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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Seismic Ambient Noise Analyses Reveal Changing Temperature and Water Signals to 10s of Meters Depth in the Critical Zone

The critical zone sustains terrestrial life, but we have few tools to explore it efficiently beyond the first few meters of the subsurface. Using analyses of high-frequency ambient seismic noise from densely spaced seismometers deployed in the forested Shale Hills subcatchment of the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO), we show that temporal changes in seismic velocities at depths from ~1 m to tens of m can be detected. Furthermore, these changes are driven by variations at the land surface. The Moving-Window Cross-Spectral (MWCS) method was employed to measure seismic-velocity changes in coda waves at hourly resolution in 10 different frequency bands. We observed a diurnal signal, a seasonal signal, and a meteorological-event-based signal. These signals were compared to time-series measurements of precipitation, well water levels, soil moisture, soil temperature, air temperature, latent heat flux, and air pressure in the heavily instrumented catchment. Most of the velocity changes can be explained by variations in temperature that result in thermoelastic strains that propagate to depth. But some double minima in seismic velocity time-series observed after large rain events were attributed in part to the effects of water infiltration. These results show that high-frequency ambient noise data may in some locations be used to detect changes in the critical zone from ~1 to ~100 m or greater depth with hourly resolution. But interpretation of such data requires multiple environmental data sets to deconvolve the complex interrelationships among thermoelastic and hydrological effects in the subsurface critical zone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Three-dimensional gravity modeling and focusing inversion using rectangular meshes.

Rectangular grid cells are commonly used for the geophysical modeling of gravity anomalies, owing to their flexibility in constructing complex models. The straightforward handling of cubic cells in gravity inversion algorithms allows for a flexible imposition of model regularization constraints, which are generally essential in the inversion of static potential field data. The first part of this paper provides a review of commonly used expressions for calculating the gravity of a right polygonal prism, both for gravity and gradiometry, where the formulas of Plouff and Forsberg are adapted. The formulas can be cast into general forms practical for implementation. In the second part, a weighting scheme for resolution enhancement at depth is presented. Modelling the earth using highly digitized meshes, depth weighting schemes are typically applied to the model objective functional, subject to minimizing the data misfit. The scheme proposed here involves a non-linear conjugate gradient inversion scheme with a weighting function applied to the non-linear conjugate gradient scheme's gradient vector of the objective functional. The low depth resolution due to the quick decay of the gravity kernel functions is counteracted by suppressing the search directions in the parameter space that would lead to near-surface concentrations of gravity anomalies. Further, a density parameter transformation function enabling the imposition of lower and upper bounding constraints is employed. Using synthetic data from models of varying complexity and a field data set, it is demonstrated that, given an adequate depth weighting function, the gravity inversion in the transform space can recover geologically meaningful models requiring a minimum of prior information and user interaction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Thermal equation of state of lower-mantle ferropericlase across the spin crossover

The thermal equation of state of ferropericlase [(Mg{sub 0.75}Fe{sub 0.25})O] has been investigated by synchrotron X-ray diffraction up to 140 GPa and 2000 K in a laser-heated diamond anvil cell. Based on results at high pressure-temperature conditions, the derived phase diagram shows that the spin crossover widens at elevated temperatures. Along the lower-mantle geotherm, the spin crossover occurs between 1700 km and 2700 km depth. Compared to the high-spin state, thermoelastic modeling of the data shows a {approx}1.2% increase in density, a factor of two increase in thermal expansion coefficient over a range of 1000 km, and a maximum decrease of 37% and 13% in bulk modulus and bulk sound velocity, respectively, at {approx}2180 km depth across the spin crossover. These anomalous behaviors in the thermoelastic properties of ferropericlase across the spin crossover must be taken into account in order to understand the seismic signatures and geodynamics of the lower mantle.

