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DOE OSTI2024

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Analyzing LF/VLF Lightning Waveforms to Estimate D-region Electron Density Profiles

Lightning waveforms in the low frequency (LF; 30-300 kHz) and the very low frequency (VLF; 3-30 kHz) bands can be exploited to produce data-driven ionospheric D-region electron density profile (EDP) estimates with significantly higher spatial and temporal coverage than previously available. The lightning waveforms used in this paper are signals detected in the LF/VLF of negative cloud-to-ground lightning by the Earth Networks Total Lightning Detection Network. Each waveform contains a ground wave and a time-delayed ionospheric reflection. The time delay between the ground wave and ionospheric reflection has previously been used to estimate a single specular reflection altitude, where LF/VLF emissions are reflected by the ionosphere. Here, we expand upon previous methods to include filtering and spectral analysis, and account for oblique propagation to produce higher-order estimates for reflection altitudes and corresponding electron densities. Once estimated, reflection altitudes and corresponding electron densities can be used to derive parameters β and h’, which define an EDP for the D-region. In this study, the lightning waveform (LW) analysis is demonstrated using a single representative 24-hour dataset over the Southeast United States, and then extended to a total of 10 separate datasets with varying locations and ionospheric conditions. The LW-derived D-region EDPs are in agreement with predictions made by the Faraday International Reference Ionosphere model, and the LW EDPs β and h’ values are consistent with previous LF/VLF-derived estimates.

D-region ionosphere↗

DOE OSTI2025

Tree-level carbon stock estimations across diverse species using multi-source remote sensing integration

Forests are critical carbon sinks, and remote sensing has been increasingly widely used for forest monitoring and biomass estimations. However, species-specific tree-level studies remain limited. In this study, we demonstrated the feasibility of integrating UAV-based LiDAR with high-resolution optical satellite imagery (0.5 m) to estimate biomass for individual trees across different species. The proposed method accurately estimated biomass for 53 trees (R² = 0.82, rRMSE = 0.44), with species-specific datasets, showing an average 25.2% increase in R² and a 14.8% reduction in rRMSE. A novel vegetation index combining forest structure parameters with vegetation indices (VIs) was developed using high-resolution multispectral satellite data (3 m) to explore its relationship with individual tree biomass. Combining forest structural parameters with VIs further improved estimation accuracy, achieving an R²of 0.89 and an rRMSE of 0.34. Species-specific datasets show an 11.6% increase in R²compared to methods without VIs, and a 22.2% improvement over methods using only VIs. SHapley Additive exPlanations (SHAP) analysis shows that the volume feature played a key role in model performance and remained stable throughout the training process. Altogether, the proposed approach enhances individual tree biomass and carbon sink estimations, showing great potential for large-scale precise forest carbon monitoring using multi-source remote sensing data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Assessing the ability of $\mathrm{MODIS}$ $\mathrm{EVI}$ to estimate terrestrial ecosystem gross primary production of multiple land cover types

