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DOE OSTI2025

Inference of Multichannel r -process Element Enrichment in the Milky Way Using Binary Neutron Star Merger Observations

Observations of GW170817 strongly suggest that binary neutron star (BNS) mergers produce rapid neutron-capture nucleosynthesis ( r -process) elements. However, it remains an open question whether these mergers can account for all the r -process element enrichment in the Milky Way’s history. Here, we constrain the contributions of the BNS channel using astrophysical neutron star observations. The rate and mass distributions are constrained by LIGO/Virgo/Kagra through the latest catalog GWTC-3, the neutron star equation of state by gravitational-wave, radio, and X-ray observations, and the delay time distribution by short gamma-ray burst (GRB) host galaxy associations. We present a Bayesian framework to consistently combine these observations with abundance information to quantify the contribution and uncertainties of single and multiple astrophysical enrichment sources, and obtain a distribution of per-event BNS r -process element yields consistent with geophysical and astrophysical abundance constraints. We then adopt a Galactic chemical evolution model assuming an instantaneous and fixed amount of Fe enrichment from core-collapse supernovae, and show that BNS-only enrichment scenarios remain inconsistent with the observed r-process abundance trend of disk stars in the Galaxy even with the uncertainties in BNS merger observations. Using stellar abundance observations instead of the short GRB constraints, we can infer a shorter BNS delay time distribution with power-law index α ≤ −2.0 and minimum delay time ${t}_{{\rm{\min }}}\leqslant 40$ Myr at 90% confidence, consistent with detailed Galactic chemical evolution models. Such delay times are in tension with those predicted by standard BNS formation models. Alternatively, we confirm that a two-channel scenario, in which the second channel tracks the star formation history without significant delay, can account for both Galactic stellar and short GRB observations. We estimate that 45%–90% of the r -process abundance in the Milky Way today would have been produced by this star formation-tracking channel, rather than BNS mergers with significant delay times.

gravitational wave astronomy↗

DOE OSTI2022

A deep learning-enhanced framework for multiphysics joint inversion

Joint inversion has drawn considerable attention due to the availability of multiple geophysical data sets, ever-increasing computational resources, the development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, in which the information obtained from different models can complement each other. We have developed a deep learning-enhanced joint inversion framework to simultaneously reconstruct different physical models by fusing different types of geophysical data. Traditionally, structure similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g., cross gradient). The constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a traditional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multiphysics without structural changes. Numerical experiments on the joint inversion of 2D DC resistivity data and seismic traveltime are used to validate our method. In addition, this learning-based framework demonstrates excellent generalization abilities when tested on data sets using different geologic structures. It also can handle different sensing configurations and nonconforming discretization.

Geochemistry & Geophysics↗

DOE OSTI2025

Bayesian Inference for the Seismic Moment Tensor Using Regional Waveforms and Teleseismic- P Polarities with a Data-Derived Distribution of Velocity Models and Source Locations

The largest source of uncertainty in any source inversion is the velocity model used in the transfer function that relates observed ground motion to the seismic moment tensor. However, standard inverse procedure often does not quantify uncertainty in the seismic moment tensor due to error in the Green’s functions from uncertain event location and Earth structure. Here, we incorporate this uncertainty into an estimation of the seismic moment tensor using a data-derived distribution of velocity models based on complementary geophysical data sets, including thickness constraints, velocity profiles, gravity data, surface-wave group velocities, and regional body-wave travel times. The data-derived distribution of velocity models is then used as a prior distribution of Green’s functions for use in Bayesian inference of an unknown seismic moment tensor using regional and teleseismic-P waveforms. The use of multiple data sets is important for gaining resolution to different components of the moment tensor. The combined likelihood is estimated using data-specific error models and the posterior of the seismic moment tensor is estimated and interpreted in terms of the most probable source type.

