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We describe preliminary comparisons of AIRS/AMU/HSB retrieved geophysical products with correlative data sets to constrain retrieval uncertainties. The results are relevant to oceans at latitudes from 40S to 40N where infrared retrievals are completed, or about 70% of retrieval footprints.
SAND2022-14658 O Linv-hfem3d uses geophysical electrical (E) field data to perform automated wellbore integrity monitoring at an affordable computational cost. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Our present knowledge of the topography of Mars is completely inadequate for addressing a wide range of geophysical, geological, and atmospheric problems. The data acquired to date by several techniques has not provided us with reliable and consistent values for even the equatorial and polar radii, and large uncertainties exist in the altitudes of many of the major volcanic constructs. While much of this can be blamed on a lack of reliable data, we feel that much more could be done to improve the analysis of the present data in a consistent global system that would necessarily involve the re-analysis of early spacecraft and Earth-based data in conjunction with more recent models of Mars' gravity field.
Previous research studies have focused on producing algorithms for extracting geophysical information from passive microwave data regarding ice floe size, sea ice concentration, open water lead locations, and sea ice extent. These studies have resulted in four separate algorithms for extracting these geophysical parameters. Sea ice concentration estimates generated from each of these algorithms (i.e., NASA/Team, NASA/Comiso, AES/York, and Navy) are compared to ice concentration estimates produced from coincident high-resolution synthetic aperture radar (SAR) data. The SAR concentration estimates are produced from data collected in both the Beaufort Sea and the Greenland Sea in March 1988 and March 1989, respectively. The SAR data are coincident to the passive microwave data generated by the Special Sensor Microwave/Imager (SSM/I).
Measurement of the infrared spectrum of the earth and atmosphere between 400 and 1600/cm. In an effort to obtain spectra with high absolute accuracy, it was necessary to incorporate many factors into the calibration, including small departures from unity in the emissivity of the on-board calibration source, imbalance between cold and warm calibration ports, and effects due to orbital variations in instrument temperature. The inclusion of such higher order corrections, along with careful data screening processes, has ensured a high quality data set that can be used in a variety of geophysical and meteorological investigations. Representative samples of data are presented that illustrate the behavior of the thermal emission spectra under a variety of conditions encountered in a typical orbit, including extremes in temperature, surface reststrahlung (or residual ray) effects, and various types of cloud conditions.
Geophysical satellite measurements of trapped particles for estimating proton energy in interplanetary plasma
Radar scattering for geophysical discrimination and identification of terrain surfaces
We will analyze the TOPEX/POSEIDON data using techniques developed for Geosat, although the more accurate TOPEX/POSEIDON data will enable a wider range of problems to be addressed. Our proposed investigations will have five distinct areas: (1) a description of global sea level variability; (2) tropical ocean dynamics; (3) coupled models for El Nino prediction; (4) structure of the lithosphere; and (5) global tide model improvement.
The Southwest Regional Partnership on Carbon Sequestration (SWP) is one of 7 regional partnerships formed in 2003 under the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnerships (RCSPs) initiative. The overall purpose of the initiative was to help determine and implement the technology, infrastructure, and regulations most appropriate to promote carbon storage in different regions of the country. Covering Arizona, Colorado, New Mexico, Oklahoma, Utah, and parts of Texas, Wyoming, and Kansas, the SWP evaluated regional carbon storage and utilization potential and focused on technologies and sites that could complement the region’s strong position in energy production. The project progressed through three phases: • Phase I (2003–2005): Characterized regional geologic formations and CO 2 sources, assessed sequestration potential, and identified pilot test sites. • Phase II (2005–2013): Conducted small-scale field tests to validate sequestration methods, including geologic and terrestrial projects. • Phase III (2008–2022): Demonstrated large-scale CO 2 injection at a commercial oil field to test monitoring, verification, and long-term storage strategies. This report covers Phase III. The final project site, the Farnsworth Unit (FWU) in Texas, provided real-world testing of reservoir characterization, monitoring, and risk evaluation tools and processes that could be used in any commercial scale carbon capture, utilization, and storage (CCUS) project. Extensive data collection and analysis helped refine best practices for reservoir characterization, injection monitoring, and storage verification. The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Key lessons learned include the importance of robust data management, strategic site selection, regulatory navigation, and