Terrestrial ecosystem gross primary production (GPP) is the largest component in the global carbon cycle. The enhanced vegetation index (EVI) has been proven to be strongly correlated with annual GPP within several biomes. However, the annual GPP-EVI relationship and associated environmental regulations have not yet been comprehensively investigated across biomes at the global scale. In this report we explored relationships between annual integrated EVI (iEVI) and annual GPP observed at 155 flux sites, where GPP was predicted with a log-log model: 1n(GPP) = a x 1n(iEVI) + b. iEVI was computed from MODIS monthly EVI products following removal of values affected by snow or cold temperature and without calculating growing season duration. Through categorisation of flux sites into 12 land cover types, the ability of iEVI to estimate GPP was considerably improved (R 2 from 0.62 to 0.74, RMSE from 454.7 to 368.2 g C m -2 yr -1 ). The biome-specific GPP-iEVI formulae generally showed a consistent performance in comparison to a global benchmarking dataset (R 2 = 0.79, RMSE = 387.8 g C m -2 yr -1 ). Specifically, iEVI performed better in cropland regions with high productivity but poorer in forests. The ability of iEVI in estimating GPP was better in deciduous biomes (except deciduous broadleaf forest) than in evergreen due to the large seasonal signal in iEVI in deciduous biomes. Likewise, GPP estimated from iEVI was in a closer agreement to global benchmarks at mid and high-latitudes, where deciduous biomes are more common and cloud cover has a smaller effect on remote sensing retrievals. Across biomes, a significant and negative correlation (R 2 = 0.37, p < 0.05) was observed between the strength (R 2 ) of GPP-iEVI relationships and mean annual maximum leaf area index (LAI max ), and the relationship between the strength and mean annual precipitation followed a similar trend. LAI max also revealed a scaling effect on GPP-iEVI relationships. Our results suggest that iEVI provides a very simple but robust approach to estimate spatial patterns of global annual GPP whereas its effect is comparable to various light-use-efficiency and data-driven models. The impact of vegetation structure on accuracy and sensitivity of EVI in estimating spatial GPP provides valuable clues to improve EVI-based models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Health impact assessment of air pollution using a dynamic exposure profile: Implications for exposure and health impact estimates

In both ambient air pollution epidemiology and health impact assessment an accurate assessment of the population exposure is crucial. Although considerable advances have been made in assessing human exposure outdoors, the assessments often do not consider the impact of individual travel behavior on such exposures. Population-based exposures to NO{sub 2} and O{sub 3} using only home addresses were compared with models that integrate all time-activity patterns-including time in commute-for Flanders and Brussels. The exposure estimates were used to estimate the air pollution impact on years of life lost due to respiratory mortality. Health impact of NO{sub 2} using an exposure that integrates time-activity information was on average 1.2% higher than when assuming that people are always at their home address. For ozone the overall estimated health impact was 0.8% lower. Local differences could be much larger, with estimates that differ up to 12% from the exposure using residential addresses only. Depending on age and gender, deviations from the population average were seen. Our results showed modest differences on a regional level. At the local level, however, time-activity patterns indicated larger differences in exposure and health impact estimates, mainly for people living in more rural areas. These results suggest that for local analyses the dynamic approach can contribute to an improved assessment of the health impact of various types of pollution and to the understanding of exposure differences between population groups. - Highlights: Black-Right-Pointing-Pointer Exposure to ambient air pollution was assessed integrating population mobility. Black-Right-Pointing-Pointer This dynamic exposure was integrated into a health impact assessment. Black-Right-Pointing-Pointer Differences between the dynamic and residential exposure were quantified. Black-Right-Pointing-Pointer Modest differences in health impact were found at a regional level. Black-Right-Pointing-Pointer At municipal level larger differences were found, influenced by gender and age.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Life cycle assessment based environmental impact estimation model for pre-stressed concrete beam bridge in the early design phase

The late rise in global concern for environmental issues such as global warming and air pollution is accentuating the need for environmental assessments in the construction industry. Promptly evaluating the environmental loads of the various design alternatives during the early stages of a construction project and adopting the most environmentally sustainable candidate is therefore of large importance. Yet, research on the early evaluation of a construction project's environmental load in order to aid the decision making process is hitherto lacking. In light of this dilemma, this study proposes a model for estimating the environmental load by employing only the most basic information accessible during the early design phases of a project for the pre-stressed concrete (PSC) beam bridge, the most common bridge structure. Firstly, a life cycle assessment (LCA) was conducted on the data from 99 bridges by integrating the bills of quantities (BOQ) with a life cycle inventory (LCI) database. The processed data was then utilized to construct a case based reasoning (CBR) model for estimating the environmental load. The accuracy of the estimation model was then validated using five test cases; the model's mean absolute error rates (MAER) for the total environmental load was calculated as 7.09%. Such test results were shown to be superior compared to those obtained from a multiple-regression based model and a slab area base-unit analysis model. Henceforth application of this model during the early stages of a project is expected to highly complement environmentally friendly designs and construction by facilitating the swift evaluation of the environmental load from multiple standpoints. - Highlights: • This study is to develop the model of assessing the environmental impacts on LCA. • Bills of quantity from completed designs of PSC Beam were linked with the LCI DB. • Previous cases were used to estimate the environmental load of new case by CBR model. • CBR model produces more accurate estimations (7.09%) than other conventional models. • This study supports decision making process in the early stage of a new construction case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America