58 GEOSCIENCES↗

NASA NTRS2023

Editorial: Advanced Technologies in Remote Sensing of Aerosols and Trace Gases Special Issue

The seven papers in this special issue span a range of satellite remote-sensing applications, from characterizing near-surface aerosol pollution and SO2 concentrations (Qu et al., 2023a and Xu et al., 2023, respectively), to atmospheric column methane and cloud measurement (Karoff and Vara-Vela, 2023 and Jian et al., 2023, respectively), to the stratospheric aerosol distribution over the over Xinjiang region of China (ZiWei and Xiangling, 2023). In these efforts to advance the field, common threads are the use of various machine learning methods to extract patterns from vast amounts of satellite data, and in many cases, the integration of multiple datasets, from different sources, sometimes including models, to provide the constraints required to obtain meaningful geophysical quantities.

aerosol remote sensing↗

NASA NTRS1987

The chronology of Mercury's geological and geophysical evolution - The Vulcanoid hypothesis

The possibility that constraints on Mercury's chronology can be relaxed through reference to a Mercury-specific bombarding population of planetesimals interior to its orbit, and therefore only occasionally encountering the planet at times of secular perturbations, is investigated. While such vulcanoids may have been a significant source of early cratering, those in the pertinent orbits are depleted by mutual collisions and can therefore only furnish a modest extension of the heavy bombardment period. A search is conducted for vulcanoids further within Mercury's orbit; evidence that Mercury's intercrater plains are of volcanic origin is discussed.

Leake, Martha A.↗

DOE OSTI2022

Improved geophysical monitoring of carbon sequestration through parameter linkage to reservoir modeling

Predictive reservoir modeling, even if present in the form of only basic hydrogeological model assumptions, is expected to accompany the majority of carbon capture and sequestration monitoring activities. It thus represents a source of prior information about the migration of injected fluids that can benefit geophysical survey planning and ensuing monitoring. Constraining the imaging of geophysical monitoring data with reservoir modeling is preferable over standalone geophysical imaging because of additional complementary hydrogeological information. However, fully coupled hydrogeophysical data inversion for flow-modeling parameters that control saturation predictions is an involved process. Within the context of three-dimensional electromagnetic (EM) inversion of data from borehole-to-surface layouts, we employ a "poor people's" alternative. The approach constrains geophysical inversion parameters through saturation predictions. The coupling is realized through spatially variable lower and upper parameter bounds that scale with gas saturation magnitudes, the latter provided by reservoir modeling. In this work, enhancement of three-dimensional time-lapse plume EM imaging is demonstrated for simulated sequestration into a depleted gas reservoir.

58 GEOSCIENCES↗

DOE OSTI2020

Integration of Soft Data Into Geostatistical Simulation of Categorical Variables

Uncertain or indirect “soft” data, such as geologic interpretation, driller’s logs, geophysical logs or imaging, offer potential constraints or “soft conditioning” to stochastic models of discrete categorical subsurface variables in hydrogeology such as hydrofacies. Previous bivariate geostatistical simulation algorithms have not fully addressed the impact of data uncertainty in formulation of the (co) kriging equations and the objective function in simulated annealing (or quenching). This paper introduces the geostatistical simulation code tsim-s, which accounts for categorical data uncertainty through a data “hardness” parameter. In generating geostatistical realizations with tsim-s, the uncertainty inherent to soft conditioning is factored into both 1) the data declustering and spatial correlation functions in cokriging and 2) the acceptance probability for change of category in simulated quenching. The degree or sensitivity to which soft data conditions a realization as a function of hardness can be quantified by mapping category probabilities derived from multiple realizations. In addition to point or borehole data, arrays of data (e.g., as derived from a depth-dependency function, probability map, or “prior realization”) can be used as soft conditioning. The tsim-s algorithm provides a theoretically sound and general framework for integrating datasets of variable location, resolution, and uncertainty into geostatistical simulation of categorical variables. A practical example shows how tsim-s is capable of generating a large-scale three-dimensional simulation including curvilinear features.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

A major crustal feature in the southeastern United States inferred from the MAGSAT equivalent source anomaly field

The MAGSAT equivalent-source anomaly field evaluated at 325 km altitude depicts a prominent anomaly centered over southeast Georgia, which is adjacent to the high-amplitude positive Kentucky anomaly. To overcome the satellite resolution constraint in studying this anomaly, conventional geophysical data were included in analysis: Bouguer gravity, seismic reflection and refraction, aeromagnetic, and in-situ stress-strain measurements. This integrated geophysical approach, infers more specifically the nature and extent of the crustal and/or lithospheric source of the Georgia MAGSAT anomaly. Physical properties and tectonic evolution of the area are all important in the interpretation.