effective industry collaboration. The project’s findings will inform ongoing and future carbon storage initiatives. Task 1 (Regional Characterization) • The SWP continued to participate in national outreach efforts and NATCARB. • The SWP evaluated multiple potential sites before selecting the FWU as the primary field test location. Task 2 (Public Outreach and Education) • The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Task 3 (Permitting and Regulatory Compliance) • The SWP ensured compliance with federal and state regulations, including National Environmental Policy Act (NEPA) requirements. • The SWP obtained all necessary permits for drilling, injection, and monitoring activities. Task 4 (Site Characterization and Planning) • The SWP developed work plans for four key activities: characterization, simulation, monitoring and verification, and risk evaluation. • The SWP collected and synthesized legacy data from multiple sources to build initial static geological models and dynamic reservoir models demonstrating project feasibility. • The SWP conducted an initial risk evaluation and developed mitigation plans. Task 5 (Field Operations and Data Collection) • The SWP drilled, logged, and cored three characterization wells to gather critical subsurface data. • The SWP conducted multiple geophysical surveys, including 3D seismic, crosswell seismic, and vertical seismic profiling, to improve reservoir characterization. Task 6 (Monitoring and Verification) • The SWP performed extensive geological characterization using data from characterization wells and seismic surveys. • The SWP established a surface monitoring network to track CO 2 flux in soil gas, groundwater chemistry, and near-surface atmospheric CO 2 levels. • The SWP built and refined reservoir models to study the effects of relative permeability on simulation behavior and improve calibration with experimental data. Task 7 (Risk Assessment and Model Refinement) • The SWP conducted multiple studies to evaluate reservoir integrity, predict CO 2 plume behavior and improve predictive modeling capabilities. • The SWP refined geological models and used them to enhance the accuracy of simulation models. • The SWP continued quantitative risk assessment of top-ranked risks and strengthened the link between qualitative and quantitative risk methodologies.
The 2020 Smoky Mountains Computational Sciences and Engineering Conference enlists research scientists from across Oak Ridge National Laboratory (ORNL) to be data sponsors and help create data analytics challenges for eminent data sets at the laboratory. This work describes the significance of each of the seven data sets and their as- sociated challenge questions. The challenge questions for each data set were required to cover multiple difficulty levels. An international call for participation was sent to students, and researchers asking them to form teams of up to four people to apply novel data analytics techniques to these data sets.
A challenge for geophysical research is the relatively short time for which continuous digital data have been available. There are many events of research interest where much of the data were collected by analog or temporary digital means. These legacy data require special efforts to find, organize, and preserve for future research. Here, we focus on one type of legacy data, nuclear explosions, and associated events (e.g., collapses, aftershocks) of high interest for nuclear monitoring research and development. For nuclear tests conducted in Nevada, the Lawrence Livermore Nevada Network recorded valuable data on a variety of formats. In this article, we briefly review this network, and describe one set of data that previously was available via CD-ROM by request, and since 2018 has become available through Incorporated Research Institutions for Seismology as assembled dataset 18-001.
Viewgraphs on Earth Observing System (EOS) Synthetic Aperture Radar (SAR) are presented. The goal of the EOS is to develop the modeling and observational capabilities to predict and/or monitor atmospheric, terrestrial, and oceanic processes that are either causing global change or resulting from global change. The EOS SAR goal is to provide important geophysical products to the EOS data set to improve our understanding of the state and functioning of the Earth system. The EOS SAR strategy is to define the instrument requirements based on required input to geophysical algorithms, to provide the processing capability and algorithms to generate such products on the required spatial (global) and temporal (3-5 days) scales, and to provide the spaceborne instrumentation with international partnerships.
Approaches used in Earth science research such as case study analysis and climatology studies involve discovering and gathering diverse data sets and information to support the research goals. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. In cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. This paper presents a specialized search, aggregation and curation tool for Earth science to address these challenges. The search rool automatically creates curated 'Data Albums', aggregated collections of information related to a specific event, containing links to relevant data files [granules] from different instruments, tools and services for visualization and analysis, and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non relevant information and data.
