of seismic wavefields, seismic velocity changes attributed to fluid substitution and that due to changes in mean effective stress are treated as linearly additive. The modeled results are upscaled using Backus averaging to reconcile scale discrepancies between the modeled and measured datasets to formulate the penalty function. This manuscript presents the forward modeling process and concludes that for the base case, the seismic velocity changes due to mean effective stress dominates over the seismic velocity changes attributed to fluid substitution because of the extensive range of the pressure perturbations. Successful minimization of this penalty function calibrates the coupled hydrodynamic geomechanical numerical model and affirms the suitability of acoustic time-lapse measurements such as 4D-VSP for geomechanical calibration.

02 PETROLEUM↗
DOE OSTI2019

Measuring the impact of burst buffers on data-intensive scientific workflows

Science applications frequently produce and consume large volumes of data, but delivering this data to and from compute resources can be concerning, as parallel file system performance is not keeping up with compute and memory performance. To alter this I/O bottleneck, some systems have deployed burst buffers, but their impact on performance for real-world scientific workflow applications is still not clear. In this paper, we examine the impact of burst buffers through the remote-shared, allocatable burst buffers on the Cori system at NERSC. By running two data-intensive workflows, a high-throughput genome analysis workflow, and a subset of the SCEC high-performance CyberShake workflow, a production seismic hazard analysis workflow, we find that using burst buffers offers read and write improvements of an order of magnitude, and these improvements lead to increased job performance, and thereby increased overall workflow performance, even for long-running CPU-bound jobs.

42 ENGINEERING↗
DOE OSTI2023

Explosive Yield Estimation Using Regional Seismic Moment Tensors

Here, we use the Pasyanos and Chiang (2022) data set to calculate the seismic moment M 0 for each explosion and use the measured explosive yield W to validate the W~M 0 relationship in Denny and Johnson (1991; hereafter, DJ91). The M 0 is corrected by transforming to a potency tensor and applying more appropriate near-source geophysical parameter values in the moment estimate. The mean residual between observed and predicted yield is near zero; however, the standard deviation of the residuals results in an F-value (a 95% confidence factor) of about 5. We re-estimate the coefficients in the DJ91 model and find similar values and only a slight improvement in the F-value. Next, we embark on a similar model selection process as DJ91, allowing for non-cube-root yield scaling and other plausible near-source elastic moduli. As was found by DJ91, the yield dependence is not significantly different from unity, and a cube root assumption is valid. Therefore, we yield scale the seismic moment and test the significance of all plausible explanatory variables. Isotropic moment performs better in the response variable than total moment. The preference for isotropic moment could be due to its relationship to volume change, which would be more directly affected by explosive yield. Surprisingly, we find that the overburden pressure, which is a function of depth, is not a significant parameter in the model. We hypothesize that this is due to the competing depth effects on source asymmetry and the incorporation of depth in the Green’s functions used to calculate the seismic moment tensors. Importantly, this emphasizes that only seismic moment tensor-derived moments should be used in these models. After removing insignificant model parameters, we are left with a simple model to predict explosive yield $\widehat{W}$ in kt from isotropic moment M I in N·m, $\widehat{W}$=κ –1.4132 10 0.035626GP M I , in which κ and GP are the near-source bulk modulus and gas porosity in Pa and %, respectively. The F-value for this model is approximately 3.

58 GEOSCIENCES↗
DOE OSTI2025

Seismic imaging of the Ecuadorian forearc and arc from joint ambient noise, local, and teleseismic tomography: catching the Nazca slab in the act of flattening

