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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Global Magellan-image map of Venus at full resolution

During its first 243-day mapping cycle, the Magellan spacecraft succeeded in imaging 84 percent of the surface of Venus at resolutions on the order of 100 meters; subsequent cycles have increased the total coverage to over 97 percent and provided redundant coverage of much of the planet with differing viewing geometries. Unfortunately, this full-resolution global dataset is in the form of thousands of individual orbit tracks (F-BIDR's) whose length-to-width ratio of nearly 1000:1 makes them minimally useful unless mosaicked. The Magellan project produced full-resolution mosaics (F-MIDR's) only for selected regions on the planet, whereas a global set of mosaics was made only at threefold degraded resolution (C1-MIDR's). Furthermore, although the F-MIDR's, which are approximately equidimensional, are much better suited for scientific interpretation than the F-BIDR's, they are still an unwieldy dataset: over 1500 quadrangles, each showing a region only about 600 km on a side, would be required to cover the entire planet. The USGS has therefore undertaken to produce and distribute a global, full resolution set of mosaics of the Magellan image data in a format that will be efficient for both hardcopy and digital use. The initial motivation was that it would provide an efficient means of verifying the integrity of the F-BIDR's to be archived on computer-compatible tape at the USGS Flagstaff facility. However, the resulting product, known as the FMAP, should also serve as an important resource for future scientific interpretation. It will offer several advantages beyond global coverage at full resolution. The first, alluded to above, is its division of the planet's surface to minimize the number of quadrangles and maximize their area, subject to the limits on the number of pixels imposed by state-of-the-art digital recording media and hardcopy output devices. The second, the use of improved 'cosmetic' processing techniques, will greatly reduce tonal discontinuities between component F-BIDR's in the FMAP compared to the standard Magellan mosaic products. Finally, wherever possible, the FMAP will incorporate data that were unavailable (e.g., because of processing delays) when the standard MIDR products were created, as well as data that were reprocessed to improve their radiometric or geometric quality.

Kirk, R. L.↗
NASA NTRS1994

Flexible Method for Inter-object Communication in C++

A method has been developed for organizing and sharing large amounts of information between objects in C++ code. This method uses a set of object classes to define variables and group them into tables. The variable tables presented here provide a convenient way of defining and cataloging data, as well as a user-friendly input/output system, a standardized set of access functions, mechanisms for ensuring data integrity, methods for interprocessor data transfer, and an interpretive language for programming relationships between parameters. The object-oriented nature of these variable tables enables the use of multiple data types, each with unique attributes and behavior. Because each variable provides its own access methods, redundant table lookup functions can be bypassed, thus decreasing access times while maintaining data integrity. In addition, a method for automatic reference counting was developed to manage memory safely.

Curlett, Brian P.↗
NASA NTRS2001

Measurement of OH, H2SO4, MSA, NH3 and DMSO Aboard the NASA P-3B Aircraft

This project involved the installation of a downsized multichannel mass spectrometer instrument on the NASA P-3B aircraft and its subsequent use on the PEM-Tropics B mission. The new instrument performed well, measuring a number of difficult-to-measure compounds and providing much new photochemical and sulfur data as well as possibly uncovering a new nighttime DMSO source. The details of this effort are discussed.

Eisele, Fred↗
NASA NTRS2003

Studies of the Antarctic Sea Ice Edges and Ice Extents from Satellite and Ship Observations

Passive-microwave derived ice edge locations in Antarctica are assessed against other satellite data as well as in situ observations of ice edge location made between 1989 and 2000. The passive microwave data generally agree with satellite and ship data but the ice concentration at the observed ice edge varies greatly with averages of 14% for the TEAM algorithm and 19% for the Bootstrap algorithm. The comparisons of passive microwave with the field data show that in the ice growth season (March - October) the agreement is extremely good, with r(sup 2) values of 0.9967 and 0.9797 for the Bootstrap and TEAM algorithms respectively. In the melt season however (November - February) the passive microwave ice edge is typically 1-2 degrees south of the observations due to the low concentration and saturated nature of the ice. Sensitivity studies show that these results can have significant impact on trend and mass balance studies of the sea ice cover in the Southern Ocean.