58 GEOSCIENCES↗
DOE OSTI2017

Subsurface Temperature, Moisture, Thermal Conductivity and Heat Flux, Area A, B, C, and D, Utqiagvik (Barrow), Alaska, Ongoing from 2012

Subsurface soil monitoring stations are located across each of the four NGEE Arctic, Utqiagvik (Barrow), Alaska, intensive study Areas: (A) low-centered polygons with well-defined troughs, (B) high-centered polygons, (C) flat-centered or transitional polygons, and (D) inundated low-centered polygons with no troughs. Two data products are being generated - both with high temporal (hourly) and spatial resolution (within polygon and with depth): (1) soil profile temperature measurements and (2) active layer and permafrost temperature and moisture measurements from instrumented soil pits. This data package currently contains seventy-nine .csv files, one .xlsx (with complimentary .csv file), and one .pdf. Data from September 2012 through August 2022 are available as annual files. Additional data will be added annually after quality checks have been completed. These data are being collected to better understand the thermal dynamics of the active layer and permafrost. Together this information serves as calibration and evaluation data for fine-scale simulations of polygons. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Soil and groundwater environmental sensor data, Wax Lake Delta, Louisiana, March 2023 - March 2024

This study evaluates how environmental parameters that integrate biogeochemical processes vary with water table fluctuations in the freshwater Wax Lake Delta (WLD) in Louisiana, U.S.A. This data package contains seven *.csv files and one Excel file that compiles all the data from the individual .csv files. This dataset reports high frequency (15-min) observations of water level, soil redox potential, specific conductance, and pH made for one year along elevation transects located on the older, proximal (OT) and younger, distal (YT) ends of a deltaic island. Water depth relative to the ground surface (cm; HOBO U20L-04; error ± 0.4 cm), water pH and temperature (HOBO MX2501), and specific conductance and temperature (HOBO U24-001) sensors were installed in March 2023. Water depth was corrected for barometric pressure recorded by a separate logger secured to a platform above the highest water level. Soil redox probes (SWAP ORP-40-4-B) were also installed in March 2023. Each probe had four Pt sensors (2 mm width) placed at 10 cm, 20 cm, 30 cm, and 40 cm below the ground surface. Redox data were referenced to an external Ag0/AgCl (3M KCl) reference probe placed in saturated ground and recorded on CR1000X dataloggers (Campbell Scientific) powered by solar panels. A second reference probe was positioned near the primary reference probe for backup and data correction. The tops of the soil redox probes and soil moisture probes were flush with the soil surface so that sensors are reported at their indicated depths below ground surface. Here, we report data collected between 15 March 2023 to 15 March 2024 for all sensors, with some differences due to exact dates of sensor placement or data gaps associated with sensor malfunction. For example, water depth at OT4 was not recorded between March to November 2023. Data flags indicate whether a value is valid (1) or was excluded from data analysis in the associated manuscript (-1).

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2016

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.

compartmentalized metabolic network↗
DOE OSTI2017

Final Scientific/Technical Report of Gas Hydrate Dynamics on the Alaskan Beaufort Continental Slope: Modeling and Field Characterization

