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The MARVEL reactor design requirements are currently listed within TFR-2576. Several MARVEL subsystems are required to be designed to seismic criteria of IBC 2015. This Engineering Analysis (EA) report documents the accelerations that need to be used in an equivalent-static method seismic analysis to satisfy this requirement.
Abstract Dissipation of tidal energy is expected to generate seismicity on icy‐ocean worlds; however, the distribution and timing of this seismic activity throughout an orbital cycle is not known. We used new observations from an icy‐ocean‐world analog environment on Earth to examine the relationship between tidally driven tensile stress and seismic activity within an ice shell. We investigated a pair of rifts within Antarctica's Ross Ice Shelf which are tidally stressed in a manner analogous to the orbital cycle of tidal stress experienced by Enceladus' Tiger Stripe Fractures. We found that seismic activity at the Antarctic rifts is sensitive to both the amplitude and the rate of tensile stress across the rifts. We combined these findings with calculated stress values along Enceladus' Tiger Stripe Fractures to predict seismic‐activity levels expected along the ice‐shell fractures. We predict a peak in seismicity along the four Tiger Stripe Fractures when Enceladus is 90°–120° past pericenter in its orbit around Saturn, at which point tensile stresses would reach ∼2/3 of their maximum value. We also used the magnitude distribution of icequakes along Antarctic rifts to investigate implications for the likely size of stick‐slip rupture patches along icy faults on Enceladus. Our findings predict that the Tiger Stripe Fractures should produce sustained, low‐magnitude seismic events that involve rupture along discrete portions of each fracture's total length. We predict that seismicity would fall to 50% of peak levels when stresses across the Tiger Stripe Fractures are dominantly compressional.
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The MARVEL reactor design requirements are currently listed within TFR-2576. Several MARVEL subsystems are required to be designed to seismic criteria of IBC 2015. This Engineering Analysis (EA) report documents the accelerations that need to be used in an equivalent-static method seismic analysis to satisfy this requirement.
Seismic data from the IBDP, included primarily under folders: Active_Seismic_Data and Passive_Seismic_Events_Monitoring. The data included under IBDP_Located_Microseismic_Event_Data are a subset of microseismic (Passive Seismic Events Monitoring) data acquired throughout the project from pre-injection to shut-in. These data represent the “located” microseismic events and the two datasets, located in folders Downhole_Geophone_Data and Surface_Seismometer_Data, are correlated.
We report that the Imperial Valley is a seismically active basin occupying the southern end of the Salton trough, an area of rapid extension, high heat flow, and abundant geothermal resources. This report describes an ongoing large-scale distributed acoustic sensing (DAS) recording study acquiring high-density seismic data on an array between Calipatria and Imperial, California. This 27 km array, operating on dark fiber since 9 November 2020, has recorded a wealth of local seismic events as well as ambient noise. The goal of the broader Imperial Valley Dark Fiber project is to evaluate passive DAS as a tool for geothermal exploration and monitoring. This report is intended to provide installation information, noise characteristics, and metadata for future studies utilizing the data set. Because of the relatively small number of basin-scale DAS studies that have been conducted to date, we also provide a range of lessons learned during the deployment to assist future researchers exploring this acquisition strategy.
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Conference presentation for International Meeting for Applied Geoscience & Energy (IMAGE), Houston, TX, August 28 – September 1, 2023. As part of the North Dakota CarbonSAFE project, seismic, controlled-source electromagnetic, and microgravity baseline monitoring surveys were collected to assess the safe, permanent, commercial-scale geologic storage of CO 2 generated by the Milton R. Young coal-fired power plant. Data from five 2D seismic lines were assessed as an alternative to 3D seismic for CO 2 monitoring. 2D and 3D surveys were compared using wave equation-based (WEB)-AVO inversion. The encouraging WEB-AVO results demonstrated that 2D surveys could be included in CO 2 monitoring programs as a cost-effective method.
This final report summarizes the work completed under the Laboratory Directed Research and Development (LDRD) project “Seismic Spatial Gradients as a Machine Learning-Based Classifier for Explosion Monitoring.” The overarching goal of the project was to explore the efficacy of using machine learning-based classification algorithms where the input data are the spatial gradient of the seismic wavefield collected at a single point on the Earth’s surface. The methods that I describe here are in direct contrast to conventional methods of seismic discrimination which typically rely on a spatially extended network of instruments and physics-based wavefield attributes such as, for example, the ratio between $\textit{P}$ and $\textit{S}$ waves. Rather, we use the spatial gradient of the seismic wavefield observed at a single point on the Earth’s surface and data processing approaches inspired by the machine learning community. We tested two algorithms, a neural network and a modified version of principal component analysis termed Spectrally Filtered Principal Component Analysis (SFPCA). To test these algorithms, we first conducted a series of numerical tests using synthetic data and then conducted a small-scale controlled field experiment. The tests using synthetic data showed that both algorithms had high success rates on gradiometric data, even when simulated noise was added to the signal. Furthermore, we found that using seismic spatial gradients increased the performance of our discrimination algorithms when compared to using just the traditional translational motion seismic data. The tests with field data also showed a high degree of discriminative success.
DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the DEEPEN 3D play fairway analysis (PFA) conducted at Newberry Volcano for multiple play types (conventional hydrothermal, superhot EGS, and supercritical), existing geoscientific exploration datasets needed to be acquired, cleaned, reformatted, and assembled in Leapfrog Geothermal. This GDR submission includes all of the cleaned and reformatted (X (m), Y (m), elevation (m), processed data values) datasets used to build the Leapfrog Geodata model. Existing datasets were acquired from the GDR, from AltaRock, and from other sources. This yielded the following datasets: - Digital elevation model produced from LiDAR data by Ramsey and Bard, 2016 - MT surveys from 2006, 2011, 2014, and 2017 (including single inversions) - Gravity surveys from 2006, 2007, and 2011 (including single) - Earthquake catalogs from PNSN, LLNL, and the Newberry EGS Demonstration project - Seismic velocity model from Templeton et al., 2014 - The Frone, 2015 temperature model and a new one produced through extrapolating downhole temperature measurements and the SMU temperature at depth maps. Two versions of the new model are provided: 250 m spacing and 500 m spacing - EarthVision geologic model with alteration from Moser et al., 2016 - Well data from EGS well 55-29, deep geothermal wells, coreholes (GEO N-2 through 5) and several thermal gradient holes - "Newberry Well Data:" Location, simple lithology, directional survey data, and temperature data for the 34 wells and coreholes used in the Newberry PFA Although there are additional 2D datasets available in the area, such as aeromagnetic surveys, these were not included in the analysis. While it may be possible to project these datasets into three dimensions by assuming the surface measurements do not vary with depth, this method is associated with high uncertainty. Preexisting inversions of these data were unavailable, and inverting additional geophysical datasets is outside the scope of this project.
Background: Interim Risk was added to P-SPRaP (SPR Phase 2) to produce intermediate results which build confidence, exercise methodology, develop early insights, and (most importantly) mitigate LANL pit production program risk. Status: SPR Phase 2 is actively underway, has P-SPRaP project team priority focus, and is targeting completion in FY21 (September 2021). Deliverable: Presentation of interim risk results (loss of confinement for screened in failure modes), list of potential criticisms of Interim Risk approach / methodology that intervenors could raise, cost and schedule for completion of Final Risk.
Earth models have important applications for seismic event monitoring including the location and characterization of potential underground nuclear tests. Seismic waves generated by earthquakes and man-made events are altered by variations in Earth’s subsurface properties; and these distortions effect the arrival times of energy packets (phases) and obscure the characteristics of the original energy source (event mechanism). Three-dimensional models of Earth’s seismic properties can help explain and predict the distortions to the seismic wavefield and reveal the properties of a source that generated the waves, including the event location using the model-predicted timing of the phases and source mechanism using the modelpredicted waveform characteristics.
Long-term seismic monitoring of carbon capture and storage projects (CCS) is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring (PRM) technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. In this context, we have developed and trialed the use of distributed acoustic sensing (DAS) coupled to permanent rotary sources, called surface orbital vibrators (SOV), with the objective to build a continuous monitoring array for CCS projects.
Time-lapse seismic is an essential tool for monitoring CO 2 injection into the subsurface but suffers from high cost and long intervals between surveys. An alternative approach is continuous monitoring using permanent sources and receivers capable of mapping CO 2 plume evolution on an almost daily basis and drastically reduce human effort, cost and survey turnaround. As part of the CO2CRC Otway project, a continuous monitoring system is employed to monitor a small (15,000 tonnes) CO 2 injection within the CO2CRC Otway Project. The permanent monitoring system employs distributed acoustic sensors (DAS) installed in boreholes as seismic receivers and seismic orbital vibrators (SOV) as seismic sources. The monitoring approach is based on vertical seismic profiling using five wells equipped with DAS cemented behind the casing and nine SOVs and is acquiring the data continuously in an autonomous manner. The data is then automatically processed on-site producing a seismic image every 2.5 days, which is then transmitted to a remote office. The data has good repeatability with NRMS of about ~10-15%.
Abstract not provided.
The geologic storage of carbon dioxide (CO 2 ) is one method that can help reduce atmospheric CO 2 by sequestering it into the subsurface. Large-scale deployment of geologic carbon storage, however, may be accompanied by induced seismicity. We present a project lifetime approach to address the induced seismicity risk at these geologic storage sites. This approach encompasses both technical and nontechnical stakeholder issues related to induced seismicity and spans the time period from the initial consideration phase to postclosure. These recommendations are envisioned to serve as general guidelines, setting expectations for operators, regulators, and the public. They contain a set of seven actionable focus areas, the purpose of which are to deal proactively with induced seismicity issues. Although each geologic carbon storage site will be unique and will require a custom approach, these general best practice recommendations can be used as a starting point to any site-specific plan for how to systematically evaluate, communicate about, and mitigate induced seismicity at a particular reservoir.
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