Worby, Anthony P.↗
NASA NTRS2005

A comparison of soil moisture sensors for space flight applications

Plants will be an important part of future long-term space missions. Automated plant growth systems require accurate and reliable methods of monitoring soil moisture levels. There are a number of different methods to accomplish this task. This study evaluated sensors using the capacitance method (ECH2O), the heat-pulse method (TMAS), and tensiometers, compared to soil water loss measured gravimetrically in a side-by-side test. The experiment monitored evaporative losses from substrate compartments filled with 1- to 2-mm baked calcinated clay media. The ECH2O data correlated well with the gravimetric measurements, but over a limited range of soil moisture. The averaged TMAS sensor data overstated soil moisture content levels. The tensiometer data appeared to track evaporative losses in the 0.5- to 2.5-kPa range of matric potential that corresponds to the water content needed to grow plants. This small range is characteristic of large particle media, and thus high-resolution tensiometers are required to distinguish changing moisture contents in this range.

NASA Center KSC↗
NASA NTRS2011

Global Precipitation Mission Visualization Tool

The Global Precipitation Mission (GPM) software provides graphic visualization tools that enable easy comparison of ground- and space-based radar observations. It was initially designed to compare ground radar reflectivity from operational, ground-based, S- and C-band meteorological radars with comparable measurements from the Tropical Rainfall Measuring Mission (TRMM) satellite's precipitation radar instrument. This design is also applicable to other groundbased and space-based radars, and allows both ground- and space-based radar data to be compared for validation purposes. The tool creates an operational system that routinely performs several steps. It ingests satellite radar data (precipitation radar data from TRMM) and groundbased meteorological radar data from a number of sources. Principally, the ground radar data comes from national networks of weather radars (see figure). The data ingested by the visualization tool must conform to the data formats used in GPM Validation Network Geometry-matched data product generation. The software also performs match-ups of the radar volume data for the ground- and space-based data, as well as statistical and graphical analysis (including two-dimensional graphical displays) on the match-up data. The visualization tool software is written in IDL, and can be operated either in the IDL development environment or as a stand-alone executable function.

Schwaller, Mathew↗
DOE OSTI2014

Quantification of Wellbore Leakage Risk Using Non-destructive Borehole Logging Techniques

Well integrity is important at all potential CCS locations and may play a crucial role establishing leakage risk in areas where there is a high density of existing wells that could be impacted by the storage operations including depleted petroleum fields where EOR or CCS will occur. To address a need for risk quantification methods that can be directly applied to individual wells using borehole logging tools a study was conducted using data from five wells in Wyoming. The objectives of the study were: Objective 1: Develop methods to establish the baseline flow parameters (porosity and permeability or mobility) from individual measurements of the material properties and defects in a well. Objective 2: Develop a correlation between field flow-property data and cement logs that can be used to establish the flow-properties of well materials and well features using cement mapping tools. Objective 3: Establish a method that uses the flow-property model (Objective 2) to analyze the statistical uncertainties associated with individual well leakage that can provide basis for uncertainty in risk calculations. The project objectives were met through the logging of five wells in Carbon and Natrona County Wyoming to collect data that was used to estimate individual and average well flow properties and model the results using ultrasonic data collected during the logging. Three of the five wells provided data on point and average flow properties for well annuli. Data from the other two wells were used to create models of cement permeability and test whether information collected in one well could be used to characterize another well. The results of the in-situ point measurements were confirmed by the lab measurements sidewall cores collected near the same depths Objective 1 was met using the data collected through logging, testing, and sampling. The methods were developed that can establish baseline flow parameters of wells by both point and average test methods. The methods to estimate the flow properties modeling of point pressure tests, modeling of vertical interference tests, and laboratory measurement of cased-hole sidewall cores The wells were in sufficiently good shape to allow the development of the characterization methods while still having enough defects to study differences in results as they relate to well integrity. Samples and tests analyzed from three of five wells studied in showed the cements were largely intact and had not degraded from exposure native brines. Log results taken in conjunction with the core measurements indicate that interfaces and/or problems with cement placement due to eccentering provide preferential flow paths for fluids, which can increase the effective permeability of the barrier several orders of magnitude above the permeability of intact cement. The results of the maps created using logging tools indicating that the cement condition and bond are generally good identify a need for more research to understand how logs can be used to predicteffective well permeabilities such as those measured by the VITs in this study.

42 ENGINEERING↗
DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗
DOE OSTI2022

Citizen Green #1 Well: Quantitative XRD & Thin Section Data

This submission includes information on quantitative x-ray diffraction measurements and thin section photography conducted on sidewall cores from the Citizen Green #1 well drilled on King Island, CA (API 07720688) as part of the WESTCARB partnership basin characterization effort. Cores were acquired with a rotary sidewall tool at a range of depths; only deeper cores were sufficiently consolidated for analysis. SidewallCoreFormationDepthNotes.xls provides details on available samples. Helium porosimetry, air permeability, and brine permeability measurements were conducted on all competent samples (~15). Samples were largely in the Mokelumne, H&T shale, and Starkey formations. All measurements were conducted at Lawrence Berkeley National Laboratory.