Methane Hydrates, a solid form of methane and water, exist at high pressures and low temperatures, occurs on every continental margin on Earth, represents one of the largest reservoirs of carbon on the planet, and, if destabilized, may play an important role in both slope stability and climate change. For decades, researchers have studied methane hydrates with the hope of determining if methane hydrates are destabilizing, and if so, how this destabilization might impact slope stability and ocean/atmosphere carbon budgets. In the past ~5 years, it has become well established that the upper “feather-edge” of methane hydrate stability (intermediate water depths of ~200-500 meters below sea level) represents an important frontier for methane hydrates stability research, as this zone is most susceptible to destabilization due to minor fluctuations in ocean temperature in space and time. The Arctic Ocean—one of the fastest warming regions on Earth—is perhaps the best place to study possible changes to methane hydrate stability due to ocean warming. To address the stability of methane hydrates at intermediate ocean depths, Southern Methodist University in partnership with Oregon State University and The United State Geological Survey at Woods Hole began investigating methane hydrate stability in intermediate water depths below both the US Beaufort Sea and the Atlantic Margin, from 2012-2017. The work was funded by the Department of Energy’s (DOE) National Energy Technology Laboratory (NETL). The key goal of the SMU component of this study was to collect the first ever heat flow data in the Beaufort Sea and compare measured shallow (probe-based1) heat flow values with deeper (BSR-derived2) heat flow values, and from this, determine whether hydrates were in thermal equilibrium. In September 2016, SMU/OSU collected the first ever heat flow measurements in the US Beaufort Sea. Despite poor weather and rough seas, the cruise was a success, with 116 heat flow measurements acquired across the margin, spanning 4 transects separated by more than 400 km. Useable heat flow data exists for 97% (113) of probe heat flow measurements, revealing a clear picture of regional heat flow across the basin. During the past 8 months since the cruise, SMU researchers have processed the heat flow and thermal conductivity measurements and compared results to deeper heat flow estimates obtained from seismic data. The analysis reveals clear, consistent trends: All probe heat flow measurements in depths greater than 800 mbsl are consistent with BSR-derived values; heat flow measurements obtained in water depths between ~250-750 mbsl are systematically lower than those estimated from BSRs; and heat flow estimates in water depths shallower than ~250 mbsl are systematically warmer than deeper estimates. The consistency between shallow (probe) and deep (BSR) heat flow measurements at depths greater than ~750 m where ocean temperature changes are minimal supports the premise that the hydrates consist primarily of methane and represent a valuable tool for estimating heat flow. The anomalous cooling trend observed in the upper 250 m is consistent with expected seasonal effects observed in shallow ocean buoy measurements in the arctic, when cold, less dense melting sea ice cools the upper 200 m of the ocean during the summer as ice melting occurs. The discrepancy in heat flow at intermediate water depths is best explained via recent intermediate ocean temperature warming, where long-term (annual or longer) warming intermediate ocean bottom waters result in an anomalously low heat flow in shallow heat flow measurements. Using the characteristic 1D time-length scale for diffusion, we estimate that ocean temperature warming began no later than ~1200 years ago but arguably much more recently as results are limited by seismic resolution. More importantly, our analysis indicates methane hydrate is destabilizing not only in the upper feather edge (200-500 mbsl) but at depths as great as 750 mbsl. The intermediate ocean warming rate supports previous studies suggesting geologically rapid warming (>0.1 deg C/decade) at intermediate ocean depths in the Beaufort Sea. Assuming no further changes or additional warming, our analysis indicates methane hydrates will destabilize at seafloor depths shallower than 750 mbsl in the Beaufort Sea within the next ~3000 years. 1 Probe outfitted with sensors inserted into the seafloor sediment 2 Bottom-simulating reflector (BSR) seismic data indicates presence of hydrate deposits

58 GEOSCIENCES↗
DOE OSTI2014

On the determination of reference levels for quality assurance of flattening filter free photon beams in radiation therapy

