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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Operational Forecasting of Induced Seismicity

Operational Forecasting of Induced Seismicity (ORION) is an open-source toolkit developed as part of the US Department of Energy SMART and NRAP programs. ORION is designed to help both expert and non-expert users understand the seismic hazard at a site. This tool analyzes how the seismic hazard evolves during injection of fluid, and can be used to identify potential mitigation strategies for observed or forecasted seismic activity. ORION is built using Python, and has an optional GUI where users can easily configure model parameters, evaluate temporal and spatial forecasts, and inspect key data (seismicity, fluid injection, pressure models, etc.).

Kroll, Kayla↗
DOE OSTI2025

sup3ruhi (Super Resolution for Renewable Resource Data and Urban Heat Islands) [SWR-25-05]

Urban heat is a growing concern, particularly in dense metropolitan areas where high temperatures increase the risk of heat-related illness and drive energy expenses for cooling. Estimating the effects of urban heat remains a challenge due to limitations in describing the built environment, computational constraints, and the need for high-resolution data. This software presents open-source, computationally efficient machine learning methods that enhance the accuracy of urban temperature estimates compared to historical reanalysis data. Models trained using this software have been applied to urban microclimates in Los Angeles and Seattle showing greater accuracy and less bias when compared to low-resolution reanalysis datasets like ERA5 and even when compared to high-resolution mesoscale numerical weather models like WRF with an urban canopy model. Initial findings highlight how machine learning can support urban heat resilience planning by enabling improved assessments of local heat islands, mitigation strategies, and their energy implications. This software is an extension of (sup3r). This software supports the following publication: Buster, Grant, et al. Tackling Extreme Urban Heat: A Machine Learning Approach to Assess the Impacts of Climate Change and the Efficacy of Climate Adaptation Strategies in Urban Microclimates. arXiv:2411.05952, arXiv, 8 Nov. 2024. arXiv.org, https://doi.org/10.48550/arXiv.2411.05952. And has related public data records available at: Buster, Grant, Cox, Jordan, Benton, Brandon, and King, Ryan. Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI). United States: N.p., 16 Oct, 2024. Web. https://data.openei.org/submissions/6220.

Buster, Grant [National Renewable Energy Laborator↗
DOE OSTI2024

Assessment of U.S. Urban Surface Temperature Using GOES-16 and GOES-17 Data: Urban Heat Island and Temperature Inequality

Abstract This study utilizes hourly land surface temperature (LST) data from the Geostationary Operational Environmental Satellite (GOES) to analyze the seasonal and diurnal characteristics of surface urban heat island intensity (SUHII) across 120 largest U.S. cities and their surroundings. Distinct patterns emerge in the classification of seasonal daytime SUHII and nighttime SUHII. Specifically, the enhanced vegetation index (EVI) and albedo (ALB) play pivotal roles in influencing these temperature variations. The diurnal cycle of SUHII further reveals different trends, suggesting that climate conditions, urban and nonurban land covers, and anthropogenic activities during nighttime hours affect SUHII peaks. Exploring intracity LST dynamics, the study reveals a significant correlation between urban intensity (UI) and LST, with LST rising as UI increases. Notably, populations identified as more vulnerable by the social vulnerability index (SVI) are found in high UI regions. This results in discernible LST inequality, where the more vulnerable communities are under higher LST conditions, possibly leading to higher heat exposure. This comprehensive study accentuates the significance of tailoring city-specific climate change mitigation strategies, illuminating LST variations and their intertwined societal implications.

Environmental Sciences & Ecology↗
DOE OSTI2014

Security Informatics Research Challenges for Mitigating Cyber Friendly Fire

This paper addresses cognitive implications and research needs surrounding the problem of cyber friendly re (FF). We dene cyber FF as intentional o*ensive or defensive cyber/electronic actions intended to protect cyber systems against enemy forces or to attack enemy cyber systems, which unintentionally harms the mission e*ectiveness of friendly or neutral forces. We describe examples of cyber FF and discuss how it ts within a general conceptual framework for cyber security failures. Because it involves human failure, cyber FF may be considered to belong to a sub-class of cyber security failures characterized as unintentional insider threats. Cyber FF is closely related to combat friendly re in that maintaining situation awareness (SA) is paramount to avoiding unintended consequences. Cyber SA concerns knowledge of a system's topology (connectedness and relationships of the nodes in a system), and critical knowledge elements such as the characteristics and vulnerabilities of the components that comprise the system and its nodes, the nature of the activities or work performed, and the available defensive and o*ensive countermeasures that may be applied to thwart network attacks. We describe a test bed designed to support empirical research on factors a*ecting cyber FF. Finally, we discuss mitigation strategies to combat cyber FF, including both training concepts and suggestions for decision aids and visualization approaches.