Thin Section↗
NASA NTRS1988

MODIS Information, Data, and Control System (MIDACS) system specifications and conceptual design

The MODIS Information, Data, and Control System (MIDACS) Specifications and Conceptual Design Document discusses system level requirements, the overall operating environment in which requirements must be met, and a breakdown of MIDACS into component subsystems, which include the Instrument Support Terminal, the Instrument Control Center, the Team Member Computing Facility, the Central Data Handling Facility, and the Data Archive and Distribution System. The specifications include sizing estimates for the processing and storage capacities of each data system element, as well as traffic analyses of data flows between the elements internally, and also externally across the data system interfaces. The specifications for the data system, as well as for the individual planning and scheduling, control and monitoring, data acquisition and processing, calibration and validation, and data archive and distribution components, do not yet fully specify the data system in the complete manner needed to achieve the scientific objectives of the MODIS instruments and science teams. The teams have not yet been formed; however, it was possible to develop the specifications and conceptual design based on the present concept of EosDIS, the Level-1 and Level-2 Functional Requirements Documents, the Operations Concept, and through interviews and meetings with key members of the scientific community.

Han, D.↗
DOE OSTI2026

Hyporheic‐Zone Processes and Stream Oxygen Dynamics: Insights From a Multiscale Reactive Transport Model

Aquatic ecosystem metabolism encapsulates the daily fixation (gross primary production, GPP d ) and mineralization (ecosystem respiration, ER d ) of organic carbon. In fluvial systems, these are commonly estimated by inverse solutions to field observations using a model that describes oxygen concentrations varying in the water column in response to metabolic fluxes and air‐water gas exchange controlled by a rate coefficient (K 600 ). The most common conceptual model is the single‐station metabolism (SSM) model. The simplicity and flexibility of this conceptualization make it attractive; however, it implicitly assumes that all the processes that consume oxygen in fluvial systems can be lumped into a bulk estimate of respiration with poorly understood consequences for estimates of GPP d , ER d , and K 600 . Here, we focus on the implications of using SSM conceptualization when estimating metabolic fluxes from oxygen dynamics in channels where hyporheic exchange occurs. We use a new multiscale numerical model for reactive transport in streams that represents hyporheic exchange and streambed heterotrophic respiration. Nondimensionalization of this model reveals dimensionless groups that collectively control oxygen dynamics. Numerical experiments offer a mechanistic understanding of the impacts of hyporheic exchange on diel oxygen dynamics revealing that potential biases arise from neglecting mass transfer limitations. Specifically, we found that hyporheic exchange significantly affects diel oxygen dynamics, even for nonreactive streambed sediments. Moreover, while the SSM performs well in many situations, we find conditions where significant bias is produced by hyporheic exchange, even when oxygen data are well‐fitted. These situations pose a major challenge in the interpretation of metabolism assessment estimates.

Gomez‐Velez, Jesus D. [Oak Ridge National Laborato↗
DOE OSTI2013

IS THE METALLICITY OF THE PROGENITOR OF LONG GAMMA-RAY BURSTS REALLY LOW?

Observations of long gamma-ray bursts (LGRBs) offer a unique opportunity to probe the history of cosmic star formation, although whether LGRBs are biased tracers remains highly debated. Based on an extensive sample of LGRBs compiled by Robertson and Ellis, we analyze various models of star formation rate, combining the possible effect of the cosmic metallicity evolution under the assumption that LGRBs preferentially occur in low-metallicity galaxies. The models of star formation rate tested in this work include empirical fits from observational data as well as a self-consistent model calculated from the hierarchical structure formation scenario. Comparing with the observational data, we find a relatively higher metallicity cut of Z {approx}> 0.6 Z{sub Sun} for the empirical fits and no metallicity cut for the self-consistent model. These results imply that there is no strong bias toward low metallicity in LGRB host galaxies, in contrast to previous studies suggesting a cut of Z {approx} 0.1-0.3 Z{sub Sun }, and that the inferred low-metallicity dependencies of LGRBs are strongly related to the specific models of star formation rate. Furthermore, a significant fraction of LGRBs that occur in small halos down to 3 Multiplication-Sign 10{sup 8} M{sub Sun} can provide an alternative explanation for the difference between the star formation rate and the LGRB rate.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Benefits and Limits of Phasing Alleles for Network Inference of Allopolyploid Complexes

Abstract Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared with haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where the depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared with using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical nonidentifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes.