Purpose: New definitions for some dosimetric parameters for use in quality assurance of flattening filter free (FFF) beams generated by medical linear accelerators have been suggested. The present study aims to validate these suggestions and to propose possible reference levels. Methods: The main characteristics of FFF photon beams were described in terms of: field size, penumbra, unflatness, slope, and peak-position parameters. Data were collected for 6 and 10 MV-FFF beams from three different Varian TrueBeam Linacs. Measurements were performed with a 2D-array (Starcheck system from PTW-Freiburg) and with the portal dosimetry method GLAaS utilizing the build-in portal imager of TrueBeam. Data were also compared to ion chamber measurements. A cross check validation has been performed on a FFF beam of 6 MV generated by a Varian Clinac-iX upgraded to FFF capability. Results : All the parameters suggested to characterize the FFF beams resulted easily measurable and little variation was observed among different Linacs. Referring to two reference field sizes of 10 × 10 and 20 × 20 cm{sup 2}, at SDD = 100 cm and d = dmax, from the portal dosimetry data, the following results (averaging X and Y profiles) were obtained. Field size: 9.95 ± 0.02 and 19.98 ± 0.03 cm for 6 MV-FFF (9.94 ± 0.02 and 19.98 ± 0.03 cm for 10 MV-FFF). Penumbra: 2.7 ± 0.3 and 2.9 ± 0.3 mm for 6 MV-FFF (3.1 ± 0.2 and 3.3 ± 0.3 for 10 MV-FFF). Unflatness: 1.11 ± 0.01 and 1.25 ± 0.01 for 6 MV-FFF (1.21 ± 0.01 and 1.50 ± 0.01 for 10 MV-FFF). Slope: 0.320 ± 0.020%/mm and 0.43 ± 0.015%/mm for 6 MV-FFF (0.657 ± 0.023%/mm and 0.795 ± 0.017%/mm for 10 MV-FFF). Peak Position −0.2 ± 0.2 and −0.4 ± 0.2 mm for 6 MV-FFF (−0.3 ± 0.2 and 0.7 ± 0.3 mm for 10 MV-FFF). Results would depend upon measurement depth. With thresholds set to at least 95% confidence level from the measured data and to account for possible variations between detectors and methods and experimental settings, a tolerance set of: 1 mm for field size and penumbra, 0.04 for unflatness, 0.1%/mm for slope, and 1 mm for peak position could be proposed from our data. Conclusions : The parameters proposed for the characterization and routine control of stability of profiles of FFF beams appear to be a viable solution with a strong similarity to the conventional parameters used for flattened beams. The results from three different TrueBeams and the cross-validation against a Clinac-iX suggested the possible generalization of the methods and the possibility to use common tolerances for the parameters. The data showed also the reproducibility of beam characteristics among different systems (of the same vendor) and the resulting parameter values could therefore be possibly generalized.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

SU-E-T-561: Development of Depth Dose Measurement Technique Using the Multilayer Ionization Chamber for Spot Scanning Method

Purpose: To develop a measurement technique which suppresses the difference between profiles obtained with a multilayer ionization chamber (MLIC) and with a water phantom. Methods: The developed technique multiplies the raw MLIC data by a correction factor that depends on the initial beam range and water equivalent depth. The correction factor is derived based on a Bragg curve calculation formula considering range straggling and fluence loss caused by nuclear reactions. Furthermore, the correction factor is adjusted based on several integrated depth doses measured with a water phantom and the MLIC. The measured depth dose profiles along the central axis of the proton field with a nominal field size of 10 by 10 cm were compared between the MLIC using the new technique and the water phantom. The spread out Bragg peak was 20 cm for fields with a range of 30.6 cm and 6.9 cm. Raw MLIC data were obtained with each energy layer, and integrated after multiplying by the correction factor. The measurements were performed by a spot scanning nozzle at Nagoya Proton Therapy Center, Japan. Results: The profile measured with the MLIC using the new technique is consistent with that of the water phantom. Moreover, 97% of the points passed the 1% dose /1mm distance agreement criterion of the gamma index. Conclusion: We have demonstrated that the new technique suppresses the difference between profiles obtained with the MLIC and with the water phantom. It was concluded that this technique is useful for depth dose measurement in proton spot scanning method.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

SU-F-T-73: Experimental Determination of the Effective Point of Measurement in Electron Beams Using a Commercial Scintillation Detector