cyber friendly fire↗
DOE OSTI2023

Response patterns of simulated corn yield and soil nitrous oxide emission to precipitation change

Abstract Background Precipitation plays an important role in crop production and soil greenhouse gas emissions. However, how crop yield and soil nitrous oxide (N 2 O) emission respond to precipitation change, particularly with different background precipitations (dry, normal, and wet years), has not been well investigated. In this study, we examined the impacts of precipitation changes on corn yield and soil N 2 O emission using a long-term (1981–2020, 40 years) climate dataset as well as seven manipulated precipitation treatments with different background precipitations using the DeNitrification-DeComposition (DNDC) model. Results Results showed large variations of corn yield and precipitation but small variation of soil N 2 O emission among 40 years. Both corn yield and soil N 2 O emission showed near linear relationships with precipitation based on the long-term precipitation data, but with different response patters of corn yield and soil N 2 O emission to precipitation manipulations. Corn yield showed a positive linear response to precipitation manipulations in the dry year, but no response to increases in precipitation in the normal year, and a trend of decrease in the wet year. The extreme drought treatments reduced corn yield sharply in both normal and wet years. In contrast, soil N 2 O emission mostly responded linearly to precipitation manipulations. Decreases in precipitation in the dry year reduced more soil N 2 O emission than those in the normal and wet years, while increases in precipitation increased more soil N 2 O emission in the normal and wet years than in the dry year. Conclusions This study revealed different response patterns of corn yield and soil N 2 O emission to precipitation and highlights that mitigation strategy for soil N 2 O emission reduction should consider different background climate conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The influence of spaceflight on the astronaut salivary microbiome and the search for a microbiome biomarker for viral reactivation

Background: Spaceflight impacts astronauts in many ways but little is known on how spaceflight affects the salivary microbiome and the consequences of these changes on astronaut health, such as viral reactivation. In order to understand this, the salivary microbiome was analyzed with 16S rRNA gene amplicon sequencing, and saliva viral titers were analyzed with quantitative polymerase chain reaction (qPCR) with primers specific for Epstein-Barr virus (EBV), herpes simplex virus (HSV), and varicella zoster virus (VZV) from 10 astronauts pre-flight, in-flight, and post-flight. Results: Streptococcus was the most abundant organism in the saliva, making up 8% of the total organisms detected, and their diversity decreased during spaceflight. Other organisms that had statistically significant changes were Proteobacteria and Fusobacteria which increased during flight and Actinobacteria which decreased during flight. At the genus level, Catonella , Megasphera , and Actinobacillus were absent in more than half of saliva samples collected pre-flight but were then detected during flight. In those subjects that already had these genera pre-flight, their relative abundances increased during flight. Correlation analyses between the microbiome and viral titers revealed a positive correlation with Gracilibacteria , Absconditabacteria , and Abiotrophia and a negative correlation between Oribacterium , Veillonella , and Haemophilus . There was also a significant positive correlation between microbiome richness and EBV viral titers. Conclusions: This is the first study to look at how the salivary microbiome changes as a result of spaceflight and the search for bacterial biomarkers for viral reactivation. Further studies examining the role of specific organisms that were shown to be correlative and predictive in viral reactivation, a serious problem in astronauts during spaceflight, could lead to mitigation strategies to help prevent disease during both short and long duration space missions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Chapter 4: "Waste"-to-Energy for Decarbonization - Transforming Nut Shells Into Carbon-Negative Electricity