Ruder, M. E.↗

NASA NTRS1997

A 70th Degree Lunar Gravity Model (GLGM-2) from Clementine and other tracking data

A spherical harmonic model of the lunar gravity field complete to degree and order 70 has been developed from S band Doppler tracking data from the Clementine mission, as well as historical tracking data from Lunar Orbiters 1-5 and the Apollo 15 and 16 subsatellites. The model combines 361,000 Doppler observations from Clementine with 347,000 historical observations. The historical data consist of mostly 60-s Doppler with a noise of 0.25 to several mm/s. The Clementine data consist of mostly 10-s Doppler data, with a data noise of 0.25 mm/s for the observations from the Deep Space Network, and 2.5 mm/s for the data from a naval tracking station at Pomonkey, Maryland. Observations provided Clementine, provide the strongest satellite constraint on the Moon's low-degree field. In contrast the historical data, collected by spacecraft that had lower periapsis altitudes, provide distributed regions of high-resolution coverage within +/- 29 deg of the nearside lunar equator. To obtain the solution for a high-degree field in the absence of a uniform distribution of observations, we applied an a priori power law constraint of the form 15 x 10(exp -5)/sq l which had the effect of limiting the gravitational power and noise at short wavelengths. Coefficients through degree and order 18 are not significantly affected by the constraint, and so the model permits geophysical analysis of effects of the major basins at degrees 10-12. The GLGM-2 model confirms major features of the lunar gravity field shown in previous gravitational field models but also reveals significantly more detail, particularly at intermediate wavelengths (10(exp 3) km). Free-air gravity anomaly maps derived from the new model show the nearside and farside highlands to be gravitationally smooth, reflecting a state of isostatic compensation. Mascon basins (including Imbrium, Serenitatis, Crisium, Smythii, and Humorum) are denoted by gravity highs first recognized from Lunar Orbiter tracking. All of the major mascons are bounded by annuli of negative anomalies representing significant subsurface mass deficiencies. Mare Orientale appears as a minor mascon surrounded by a horseshoe-shaped gravity low centered on the Inner and Outer Rook rings that is evidence of significant subsurface structural heterogeneity. Although direct tracking is not available over a significant part of the lunar farside, GLGM-2 resolves negative anomalies that correlate with many farside basins, including South Pole-Aitken, Hertzsprung, Korolev, Moscoviense, Tsiolkovsky, and Freundlich-Sharonov.

Lemonie, Frank G. R.↗

NASA NTRS2019

The Moon's Deep Interior: A Hotbed for Seismicity and the Question of Partial Melt

Terrestrial planets all share a common structural framework (crust, mantle, core) which is developed very shortly after formation and which determines subsequent evolution. The Moon formed under very unique circumstances. Interior properties provide constraints on formation and evolution models, and possible indicators of an early dynamo for magnetic field generation: Layering, Composition, Seismic velocity & density, Presence of partial melt, Core state. Constraints on these properties arise from geophysical observations: Geodetic parameters, Lunar laser ranging, Gravity field, Magnetic induction, Heat flow, Seismology.

Weber, Renee↗

NASA NTRS1974

The geochemical evolution of the moon

Evidence for and implications of homogeneous accretion of the moon are considered. Interelement ratios are used to calculate the composition of the highland crust. The rare-earth data for the highlands show a positive Eu anomaly, allowing the deduction that the interior has a negative anomaly. The Cr/Ni ratios in the highlands are considered in regard to the overall lunar abundance of the siderophile elements. These abundance data and the constraints from the heat flow measurements are next used to set limits on the bulk composition for the moon. The geochemical constraints are integrated with the petrological and geophysical data in an attempt to provide a consistent model for the geochemical evolution of the moon.