Purpose: The aim of this work was to determine experimentally the effective point of measurement (EPOM) in clinical electron beams for three cylindrical ionization chambers using a commercial scintillation detector as a reference detector. Methods: Percent depth dose (PDD) curves were measured using an Exradin W1 scintillation detector and were used as a representative PDD to water. Depth dose curves were measured with the Exradin A18, A1SL, and A28 ionization chambers. The raw ionization chamber curve data were corrected by the chamber fluence perturbation correction factor and restricted mass collisional stopping power ratio at each depth to obtain a percent depth dose curve to the gas volume (PDDGV) of the detector. Ratios of the W1 PDD to the ion chamber PDDGV were calculated for each measurement depth. The W1 PDD curve was shifted by small depth increments, Δz, until the ratio of the W1 PDD to the ion chamber PDDGV was depth-independent (optimal Δz). A MATLAB routine was developed to determine the optimal Δz value. Results: The optimal Δz shift was used as an estimate of the EPOM for each chamber. The average calculated EPOM shifts (expressed as a fraction of the chamber cavity radius) for the A18, A1SL, and A28 ionization chambers were 0.21 ± 0.04, 0.10 ± 0.05, and 0.22 ± 0.03, respectively. Conclusion: The experimentally determined EPOM values for the A18 and A1SL in this work agreed with the simulated values of Muir and Rogers (MedPhys 2014). The results also indicate that the Exradin W1 scintillator is water equivalent for electron energies of 6 MeV, 9 MeV, 12 MeV, and 16 MeV. In addition, we confirmed that the AAPM TG51 recommended EPOM shift of 0.5 times the cavity radius is not accurate for the A18 and A1SL chambers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Development of a 3D optical scanning-based automatic quality assurance system for proton range compensators

Purpose: A new automatic quality assurance (AutoRCQA) system using a three-dimensional scanner (3DS) with system automation was developed to improve the accuracy and efficiency of the quality assurance (QA) procedure for proton range compensators (RCs). The system performance was evaluated for clinical implementation. Methods: The AutoRCQA system consists of a three-dimensional measurement system (3DMS) based on 3DS and in-house developed verification software (3DVS). To verify the geometrical accuracy, the planned RC data (PRC), calculated with the treatment planning system (TPS), were reconstructed and coregistered with the measured RC data (MRC) based on the beam isocenter. The PRC and MRC inner surfaces were compared with composite analysis (CA) using 3DVS, using the CA pass rate for quantitative analysis. To evaluate the detection accuracy of the system, the authors designed a fake PRC by artificially adding small cubic islands with side lengths of 1.5, 2.5, and 3.5 mm on the inner surface of the PRC and performed CA with the depth difference and distance-to-agreement tolerances of [1 mm, 1 mm], [2 mm, 2 mm], and [3 mm, 3 mm]. In addition, the authors performed clinical tests using seven RCs [computerized milling machine (CMM)-RCs] manufactured by CMM, which were designed for treating various disease sites. The systematic offsets of the seven CMM-RCs were evaluated through the automatic registration function of AutoRCQA. For comparison with conventional technique, the authors measured the thickness at three points in each of the seven CMM-RCs using a manual depth measurement device and calculated thickness difference based on the TPS data (TPS-manual measurement). These results were compared with data obtained from 3DVS. The geometrical accuracy of each CMM-RC inner surface was investigated using the TPS data by performing CA with the same criteria. The authors also measured the net processing time, including the scan and analysis time. Results: The AutoRCQA system accurately detected all fake objects in accordance with the given criteria. The median systematic offset of the seven CMM-RCs was 0.08 mm (interquartile range: −0.25 to 0.37 mm) and −0.08 mm (−0.58 to 0.01 mm) in the X- and Y-directions, respectively, while the median distance difference was 0.37 mm (0.23–0.94 mm). The median thickness difference of the TPS-manual measurement at points 1, 2, and 3 was −0.4 mm (−0.4 to −0.2 mm), −0.2 mm (−0.3 to 0.0 mm), and −0.3 mm (−0.6 to −0.1 mm), respectively, while the median difference of 3DMS was 0.0 mm (−0.1 to 0.2 mm), 0.0 mm (−0.1 to 0.3 mm), and 0.1 mm (−0.1 to 0.2 mm), respectively. Thus, 3DMS showed slightly better values compared to the manual measurements for points 1 and 3 in statistical analysis (p < 0.05). The average pass rate of the seven CMM-RCs was 97.97% ± 1.68% for 1-mm CA conditions, increasing to 99.98% ± 0.03% and 100% ± 0.00% for 2- and 3-mm CA conditions, respectively. The average net analysis time was 18.01 ± 1.65 min. Conclusions: The authors have developed an automated 3DS-based proton RC QA system and verified its performance. The AutoRCQA system may improve the accuracy and efficiency of QA for RCs.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Alabama Carbon Storage: Bringing Data to the People