This chapter presents a study demonstrating waste pistachio nut shells as a renewable feedstock for climate-friendly electricity generation via industrial gasification technology. The study includes biomass feedstock characterization (i.e., pistachio waste critical material attributes), process variability (i.e., bulk material handling), and overall operational reliability and conversion performance through extended testing. Additionally, techno-economic analysis (TEA) and life cycle assessment (LCA) were performed to assess the economic feasibility and environmental impact of the technology to transform agricultural waste to biopower. For processing pistachio waste material, among critical material attributes, fines content in the biomass (<1/4") had the largest potential to reduce the operating time of the gasifiers due to plugging. Pelletizing fines and co-feeding them with the mixed pistachio waste increased the average feed density, feed rate, and biochar production. Compared to pine wood chips, mixed pistachio waste yielded higher biochar quantity but slightly reduced quality. In general, a systematic Quality by Design methodology is the preferred approach for designing preprocessing and material conveyance systems, where a downstream technology (end user) for the produced intermediate is specified at the outset. TEA results show that the biochar production rate and selling price had an overwhelming impact on the modeled Minimum Electricity Selling Price (MESP), which ranged from 35.5 to 39.9 cents/kWh for the cases studied (16 h/day operational basis). Moreover, LCA results show that the valorization of pistachio shells for biopower generation is a "carbon negative" process that can help decarbonize the U.S. electricity grid. The specific carbon intensity was -0.29 to -0.71 kg CO2e/kWh, compared to 0.45 kg CO2e/kWh for the average U.S. electricity mix. Biochar production from pistachio waste as a potential means for carbon sequestration was a significant driver for the LCA. The highly stable biochar permanently sequesters a considerable fraction of biochar carbon in the ground, more than enough to offset the life cycle emissions, and can be a complementary climate change mitigation strategy.

bio-char↗
DOE OSTI2023

Endurance Exercise Performance Is Reduced after 6-h Dives at 1.35 ATA When Breathing 100% Oxygen Compared with Air

ABSTRACT Introduction Long-duration dives on consecutive days reduces muscular performance, potentially affecting military personnel. However, a paucity of data exists on how breathing gases affect endurance performance. This study examined the influence of long-duration diving with different breathing gases on aerobic endurance and handgrip performance. Methods Twenty-three military divers completed a single 6-h dive (single dive [SD]) and five 6-h dives over consecutive days (dive week [DW]) with 30-min cycling intervals using air (AIR,n= 13) or 100% oxygen (OXY,n= 10). Before and after SD and DW, subjects completed a maximum handgrip strength test, a handgrip endurance test at 40% maximal strength, and a time to exhaustion run. Results Handgrip endurance decreased after DW in OXY (SD, 1.9 ± 0.0 vs 1.4 ± 0.3 min) compared with AIR (1.8 ± 0.0 vs 1.8 ± 0.2 min) (P< 0.001). Run time decreased after SD (Pre, 20.7 ± 10.4 min; Post, 16.6 ± 7.6 min;P= 0.039) and DW (Pre, 21.6 ± 9.0 min; Post, 11.2 ± 4.0 min;P< 0.001) in OXY and after overall diving in AIR (Pre, 26.5 ± 10.2 min; Post, 22.3 ± 7.5 min;P= 0.025). V̇O 2 decreased after diving only in AIR (Pre, 42.6 ± 3.4 mL·kg −1 ⋅min −1 ; Post, 40.4 ± 3.7 mL·kg −1 ⋅min −1 ;P= 0.010). There were no other significant effects. Conclusions Breathing 100% oxygen during long-duration dives on consecutive days may exacerbate decreases in aerobic endurance and impairs handgrip endurance compared with air. Additional research is needed to elucidate mechanisms of action and possible mitigation strategies.

Sport Sciences↗
DOE OSTI2020

Designing an impedance-based technique for studying corrosion on cladding materials