SUMMARY The Ecuadorian Andes are a complex region characterized by accreted oceanic terranes driven by the ongoing subduction of the oceanic Nazca plate beneath South America. Present-day tectonics in Ecuador are linked to the downgoing plate geometry featuring the subduction of the aseismic, oceanic Carnegie Ridge, which is currently entering the trench. Using seismic tomography, we jointly invert arrival times of P and S waves from local and teleseismic earthquakes with surface wave dispersion curves to image the structure of the forearc and magmatic arc of the Ecuadorian Andes. Our data set includes > 100 000 traveltimes recorded at 294 stations across Ecuador. Our images show the basement of the central forearc is composed of accreted oceanic terranes with high elastic wave speeds. Inboard of the Carnegie Ridge, the westernmost forearc and coastal cordilleras display relatively low Vp and Vs and high Vp/Vs values, which we attribute to the increased hydration and fracturing of the overriding plate due to the subduction of the thick oceanic crust of the Carnegie Ridge. We additionally image across-arc differences in magmatic architecture. The frontal volcanic arc overlies accreted terranes and is characterized by low velocities and high Vp/Vs indicative of partial melt reservoirs which are limited to the upper crust. In contrast, the main arc displays regions of partial melt across a wider range of depths. The Subandean zone of Ecuador has two active volcanoes built on continental crust suggesting the arc is expanding eastwards. The mid to lower crust does not show indications of being modified from the magmatic process. We infer that the slab is in the process of flattening as a consequence of early-stage subduction of the buoyant Carnegie Ridge.

58 GEOSCIENCES↗
DOE OSTI2014

Innovative Ballasted Flat Roof Solar PV Racking System

The objective of this project was to reduce the cost of racking for PV solar on flat commercial rooftops. Cost reductions would come from both labor savings and material savings related to the installation process. The rack would need to accommodate the majority of modules available on the market. Cascade Engineering has a long history of converting traditional metal type applications over to plastic. Injection molding of plastics have numerous advantages including selection of resin for the application, placing the material exactly where it is needed, designing in features that will speed up the installation process, and weight reduction of the array. A plastic rack would need to meet the requirements of UL2703, Mounting systems, mounting devices, clamping/retention devices, and ground lugs for use with flat-plate photovoltaic modules and panels. Comparing original data to the end of project racking design, racking material costs were reduced 50% and labor costs reduced 64%. The racking product accommodates all 60 and 72 cell panels on the market, meets UL2703 requirements, contributes only 1.3 pounds per square foot of weight to the array, requires little ballast to secure the array, automatically grounds the module when the module is secured, stacks/nests well for shipping/fewer lifts to the roof, provides integrated wire routing, allows water to drain on the roof, and accommodates various seismic roof connections. Project goals were achieved as noted in the original funding application.

14 SOLAR ENERGY↗
DOE OSTI2021

Three-dimensional paganica fault morphology obtained from hypocenter clustering (L'Aquila 2009 seismic sequence, Central Italy)

In seismic modelling, fault planes are normally assumed to be flat due to the lack of data which can constrain fault morphology. However, incorporating 3D fault morphology is important for modelling several phenomena, for example calculating mainshock induced stress changes. Here we utilize a data-analytical method to unveil the 3D rupture morphology of faults using unsupervised clustering techniques applied to earthquake hypocenters in seismic sequences. We apply this method to the 2009 L'Aquila seismic sequence which involved a M W 6.1 mainshock on April 6th. We use a dataset of about 50,000 relocated events, mostly microearthquakes, reaching magnitude of completeness equal to 0.7. Clustering distinguishes the earthquakes as occurring in three main clusters along with other minor fault segments. We then represent the morphology of the main Paganica fault system (responsible for the largest mainshock) using splines. This method shows promise as a step toward robustly and quickly obtaining 3D rupture morphologies where earthquake sequences have been monitored. The 3D model is presented interactively online, and the processing is presented in an interactive Jupyter Notebook (https://bit.ly/2MnCFdj).

58 GEOSCIENCES↗
DOE OSTI2019

Applying Waveform Correlation to Aftershock Sequences Using a Global Sparse Network

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize earthquake aftershock sequences to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in detecting similar seismic waveforms from repeating earthquakes, including aftershock sequences. We report the results of an experiment that uses waveform templates recorded by multiple stations of the Comprehensive Nuclear-Test-Ban Treaty International Monitoring System during the first twelve hours after a mainshock to detect and identify aftershocks that occur during the subsequent week. We discuss approaches for station and template selection, threshold setting, and event detection that are specialized for aftershock processing for a sparse, global network. We apply the approaches to three aftershock sequences to evaluate the potential for establishing a set of standards for aftershock waveform correlation processing that can be effective for operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Center to develop an intuition about potential reduction in analyst workload.

58 GEOSCIENCES↗