Taylor, S. R.↗

NASA NTRS1992

Inversion of parameters for semiarid regions by a neural network

Microwave brightness temperatures obtained from a passive radiative transfer model are inverted through use of a neural network. The model is applicable to semiarid regions and produces dual-polarized brightness temperatures for 6.6-, 10.7-, and 37-GHz frequencies. A range of temperatures is generated by varying three geophysical parameters over acceptable ranges: soil moisture, vegetation moisture, and soil temperature. A multilayered perceptron (MLP) neural network is trained with a subset of the generated temperatures, and the remaining temperatures are inverted using a backpropagation method. Several synthetic terrains are devised and inverted by the network under local constraints. All the inversions show good agreement with the original geophysical parameters, falling within 5 percent of the actual value of the parameter range.

Zurk, Lisa M.↗

NASA NTRS1990

Microgravity laboratory simulations of planetary and stellar circulations

This paper summarizes experiments on thermal convection in a rotating differentially heated spherical shell of liquid that were carried out in the microgravity laboratory of Spacelab III. In these experiments, dielectric polarization forces were used to generate a radially-directed buoyancy force. This enabled the simulation of a number of geophysically and astrophysically important situations in which sphericity and rotation both impose strong constraints on the global scale fluid dynamics. Recent theoretical and numerical interpretations of the original data are discussed. Further laboratory experiments in the areas of geophysical and g-jitter fluid dynamics are suggested.

Hart, John E.↗

NASA NTRS2001

Rotation of EOFs by the Independent Component Analysis: Towards A Solution of the Mixing Problem in the Decomposition of Geophysical Time Series

The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.

Aires, Filipe↗

NASA NTRS2000

Rotation of EOFs by the Independent Component Analysis: Towards a Solution of the Mixing Problem in the Decomposition of Geophysical Time Series

Aires, Filipe↗

NASA NTRS1992

Physical retrieval of precipitation water contents using multisensor microwave data and model constraints

A physical retrieval method is proposed for estimating precipitation water distributions and other geophysical parameters from the Special Sensor Microwave/Imager (SSM/I) data. In the proposed method, sensor antenna functions are explicitly included to accommodate varying channel resolution. An embedded one-dimensional cloud model is utilized to generate vertical distributions of precipitating and nonprecipitating water, and spectral solutions are sought for certain background parameters, such as humidity, which vary more slowly in the horizontal than the cloud and precipitation water contents. The method allows the incorporation of measurements from SSM/T and geostationary infrared measurements in addition to SSM/I data, as well as information from conventional sources (e.g., radiosondes) or numerical forecast model fields.

Olson, William S.↗

NASA NTRS1977

Some constraints on the thermal history of the lunar magma ocean

If the accumulating evidence is accepted that the outer portion of the moon was molten for 100-200 million years, it is clear that a permanent insulating surface layer existed over nearly all of that epoch. Considerations of crustal stability against break-up and foundering lead to the view that this insulating blanket must have been an early-forming plagioclase-rich layer light enough to float on the hot magma. It is found that radiometric age-dating evidence implies a fairly specific history for the solidification of the lunar magma ocean. The possibility is anticipated that geochronological and petrological constraints will be sufficient to narrow the range of allowed geophysical and geochemical models. It is hoped that such a study will make it possible to deduce the original depth, and hence, the composition of the lunar magma ocean. If the moon accreted homogeneously, the composition of the magma ocean will also be that of the whole moon, and hence such models should allow estimation of the bulk lunar composition.

Herbert, F.↗

NASA NTRS1986

A scanning radar altimeter for mapping continental topography

Topographic information constitutes a fundamental data set for the Earth sciences. In the geological and geophysical sciences, topography combined with gravitational information provides an important constraint on the structure and rheologic properties of the crust and lithosphere. Detailed topography data can also be used to map offsets associated with faulting and to reveal the effects of tectonic deformation. In the polar regions, elevation data form a crucial but as yet largely unavailable resource for studying ice sheet mass balance and ice flow dynamics. The vast Antarctic ice sheet is the largest fresh water reservoir on Earth and is an important influence on ocean circulation and global climate. However, our knowledge of its stability is so limited that we cannot even specify whether the Antarctic ice sheet is growing or shrinking. It is clear that there is need for high quality global topography data. A summary of potential applications with their resolution requirements is shown.

Dixon, T. H.↗