As South and Central American countries prepare for increased birth defects from Zika virus outbreaks and plan for mitigation strategies to minimize ongoing and future outbreaks, understanding important characteristics of Zika outbreaks and how they vary across regions is a challenging and important problem. We developed a mathematical model for the 2015/2016 Zika virus outbreak dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly available data provided by the Pan American Health Organization, using Approximate Bayesian Computation to estimate parameter distributions and provide uncertainty quantification. The model indicated that a country-level analysis was not appropriate for Colombia. We then estimated the basic reproduction number to range between 4 and 6 for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We estimated the reporting rate to be around 16% in El Salvador and 18% in Suriname with estimated total outbreak sizes of 73,395 and 21,647 people, respectively. The uncertainty in parameter estimates highlights a need for research and data collection that will better constrain parameter ranges.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

The Reservoir Temperature Estimator (RTEst): A multicomponent geothermometry tool

The Reservoir Temperature Estimator (RTEst) is a multicomponent geothermometry tool for estimating reservoir geochemical parameters including reservoir temperature, CO 2 fugacity, mass of water lost or gained, and a reaction factor. It estimates these parameters and their associated uncertainties by minimizing an objective function that is the weighted sum of squares of the saturation indices of a user-selected set of minerals believed to be equilibrated with the reservoir fluid. RTEst accomplishes these estimates by combining the geochemical modeling capabilities of The Geochemist’s Workbench® with the optimization/parameter estimation resources of PEST®. An included interface aids the user in selecting plausible mineral phases to comprise the objective function and calculates their weighting factors. The working principles of RTEst are described and its efficacy is illustrated by presenting results of its application to various geothermal fields with known conditions. These examples show RTEst can account for the alteration of ascending reservoir fluid by mineral (calcite) re-equilibration with changes in temperature, reconstruct waters with CO 2 loss, correct for the deficit of water and other volatiles (CO 2,gas , H 2 S gas ) from boiling, and determine the amount of mixing of thermal and non-thermal waters. RTEst can use data with basis species below detection limit, missing, or unreliable either by assuming equilibrium with a controlling mineral (fixed-analyte method) or by treating the analyte concentration as an optimization parameter. The inverse of variance weighting method included in RTEst provides more representative results than either the normalization or unit weighting methods. Finally, the ability of RTEst to calculate reservoir temperatures, gas fugacity, and mixing fractions demonstrates its usefulness as a tool for evaluating geothermal systems.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Using in-situ strain measurements to evaluate the accuracy of stress estimation procedures from fracture injection/shut-in tests