The Gulf Coastal Plain of Alabama has proven potential for geologic carbon storage and current interest in the area for large carbon capture and storage (CCS) projects is high. Extensive CCS relevant data exist in the records of the Geological Survey of Alabama and State Oil and Gas Board of Alabama, however, most of this data is not publicly available or is scattered in separate databases, file cabinets, and tables in publications. The “Alabama Carbon Storage: Data Sharing and Engagement” (ACS-DSE) project seeks to accelerate the responsible development of large CCS projects in the Gulf Coastal Plain of Alabama and offshore in state waters through a publicly accessible database of geologic carbon storage models and data across the region. The ACS-DSE draws on the over 150 years of geologic research and over 20 years of experience in CCS research to place relevant geologic, geophysical, and infrastructure data on a single web platform. Datasets available will include formation depths and elevations, geologic structures, reservoir properties, digital well logs (LAS files), existing penetrations, and geologic models. In addition to downloadable datasets, links to CCS related regulatory agencies and other sources of information will be included (for example, Class VI UIC permitting regulations and pipeline regulations). By making these datasets and models available in commonly used formats on a public website, the project will increase transparency in decision making and decrease the data acquisition time for industry.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Depth of nutrient uptake by deep-rooted plants is regulated by water availability

The capacity of some plants to access water and nutrients at depths greater than one meter is a critical functional trait that confers resistance to drought and impacts both belowground and shallow soil processes. Here, we report water and strontium isotopic data from an alpine meadow transect showing the correlation between water and nutrient acquisition depths. The isotopic compositions of Sr ( 87 Sr/ 86 Sr ratio) and water in rock and soil, and in plant leaf tissues, reveal that deeper-rooted plants acquire a higher proportion of water, Sr, and cation nutrients that are derived from the saprolite, a zone of silicate weathering, than shallow-rooted grass. A three-decade dendrochemical record reveals that reductions of wet precipitation drive deep-rooted plants to acquire cation nutrients from deeper saprolite or bedrock regions. Thus, the depth of cation nutrient acquisition by deep-rooted plant species at this site is tightly coupled with, and likely determined by, water availability in soil, saprolite, and bedrock. The enhanced uptake of cations as well as water from deeper saprolite zones could impact the rate of bedrock weathering and watershed chemistry during drought.

biogeochemistry↗
DOE OSTI2021

High resolution depth profiling using near-total-reflection hard x-ray photoelectron spectroscopy

By adjusting the incidence angle of incoming x rays near the critical angle of x-ray total reflection, photoelectron intensity is strongly modulated due to the variation of x-ray penetration depth. Photoelectron spectroscopy combined with near-total reflection exhibits tunable surface sensitivity, providing depth-resolved information. In this Review, we first describe the experimental setup and specific data analysis process. We then review three different examples that show the broad application of this method. Furthermore, the emphasis is on its applications correlated to oxide heterostructures, especially quantitative depth analyses of compositions and electronic states. In the last part, we discuss the limitations of this technique, mostly in terms of the range of samples that can be studied.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Integrated Reflection Seismic Monitoring and Reservoir Modeling for Geologic CO 2 Sequestration

