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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

COMPASS-FME Synoptic Site Characterization

This dataset contains soil biogeochemical and physicochemical characterization data for the COMPASS-FME synoptic sites.This dataset also contains data for the paper Patel et al. 2025 "Transition zones at the changing coastal terrestrial-aquatic interface", https://doi.org/10.1029/2025JG008978.Coastal soils are a significant but highly uncertain component of global biogeochemical cycles. These systems experience unique spatial and temporal variability in biogeochemical processes, driven by wetland-to-upland gradients and hydrological fluctuations. We studied drivers of coastal soil variability (a) at regional scales and (b) across transects from upland forest to wetland, in two contrasting regions — Lake Erie, a freshwater lacustrine system, and Chesapeake Bay, a saltwater estuarine system. Salinity-related analytes were a key driver of soil variability, not just in the saltwater system, but surprisingly, also in the freshwater system. We had hypothesized linear trends in biogeochemical parameters along the TAI – however, contrary to expectations, transition soils were not consistently intermediate between upland and wetland endmembers; the non-monotonic trends of carbon, phosphorus, iron along our transects suggest that these are key analytes to study in our regions. Rapidly changing soil factors across coastal gradients provide insights into which soil processes may act as precursors to ecosystem shifts. Our comprehensive soil characterization across the coastal transects provides essential data for mechanistic modeling of ecosystem dynamics.The data are provided as processed, csv files. Raw data and processing scripts can be accessed on GitHub (https://github.com/COMPASS-DOE/cmps-soil_characterization).A note on the nomenclature: the experimental design represents three points along the coastal gradient -- upland, transition, and wetland. "wetland" is referred to as "marsh" in the corresponding paper. The two terms can be used interchangeably for the sites in this study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

SECARB Regional Project Assessment

The SECARB Cranfield Early Test Project was designed to safely demonstrate large-scale, long-term CO 2 injection and storage in a CO 2 -enhanced oil recovery (EOR) operation and associated saline reservoir. Storage during EOR operations has the potential to offset commercial costs, holding a significant promise for future large-scale deployment in the southeastern United States. Denbury Onshore LLC began CO 2 -EOR operations in July 2008 at the Cranfield oilfield located east of Natchez, Mississippi (Figure 1). The location offered an ideal setting to investigate storage monitoring at a commercial CO 2 -EOR operation. The field team, led by the Gulf Coast Carbon Center of the Bureau of Economic Geology at the University of Texas at Austin, deployed the largest suite of monitoring, verification, accounting (MVA) technologies to collect data to assess long-term CO 2 utilization and storage. The CO 2 injection interval is within the 3,000-meter-deep fluvial lower Tuscaloosa Formation, a large and regionally extensive saline reservoir, at a broad four-way structural closure that defines the Cranfield oilfield. Tests were conducted in the oil-producing area as well as in the non-oil-producing brine aquifer. The CO 2 used during EOR operation was transported via pipeline from the Jackson Dome, a natural source of CO 2 . The study focused on four integrated research areas: (1) the High-Volume Injection Test area (HiVIT); (2) the Detailed Area of Study (DAS); (3) the Geomechanical area; and (4) the near-surface observatory, also called the “P-site.” CO 2 injection occurred at the HiVIT and the DAS (Figure 2 and Figure 3). The DAS was located down-dip of the oil-water interface of the Cranfield oilfield. The well layout included one injection well and two down-dip observation wells. The team was the first of the regional partnerships to monitor more than 1 million metric tons of CO 2 . At the end of the project, the team monitored more than 11 million metric tons of CO 2 injected and 5 million metric tons of CO 2 stored. The Cranfield Project was the fifth project worldwide, and the first such project in the United States, to reach this CO 2 injection volume while being monitored to demonstrate storage effectiveness.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1972

Proton irradiation of stem cells: Radiation damage and chemical radioprotection

Effects of high energy protons on erythropoietic stem cells and radioprotection by chemicals were investigated in NASA Space Radiation Effects Laboratory. The effects of a parallel beam of 600 MeV protons. The fluence, when converted to dose, were referenced to the synchrocyclotron beam monitors which were then used to administer radiation exposures. Mice were given graded doses to 300 rads to determine dose-response curve. Other mice received saline, AET, or 5-hydroxytryptamine 10 to 15 minutes before exposure.