Fracture injection/shut-in tests are commonly used to measure the state of stress. Injection creates a hydraulic fracture (or in some cases, opens a preexisting fracture), and then the pressure after shut-in is monitored to identify fracture closure. Different interpretation procedures have been proposed for estimating closure, and the procedures sometimes yield significantly different results. In this study, direct, in-situ strain measurements are used to observe fracture reopening and closure. The tests were performed as part of the EGS Collab project, a mesoscale project performed at 1.25 and 1.5 km depth at the Sanford Underground Research Facility. The tests were instrumented with the SIMFIP tool, a double-packer probe with a high-resolution three-dimensional borehole displacement sensor. The measurements provide a direct observation of the fracture closure signature, enabling a high-fidelity estimate of the fracture closure stress (ie, the normal stress on the fracture). In two of the four tests, injection created an opening mode fracture, and so the closure stress can be interpreted as the minimum principal stress. In the other two tests, injection probably opened preexisting natural fractures, and so the closure stress can be interpreted as the normal stress on the fractures. The strain measurements are compared against different proposed methods for estimating closure stress from pressure transients. The shut-in transients are analyzed with two techniques that are widely used in the field of petroleum engineering – the ‘tangent’ method and the ‘compliance’ method. In three of the four tests, the tangent method significantly underestimates the closure stress. The compliance method is reasonably accurate in all four tests. Closure stress is also interpreted using two other commonly-used methods – ‘first deviation from linearity’ and the method of Hayashi and Haimson. In comparison with the SIMFIP data, these methods tend to overestimate the closure stress, evidently because they identify closure from early-time transient effects, such as near-wellbore tortuosity. In two of the tests, microseismic imaging provides an independent estimate of the size of the fracture created by injection. When combined with a simple mass balance calculation, the SIMFIP stress measurements yield predictions of fracture size that are reasonably consistent with the estimates from microseismic. In conclusion, the calculations imply an apparent fracture toughness 2-3x higher than typical laboratory-derived values.

58 GEOSCIENCES↗

DOE OSTI2015

Simple Method to Estimate Mean Heart Dose From Hodgkin Lymphoma Radiation Therapy According to Simulation X-Rays

Purpose: To describe a new method to estimate the mean heart dose for Hodgkin lymphoma patients treated several decades ago, using delineation of the heart on radiation therapy simulation X-rays. Mean heart dose is an important predictor for late cardiovascular complications after Hodgkin lymphoma (HL) treatment. For patients treated before the era of computed tomography (CT)-based radiotherapy planning, retrospective estimation of radiation dose to the heart can be labor intensive. Methods and Materials: Patients for whom cardiac radiation doses had previously been estimated by reconstruction of individual treatments on representative CT data sets were selected at random from a case–control study of 5-year Hodgkin lymphoma survivors (n=289). For 42 patients, cardiac contours were outlined on each patient's simulation X-ray by 4 different raters, and the mean heart dose was estimated as the percentage of the cardiac contour within the radiation field multiplied by the prescribed mediastinal dose and divided by a correction factor obtained by comparison with individual CT-based dosimetry. Results: According to the simulation X-ray method, the medians of the mean heart doses obtained from the cardiac contours outlined by the 4 raters were 30 Gy, 30 Gy, 31 Gy, and 31 Gy, respectively, following prescribed mediastinal doses of 25-42 Gy. The absolute-agreement intraclass correlation coefficient was 0.93 (95% confidence interval 0.85-0.97), indicating excellent agreement. Mean heart dose was 30.4 Gy with the simulation X-ray method, versus 30.2 Gy with the representative CT-based dosimetry, and the between-method absolute-agreement intraclass correlation coefficient was 0.87 (95% confidence interval 0.80-0.95), indicating good agreement between the two methods. Conclusion: Estimating mean heart dose from radiation therapy simulation X-rays is reproducible and fast, takes individual anatomy into account, and yields results comparable to the labor-intensive representative CT-based method. This simpler method may produce a meaningful measure of mean heart dose for use in studies of late cardiac complications.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2017

Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach

Purpose: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. Methods and Materials: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. Results: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2017

Joint Estimation of Cardiac Toxicity and Recurrence Risks After Comprehensive Nodal Photon Versus Proton Therapy for Breast Cancer