In nuclear cladding materials, changes in structure and chemistry over the lifetime of claddings can have a significant impact on fuel and reactor performance. Currently, chemical evolution over the lifetime of the fuel and cladding materials can only be inferred using post-irradiation examination. As an alternative to post-irradiation measurements, electrochemical impedance spectroscopy (EIS) can provide an in-pile measurement of changes in chemistry in oxides and hydrides. The objective of this study is to develop an impedance based sensing technology for measuring spatial- and time-resolved in-pile changes in cladding chemistry in order to understand the mechanisms of hydride formation, hydride dealloying and associated cladding corrosion so that a mitigation strategy and technology can be developed for reactor safe operation. Two prototype impedance-based sensors were designed and assembled using Zr-4 as working electrode and Pt as counter and reference electrode. One sensor is tubular type and the other one is plate-type. These sensors will be tested in static autoclave to investigate the effects of various environmental conditions (including temperature, pressure, and water chemistry) on cladding material corrosion mechanisms. These in-situ EIS sensors would provide corrosion-layer parameters in high-temperature water, such as (1) total layer thickness, (2) the evolution of oxide structures as uniform layer dielectric or sublayer(s), and (3) the possible electrochemistry of the volume corrosion process and the solid/liquid interface, such as the cyclic nature of corrosion of Zr alloys in high-temperature water. This work was focused on electrochemical sensor development, targeting cladding oxides/hydrides under thermodynamic equilibria; characterization of samples using Kelvin probe force microscopy (SKPFM), Raman spectroscopy, and scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS) to provide high-resolution co-localized characterization of zirconium oxide and metal/oxide interface.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Design and optimization of a high-energy optical parametric amplifier for broadband, spectrally incoherent pulses

Spectrally incoherent laser pulses with sufficiently large fractional bandwidth are in demand for the mitigation of laser–plasma instabilities occurring in high-energy laser–target interactions. Here, we modeled, implemented, and optimized a dual-stage high-energy optical parametric amplifier for broadband, spectrally incoherent pulses in the near-infrared. The amplifier delivers close to 400 mJ of signal energy through noncollinear parametric interaction of 100-nJ-scale broadband, spectrally incoherent seed pulses near 1053 nm with a narrowband high-energy pump operating at 526.5 nm. Mitigation strategies for high-frequency spatial modulations in the amplified signal caused by index inhomogeneities in the Nd:YLF rods of the pump laser are explored and discussed in detail.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Photoluminescence probing of light absorption centers at silica laser damage

We use photoluminescence (PL) imaging to study damage growth precursors within laser damage sites on the surface of silica. Damage site evolution is induced by multiple shots of UV nanosecond pulsed laser at various energy densities and monitored throughout the early stages of growth. Wide-field PL imaging rapidly locates microscopic light absorption centers within the silica damage site. Our quantitative analysis shows that damage sites with strong local PL intensity show a higher probability of growth upon subsequent laser pulses. Scanning electron microscopy (SEM) paired with a study of PL spectrum shows that the strong PL intensity appears from the subsurface fractures with high defect density, which provides a local light absorption center leading to significant damage growth. We believe that this result offers an efficient optical damage mitigation strategy by providing a rapid and non-destructive optical inspection approach.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantifying the effects of multiple land management practices, land cover change, and wildfire on the California landscape carbon budget with an empirical model

The effectiveness of land-based climate mitigation strategies is generally estimated on a case-by-case basis without considering interactions with other strategies or influencing factors. Here we evaluate a new, comprehensive approach that incorporates interactions among multiple management strategies, land use/cover change, wildfire, and climate, although the potential effects of climate change are not evaluated in this study. The California natural and working lands carbon and greenhouse gas model (CALAND) indicates that summing individual practice estimates of greenhouse gas impacts may underestimate emission reduction benefits in comparison with an integrated estimate. Annual per-area estimates of the potential impact of specific management practices on landscape emissions can vary based on the estimation period, which can be problematic for extrapolating such estimates over space and time. Furthermore, the actual area of implementation is a primary factor in determining potential impacts of management on landscape emissions. Nonetheless, less intensive forest management, avoided conversion to urban land, and urban forest expansion generally create the largest annual per-area reductions, while meadow restoration and forest fuel reduction and harvest practices generally create the largest increases with respect to no management. CALAND also shows that data uncertainty is too high to determine whether California land is a source or a sink of carbon emissions, but that estimating effects of management with respect to a baseline provides valid results. Important sources of this uncertainty are initial carbon density, net ecosystem carbon accumulation rates, and land use/cover change data. The appropriate choice of baseline is critical for generating valid results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Raccoon spatial ecology in the rural southeastern United States