The US DOE/NETL CCS MVA program funded a project with Fusion Petroleum Technologies Inc. (now SIGMA) to model the proof of concept of using sparse seismic data in the monitoring of CO 2 injected into saline aquifers. The goal of the project was to develop and demonstrate an active source reflection seismic imaging strategy based on deployment of spatially sparse surface seismic arrays. The primary objective was to test the feasibility of sparse seismic array systems to monitor the CO 2 plume migration injected into deep saline aquifers. The USDOE/RMOTC Teapot Dome (Wyoming) 3D seismic and reservoir data targeting the Crow Mountain formation was used as a realistic proxy to evaluate the feasibility of the proposed methodology. Though the RMOTC field has been well studied, the Crow Mountain as a saline aquifer has not been studied previously as a CO 2 sequestration (storage) candidate reservoir. A full reprocessing of the seismic data from field tapes that included prestack time migration (PSTM) followed by prestack depth migration (PSDM) was performed. A baseline reservoir model was generated from the new imaging results that characterized the faults and horizon surfaces of the Crow Mountain reservoir. The 3D interpretation was integrated with the petrophysical data from available wells and incorporated into a geocellular model. The reservoir structure used in the geocellular model was developed using advanced inversion technologies including Fusion's ThinMAN™ broadband spectral inversion. Seal failure risk was assessed using Fusion's proprietary GEOPRESS™ pore pressure and fracture pressure prediction technology. CO 2 injection was simulated into the Crow Mountain with a commercial reservoir simulator. Approximately 1.2MM tons of CO 2 was simulated to be injected into the Crow Mountain reservoir over 30 years and subsequently let 'soak' in the reservoir for 970 years. The relatively small plume developed from this injection was observed migrating due to gravity to the apexes of the double anticline in the Crow Mountain reservoir of the Teapot dome. Four models were generated from the reservoir simulation task of the project which included three saturation models representing snapshots at different times during and after simulated CO 2 injection and a fully saturated CO 2 fluid substitution model. The saturation models were used along with a Gassmann fluid substitution model for CO 2 to perform fluid volumetric substitution in the Crow Mountain formation. The fluid substitution resulted in a velocity and density model for the 3D volume at each saturation condition that was used to generate a synthetic seismic survey. FPTI's (Fusion Petroleum Technologies Inc.) proprietary SeisModelPRO™ full acoustic wave equation software was used to simulate acquisition of a 3D seismic survey on the four models over a subset of the field area. The simulated acquisition area included the injection wells and the majority of the simulated plume area.

02 PETROLEUM↗
DOE OSTI2025

An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics

Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hydrodynamic models (RHMs) or data-driven models with formidable training costs, whereas data-driven modeling of flow velocity has rarely been explored. Here, using the hybrid Low-fidelity, Spatial analysis, and Gaussian process learning (LSG) model, we developed a downscaling approach to construct high-resolution flow depth and velocity from a two-dimensional (2-D) RHM simulation at coarse resolution. The LSG models were trained and tested in an urban watershed in Houston using two different hurricane-driven flood events. The high-resolution (as fine as 30 m resolution) and low-resolution (mostly 1000 m resolution) meshes include 664 724 and 14 536 grid cells, respectively. The results showed that through downscaling, the simulation errors were reduced to less than one-fourth and one-third of the errors of the low-resolution 2-D RHM for flow depth and velocity, respectively. Our analysis further revealed that the dominant uncertainty sources of the downscaled hydrodynamics are different, with flow velocity dominated by the dimensionality reduction error, which we reduced by using a regionalized training procedure. The downscaling approach achieves an 84-fold acceleration in computational time compared to the high-resolution 2-D RHM, making high-fidelity ensemble flood modeling feasible. More importantly, the developed method provides an opportunity to couple large-scale hydrodynamical processes with local physical, chemical, and biological processes in river models.

Tan, Zeli [Pacific Northwest National Laboratory (↗
DOE OSTI2023

Renewable Energy Potential Model: Geothermal Supply Curves

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. The included paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results provided here should be considered with care due to the high uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for three scenarios: two hydrothermal (3.5km depth, USGS heat flow & SMU temperatures respectively) and one EGS (4.5km depth, SMU temperatures). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

15 GEOTHERMAL ENERGY↗