Evolutionary Biology↗
DOE OSTI2016

Special features of isomeric ratios in nuclear reactions induced by various projectile particles

Calculations for (p, n) and (α, p3n) reactions were performed with the aid of the TALYS-1.4 code. Reactions in which the mass numbers of target and product nuclei were identical were examined in the range of A = 44–124. Excitation functions were obtained for product nuclei in ground and isomeric states, and isomeric ratios were calculated. The calculated data reflect well the dependence of the isomeric ratios on the projectile type. A comparison of the calculated and experimental data reveals, that, for some nuclei in a high-spin state, the calculated data fall greatly short of their experimental counterparts. These discrepancies may be due to the presence of high-spin yrast states and rotational bands in these nuclei. Calculations involving various level-density models included in the TALYS-1.4 code with allowance for the enhancement of collective effects do not remove the discrepancies in the majority of cases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

A RESTful API for accessing microbial community data for MG-RAST

Metagenomic sequencing has produced significant amounts of data in recent years. For example, as of summer 2013, MGRAST has been used to annotate over 110,000 data sets totaling over 43 Terabases. With metagenomic sequencing finding even wider adoption in the scientific community, the existing web-based analysis tools and infrastructure in MG-RAST provide limited capability for data retrieval and analysis, such as comparative analysis between multiple data sets. Moreover, although the system provides many analysis tools, it is not comprehensive. By opening MG-RAST up via a web services API (application programmers interface) we have greatly expanded access to MG-RAST data, as well as provided a mechanism for the use of third-party analysis tools with MG-RAST data. This RESTful API makes all data and data objects created by the MG-RAST pipeline accessible as JSON objects. As part of the DOE Systems Biology Knowledgebase project (KBase, http:// kbase.us) we have implemented a web services API for MG-RAST. This API complements the existing MG-RAST web interface and constitutes the basis of KBase’s microbial community capabilities. In addition, the API exposes a comprehensive collection of data to programmers. This API, which uses a RESTful (Representational State Transfer) implementation, is compatible with most programming environments and should be easy to use for end users and third parties. It provides comprehensive access to sequence data, quality control results, annotations, and many other data types. Where feasible, we have used standards to expose data and metadata. Code examples are provided in a number of languages both to show the versatility of the API and to provide a starting point for users. We present an API that exposes the data in MG-RAST for consumption by our users, greatly enhancing the utility of the MG-RAST service.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Characterization of Pliocene and Miocene Formations in the Wilmington Graben, Offshore Los Angeles, for Large-Scale Geologic Storage of CO₂

Geomechanics Technologies has completed a detailed characterization study of the Wilmington Graben offshore Southern California area for large-scale CO₂ storage. This effort has included: an evaluation of existing wells in both State and Federal waters, field acquisition of about 175 km (109 mi) of new seismic data, new well drilling, development of integrated 3D geologic, geomechanics, and fluid flow models for the area. The geologic analysis indicates that more than 796 MMt of storage capacity is available within the Pliocene and Miocene formations in the Graben for midrange geologic estimates (P50). Geomechanical analyses indicate that injection can be conducted without significant risk for surface deformation, induced stresses or fault activation. Numerical analysis of fluid migration indicates that injection into the Pliocene Formation at depths of 1525 m (5000 ft) would lead to undesirable vertical migration of the CO₂ plume. Recent well drilling however, indicates that deeper sand is present at depths exceeding 2135 m (7000 ft), which could be viable for large volume storage. For vertical containment, injection would need to be limited to about 250,000 metric tons per year per well, would need to be placed at depths greater than 7000ft, and would need to be placed in new wells located at least 1 mile from any existing offset wells. As a practical matter, this would likely limit storage operations in the Wilmington Graben to about 1 million tons per year or less. A quantitative risk analysis for the Wilmington Graben indicate that such large scale CO₂ storage in the area would represent higher risk than other similar size projects in the US and overseas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantitative Assessment of Parent Well Effect on Hydraulic Fracture Propagation at HFTS2: Insights from Cross-Well Strain Measurements and Microseismic Data

Understanding fracture propagation behavior is essential for optimizing hydraulic fracturing in unconventional reservoirs. This study demonstrates the value of integrating Low-Frequency Distributed Acoustic Sensing (LF-DAS) and microseismic data, which together provide a more complete picture of fracture growth. Using data from Hydraulic Fracturing Test Site 2 (HFTS2), we identify stress changes in depletion zones induced by parent wells as a key factor influencing fracture propagation. This result is shown by new measurements of in-situ fracture propagation velocity and fracture-hit volume (fluid volume at fracture hit?) from LF-DAS and event density from microseismic. These findings highlight the importance of considering parent well effects, well spacing, and stimulation sequencing in completion design to improve reservoir development and production efficiency.

depletion zones↗
DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