The movement ecology of raccoons varies widely across habitats with important implications for the management of zoonotic diseases such as rabies. However, the spatial ecology of raccoons remains poorly understood in many regions of the United States, particularly in the southeast. To better understand the spatial ecology of raccoons in the southeastern US, we investigated the role of sex, season, and habitat on monthly raccoon home range and core area sizes in three common rural habitats (bottomland hardwood, upland pine, and riparian forest) in South Carolina, USA. From 2018–2022, we obtained 264 monthly home ranges from 46 raccoons. Mean monthly 95% utilization distribution (UD) sizes ranged from 1.05 ± 0.48 km 2 (breeding bottomland females) to 5.69 ± 3.37 km 2 (fall riparian males) and mean monthly 60% UD sizes ranged from 0.25 ± 0.15 km 2 (breeding bottomland females) to 1.59 ± 1.02 km 2 (summer riparian males). Males maintained home range and core areas ~2–5 times larger than females in upland pine and riparian habitat throughout the year, whereas those of bottomland males were only larger than females during the breeding season. Home ranges and core areas of females did not vary across habitats, whereas male raccoons had home ranges and core areas ~2–3 times larger in upland pine and riparian compared to bottomland hardwood throughout much of the year. The home ranges of males in upland pine and riparian are among the largest recorded for raccoons in the United States. Such large and variable home ranges likely contribute to elevated risk of zoonotic disease spread by males in these habitats. These results can be used to inform disease mitigation strategies in the southeastern United States.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Optimizing spin qubit coherence through materials codesign

The evolution of defect-based spin qubit systems is currently transitioning from fundamental studies and proof-of-concept demonstrations into applications in the burgeoning field of quantum technology. Within this context, new challenges emerge, in particular, the need to understand and engineer the fundamental materials that form the hardware building blocks critical for the scalability and wide-scale adoption of such technologies. While earlier discussions have often focused on qubits within idealized systems, major limitations on spin coherence and optical properties arise from effects imposed by the nonideality of the surrounding host matrix. Decoherence can stem from a variety of sources, including other qubits, nuclear spins, and parasitic point- and extended defects, which interact with the qubit via magnetic and electric fields, photons, phonons, and strain. In this article, we focus on the relevant sources and mechanisms through which decoherence occurs and provide potential mitigation strategies via the synergistic integration of first-principles simulations and materials synthesis and engineering. We aim to provide a tangible link between material properties and material functions thereby enabling materials-by-design.

ab initio simulations↗
DOE OSTI2020

Thermal dissipation sensors enter a new age: Navigating frontiers in transpiration and hydrologic function

Thermal dissipation (TD) sensors have been used extensively for a wide array of sap flow applications, and yet recently several limitations of this approach have been identified, including a tendency to underestimate absolute flows, especially in small stems with very high flows. However, when used properly, the TD method remains the most cost effective and reliable approach for determining relative flows in comparison studies. Major advantages of this method include the ability to replicate maximally within trees, across trees of different sizes, and across multiple species to capture the full range of natural variation in the study system. From early studies, such variation has been deemed a major challenge for representing stand-level dynamics. In a series of recent comparison studies, we determined mean sap flux density dynamics within and among groups of trees. These studies were conducted within temperate coniferous forest (n=46), temperate oak savanna (n=36), temperate pine forest (n=49), temperate bottomland hardwood forest (n=23), tropical premontane rainforest (n=43), semiarid subtropical shrubland (n=25), and tropical dry forest (n=15), with a total of 237 sensors. Two study sites were also equipped with eddy covariance systems for determination of stand-level evapotranspiration. Sites subject to high background thermal gradients used the transient thermal dissipation method. Comparisons varied between studies but tended to focus on relative flow differences between species, between wetter and drier microsites, between understory and overstory components, or between wetter and drier periods of the growing season. Except for the shrubland sites, study trees tended to be at least 15-cm diameter and frequently exceeded 50-cm diameter. Results of this cross-site comparison highlight the inherent variation in natural stands and the importance of using large sample sizes. The TD approach remains a valuable and preferred method for the future determination of water use in trees. However, sensor replication can fundamentally impact a study's outcomes and should be more carefully considered in study design. Customized error mitigation strategies are best to address the sources of variation most problematic for a particular study. In conclusion, the development of “smart” calibration approaches that correct for fundamental effects of radial variation inherent in thermal dissipation studies is discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Evaluating the Aftershock Duration of Induced Earthquakes