Purpose: The study aims to perform joint estimation of the risk of recurrence caused by inadequate radiation dose coverage of lymph node targets and the risk of cardiac toxicity caused by radiation exposure to the heart. Delivered photon plans are compared with realistic proton plans, thereby providing evidence-based estimates of the heterogeneity of treatment effects in consecutive cases for the 2 radiation treatment modalities. Methods and Materials: Forty-one patients referred for postlumpectomy comprehensive nodal photon irradiation for left-sided breast cancer were included. Comparative proton plans were optimized by a spot scanning technique with single-field optimization from 2 en face beams. Cardiotoxicity risk was estimated with the model of Darby et al, and risk of recurrence following a compromise of lymph node coverage was estimated by a linear dose-response model fitted to the recurrence data from the recently published EORTC (European Organisation for Research and Treatment of Cancer) 22922/10925 and NCIC-CTG (National Cancer Institute of Canada Clinical Trials Group) MA.20 randomized controlled trials. Results: Excess absolute risk of cardiac morbidity was small with photon therapy at an attained age of 80 years, with median values of 1.0% (range, 0.2%-2.9%) and 0.5% (range, 0.03%-1.0%) with and without cardiac risk factors, respectively, but even lower with proton therapy (0.13% [range, 0.02%-0.5%] and 0.06% [range, 0.004%-0.3%], respectively). The median estimated excess absolute risk of breast cancer recurrence after 10 years was 0.10% (range, 0.0%-0.9%) with photons and 0.02% (range, 0.0%-0.07%) with protons. The association between age of the patient and benefit from proton therapy was weak, almost non-existing (Spearman rank correlations of −0.15 and −0.30 with and without cardiac risk factors, respectively). Conclusions: Modern photon therapy yields limited risk of cardiac toxicity in most patients, but proton therapy can reduce the predicted risk of cardiac toxicity by up to 2.9% and the risk of breast cancer recurrence by 0.9% in individual patients. Predicted benefit correlates weakly with age. Combined assessment of the risk from cardiac exposure and inadequate target coverage is desirable for rational consideration of competing photon and proton therapy plans.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

The effect of magnetic susceptibility and magnetic field strength on porosity estimates determined from low-field nuclear magnetic resonance

The presence of materials with high magnetic susceptibility are known to have an impact on 1H nuclear magnetic resonance (NMR) measurements and in laboratory data this often results in poor estimates of porosity from NMR data. To quantify and understand the cause of the poor NMR porosity estimations, in this laboratory study, we examine the effect of magnetic susceptibility, NMR measurement parameters, and NMR instrument design on NMR measurements. Data were collected with two instruments, each with a different Larmor frequency (2 MHz and 485 kHz), on water-saturated unconsolidated sediments with magnetic susceptibility values ranging from 3.6 × 10 -6 to 7020 × 10 -6 SI. The results show that for materials with low magnetic susceptibility (<378 × 10 -6 SI), the gravimetric porosity is accurately predicted from the NMR measurements. For the samples with high magnetic susceptibility (>987 × 10 -6 SI) the gravimetric porosity is poorly predicted from the 2 MHz NMR measurements made at all echo times (from 0.2 to 3.0 ms). In contrast, the gravimetric porosity is more accurately predicted at an echo time of 0.2 ms for measurements made using the 485 kHz instrument, although at larger echo times (>1.0 ms), the porosity estimate becomes poor. The 485 kHz NMR instrument has non-zero internal magnetic field gradients, similar to those found in borehole instruments, in contrast to the 2 MHz NMR instrument, which has a homogeneous applied magnetic field. Finally, we conclude that differences is the magnetic field strength and higher magnetic field inhomogeneities in the 485 kHz NMR instrument contribute to a reduction of the impact of inhomogeneities in the magnetic field caused by materials with high magnetic susceptibility, allowing for improved porosity estimation. These results indicate that NMR measurements collected at short echo times in low, inhomogeneous static fields, e.g., borehole instruments, may provide accurate estimates of porosity in water saturated sediments, even in the presence of magnetic minerals.