Electronic and optical crosstalk are radiometric challenges that often exist in the focal plane design in many sensors Such as MODIS. A methodology is described to assess the impact due to optical and electronic crosstalk on the measured radiance, and thereafter, the retrieval of geophysical products using MODIS Level I data sets. Based on a postulated set of electronic and optical crosstalk coefficients, and a set of MODIS scenes, we have simulated a system signal contamination on any detector on a focal plane when another detector on that focal plane is stimulated with a geophysical signal. The original MODIS scenes and the crosstalk impacted scenes can be used with validated geophysical algorithms to derive the final data products. Products contaminated with crosstalk are then compared to those without contamination to assess the impact magnitude and location, and will allow us to separate Out-Of-Band (OOB) leaks from hand-to-hand optical crosstalk, and identify potential failures to meet climate research requirements.
The goal of the Earth Orbiting System Synthetic Aperture Radar (EOS SAR) program is to help develop the modeling and observational capabilities to predict and monitor terrestrial and oceanic processes that are either causing global change or resulting from global change. Specifically, the EOS SAR will provide important geophysical products to the EOS data set to improve our understanding of the state and functioning of the Earth system. The strategy for the EOS SAR program is to define the instrument requirements based on required input to geophysical algorithms, provide the processing capability and algorithms to generate such products on the required spatial (global) and temporal (3-5 days) scales, and to provide the spaceborne instrumentation with international partnerships. Initially this partnership has been with Germany; currently we are exploring broader international partnerships. A MultiSAR approach to the EOS SAR which includes a number of SARs provided by Japan, ESA, Germany, Canada, and the US in synergistic orbits could be used to attain a truly global monitoring capability using multifrequency polarimetric signatures. These concepts and several options for mission scenarios will be presented.
Abstract. Electrical resistivity tomography (ERT) is a broadly accepted geophysical method for subsurface investigations. Interpretation of field ERT data usually requires the application of computationally intensive forward modeling and inversion algorithms. For large-scale ERT data, the efficiency of these algorithms depends on the robustness, accuracy, and scalability on high-performance computing resources. In this regard, we present a robust and highly scalable implementation of forward modeling and inversion algorithms for ERT data. The implementation is publicly available and developed within the framework of PFLOTRAN, an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The forward modeling is based on a finite-volume discretization of the governing differential equations, and the inversion uses a Gauss–Newton optimization scheme. To evaluate the accuracy of the forward modeling, two examples are first presented by considering layered (1D) and 3D earth conductivity models. The computed numerical results show good agreement with the analytical solutions for the layered earth model and results from a well-established code for the 3D model. Inversion of ERT data, simulated for a 3D model, is then performed to demonstrate the inversion capability by recovering the conductivity of the model. To demonstrate the parallel performance of PFLOTRAN's ERT process model and inversion capabilities, large-scale scalability tests are performed by using up to 131 072 processes on a leadership class supercomputer. These tests are performed for the two most computationally intensive steps of the ERT inversion: forward modeling and Jacobian computation. For the forward modeling, we consider models with up to 122 ×106 degrees of freedom (DOFs) in the resulting system of linear equations and demonstrate that the code exhibits almost linear scalability on up to 10 000 DOFs per process. On the other hand, the code shows superlinear scalability for the Jacobian computation, mainly because all computations are fairly evenly distributed over each process with no parallel communication.
The retrieval algorithms used for optical remote sensing satellite data to estimate Earth's geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal-to-noise ratio (SNR) performance to meet biogeoscience objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at >10 nm spectral resolution, >30 m spatial resolution, and >8-day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit, and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.
Semicontrolled image mosaics of Venus, based on Magellan data, are being compiled at 1:50,000,000, 1:10,000,000, 1:5,000,000, and 1:1,000,000 scales to support the Magellan Radar Investigator (RADIG) team. The mosaics are semicontrolled in the sense that data gaps were not filled and significant cosmetic inconsistencies exist. Contours are based on preliminary radar altimetry data that is subjected to revision and improvement. Final maps to support geologic mapping and other scientific investigations, to be compiled as the dataset becomes complete, will be sponsored by the Planetary Geology and Geophysics Program and/or the Venus Data Analysis Program. All maps, both semicontrolled and final, will be published as I-maps by the United States Geological Survey. All of the mapping is based on existing knowledge of the spacecraft orbit; photogrammetric triangulation, a traditional basis for geodetic control on planets where framing cameras were used, is not feasible with the radar images of Venus, although an eventual shift of coordinate system to a revised spin-axis location is anticipated. This is expected to be small enough that it will affect only large-scale maps.