As the carbon sequestration community prepares to scale up the number and size of commercial operations, the need for tools and methods to assess and mitigate risks associated with these operations becomes increasingly important. One outstanding question is whether aftershocks of induced events decay quickly after injection operations cease or if aftershock activity persists for hundreds of years before returning to background levels more akin to tectonic events (Stein and Liu, 2009). Appropriate estimates of the aftershock duration impact several operational management decisions including mitigation strategies and post-injection monitoring for seismic activity. It is hypothesized that induced earthquake rates may diminish more quickly after injection is stopped, owing to higher stressing rates from injected fluids. Alternatively, it is plausible that only the first event in the sequence is induced by increased fluid overpressures, whereas subsequent events (e.g., aftershocks) respond to the stored tectonic stresses and static and dynamic stress changes due to the mainshock (Keranen et al., 2013). Further, if the aftershock duration can be linked to stressing rates due to injection, then it follows that operational strategies to reduce seismic hazard by reducing injection rates or volumes may be successful. However, if aftershocks of induced events are relieving stored tectonic stresses, then altering injection volumes may not alleviate ongoing seismic activity. Furthermore, knowledge of an aftershock duration could aid in the determination of an appropriate post-injection monitoring period for ongoing seismicity, which is a factor in overall operational costs. In this study, we model induced seismicity sequences in Oklahoma with a coupled Coulomb rate–state earthquake rate model (Dieterich, 1994; Kroll et al., 2017) to estimate aftershocks durations. Results for the current study indicate that elevated rates of aftershock activity following induced mainshocks return to background seismicity rates in less than five years, contrary to the tens to hundreds of years observed for tectonic aftershocks.

58 GEOSCIENCES↗
DOE OSTI2023

Modeling-Based Assessment of Deep Seismic Potential Induced by Geologic Carbon Storage

Induced seismicity is an inherent risk associated with geologic carbon storage (GCS) in deep rock formations that could contain undetected faults prone to failure. Modeling-based risk assessment has been implemented to quantify the potential of injection-induced seismicity, but typically simplified multiscale geologic features or neglected multiphysics coupled mechanisms because of the uncertainty in field data and computational cost of field-scale simulations, which may limit the reliable prediction of seismic hazard caused by industrial-scale CO 2 storage. The degree of lateral continuity of the stratigraphic interbedding below the reservoir and depth-dependent fault permeability can enhance or inhibit pore-pressure diffusion and corresponding poroelastic stressing along a basement fault. In conclusion, this study presents a rigorous modeling scheme with optimal geological and operational parameters needed to be considered in seismic monitoring and mitigation strategies for safe GCS.

58 GEOSCIENCES↗
DOE OSTI2023

ORION: Operational FoRecastIng Of INduced Seismicity

The Operational Forecasting of Induced Seismicity toolkit “ORION” (ORION) is an open-source, observation-based ensemble forecasting toolkit which is geared towards helping operators understand the seismic hazard (i.e., probabilistic assessment of the magnitude and frequency of induced seismic events) at a site. ORION analyzes how the seismic hazard evolves during injection and suggests possible mitigation strategies to employ if an earthquake that exceeds certain threshold is observed. Through its ensemble modeling approach, ORION leverages the benefits of statistical-, physics-, and machine learning-based forecasting methodologies, while reducing the impact of each model’s respective limitations. The ORION toolkit consists of an easy-to-use GUI interface that affords a user as much or as little interaction as desired. Advanced capabilities allow the user to upload local, high-precision earthquake catalogs, projected injection profiles and/or spatiotemporal estimates of pressure/stress, and to tune various model parameters. ORION will then provide a spatial and temporal ensemble forecast of seismicity defined as the probability of exceedance of a given earthquake magnitude over a forecast period. Additionally, ORION will provide probability distribution of the statistically derived maximum possible earthquake magnitude that may be expected. Finally, ORION will provide suggested operational management strategies (e.g. reduce injection volumes at specific wells) based on the level of hazard.

NRAP,NRAP Tools,ORION,Operational Forcasting of In↗