58 GEOSCIENCES↗

DOE OSTI2018

Comparison of experimental methods for estimating matrix diffusion coefficients for contaminant transport modeling

Here, diffusion cell and diffusion wafer experiments were conducted to compare methods for estimating effective matrix diffusion coefficients in rock core samples from Pahute Mesa at the Nevada Nuclear Security Site (NNSS). A diffusion wafer method, in which a solute diffuses out of a rock matrix that is pre-saturated with water containing the solute, is presented as a simpler alternative to the traditional through-diffusion (diffusion cell) method. Both methods yielded estimates of effective matrix diffusion coefficients that were within the range of values previously reported for NNSS volcanic rocks. The difference between the estimates of the two methods ranged from 14 to 30%, and there was no systematic high or low bias of one method relative to the other. From a transport modeling perspective, these differences are relatively minor when one considers that other variables (e.g., fracture apertures, fracture spacings) influence matrix diffusion to a greater degree and tend to have greater uncertainty than effective matrix diffusion coefficients. For the same relative random errors in concentration measurements, the diffusion cell method yields effective matrix diffusion coefficient estimates that have less uncertainty than the wafer method. However, the wafer method is easier and less costly to implement and yields estimates more quickly, thus allowing a greater number of samples to be analyzed for the same cost and time. Given the relatively good agreement between the methods, and the lack of any apparent bias between the methods, the diffusion wafer method appears to offer advantages over the diffusion cell method if better statistical representation of a given set of rock samples is desired.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

An iterative stochastic ensemble method for parameter estimation of subsurface flow models

Parameter estimation for subsurface flow models is an essential step for maximizing the value of numerical simulations for future prediction and the development of effective control strategies. We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives. ISEM eliminates the need for adjoint coding and deals with the numerical simulator as a blackbox. The proposed method employs directional derivatives within a Gauss–Newton iteration. The update equation in ISEM resembles the update step in ensemble Kalman filter, however the inverse of the output covariance matrix in ISEM is regularized using standard truncated singular value decomposition or Tikhonov regularization. We also investigate the performance of a set of shrinkage based covariance estimators within ISEM. The proposed method is successfully applied on several nonlinear parameter estimation problems for subsurface flow models. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

A procedure for the estimation of the numerical uncertainty of CFD calculations based on grid refinement studies

This paper offers a procedure for the estimation of the numerical uncertainty of any integral or local flow quantity as a result of a fluid flow computation; the procedure requires solutions on systematically refined grids. The error is estimated with power series expansions as a function of the typical cell size. These expansions, of which four types are used, are fitted to the data in the least-squares sense. The selection of the best error estimate is based on the standard deviation of the fits. The error estimate is converted into an uncertainty with a safety factor that depends on the observed order of grid convergence and on the standard deviation of the fit. For well-behaved data sets, i.e. monotonic convergence with the expected observed order of grid convergence and no scatter in the data, the method reduces to the well known Grid Convergence Index. Examples of application of the procedure are included. - Highlights: • Estimation of the numerical uncertainty of any integral or local flow quantity. • Least squares fits to power series expansions to handle noisy data. • Excellent results obtained for manufactured solutions. • Consistent results obtained for practical CFD calculations. • Reduces to the well known Grid Convergence Index for well-behaved data sets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Stochastic goal-oriented error estimation with memory

We propose a stochastic dual-weighted error estimator for the viscous shallow-water equation with boundaries. For this purpose, previous work on memory-less stochastic dual-weighted error estimation is extended by incorporating memory effects. The memory is introduced by describing the local truncation error as a sum of time-correlated random variables. The random variables itself represent the temporal fluctuations in local truncation errors and are estimated from high-resolution information at near-initial times. The resulting error estimator is evaluated experimentally in two classical ocean-type experiments, the Munk gyre and the flow around an island. In these experiments, the stochastic process is adapted locally to the respective dynamical flow regime. Our stochastic dual-weighted error estimator is shown to provide meaningful error bounds for a range of physically relevant goals. We prove, as well as show numerically, that our approach can be interpreted as a linearized stochastic-physics ensemble.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