Riley, R. C.↗
DOE OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

58 GEOSCIENCES↗
NASA NTRS2018

Satellite Sensor Requirements for Monitoring Essential Biodiversity Variables of Coastal Ecosystems

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration less than 2%, relative calibration of 0.2%, polarization sensitivity less than 1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

aquatic↗
NASA NTRS1999

Atmospheric Energy Limits on Subsurface Life on Mars

It has been suggested that the terrestrial biomass of subterranean organisms may equal or exceed that at the surface. Taken as a group, these organisms can live in heavily saline conditions at temperatures from 115 C to as low as -20 C. Such conditions might exist on Mars beneath the surface oxidant in an aquifer or hydrothermal system, where the surrounding rock would also protect against the solar ultraviolet radiation. The way that such systems could obtain energy and carbon is not completely clear, although it is believed that on Earth, energy flows from the interaction of highly reduced basalt with groundwater produce H2, while carbon is derived from CO2 dissolved in the groundwater. Another potential source is the Martian atmosphere, acting as a photochemical conduit of solar insolation.

Weiss, B. P.↗
NASA NTRS2004

Contrasting Inherent Optical Properties and Carbon Metabolism Between Five Northeastern (USA) Estuary-plume Systems

We have recently developed the ability to rapidly assess Surface inherent optical properties (IOP), oxygen concentration and pCO2 in estuarine-plume systems using flow-through instrumentation. During the summer of 2004, several estuarine-plume systems were surveyed which include the Pleasant (ME), Penobscot (ME), Kennebec-Androscoggin (ME), Merrimack (NH-MA) and Hudson (NY). Continuous measurements of surface chlorophyll and colored dissolved organic carbon (CDOM) fluorescence, beam attenuation, temperature, salinity, oxygen and pC02 were taken at each system along a salinity gradient from fresh water to near oceanic endmembers. CTD and IOP profiles were also taken at predetermined surface salinity intervals. These were accompanied by discrete determinations of chlorophyll (HPLC and fluorometric), total suspended solids (TSS), dissolved organic carbon (DOC) and alkalinity. IOP data were calibrated using chlorophyll, DOC and TSS data to enable the retrieval of these constituents from IOP data. Considerable differences in the data sets were observed between systems. These ranged from the DOC-enriched, strongly heterotrophic Pleasant River System to the high-chlorophyll autotrophic Merrimack River System. Using pCO2 and oxygen saturation measurements as proxies for water column metabolism, distinct relationships were found between trophic status and inherent optical properties. The nature of these relationships varies between systems and is likely a function of watershed and estuarine attributes including carbon and nutrient loading, in-situ production and related autochthonous inputs of DOC and alkalinity. Our results suggest that IOP data may contain significant information about the trophic status of estuarine and plume systems.

Vandemark, Doug↗
DOE OSTI2024

Mapping Permafrost Variability and Degradation Using Seismic Surface Waves, Electrical Resistivity, and Temperature Sensing: A Case Study in Arctic Alaska

Subsurface processes significantly influence surface dynamics in permafrost regions, necessitating utilizing diverse geophysical methods to reliably constrain permafrost characteristics. This research uses multiple geophysical techniques to explore the spatial variability of permafrost in undisturbed tundra and its degradation in disturbed tundra in Utqiagvik, Alaska. Here, we integrate multiple quantitative techniques, including multichannel analysis of surface waves (MASW), electrical resistivity tomography (ERT), and ground temperature sensing, to study heterogeneity in permafrost’s geophysical characteristics. MASW results reveal active layer shear wave velocities (V s ) between 240 and 370 m/s, and permafrost V s between 450 and 1,700 m/s, typically showing a low-high-low velocity pattern. Additionally, we find an inverse relationship between in situ V s and ground temperature measurements. The V s profiles along with electrical resistivity profiles reveal cryostructures such as cryopeg and ice-rich zones in the permafrost layer. The integrated results of MASW and ERT provide valuable information for characterizing permafrost heterogeneity and cryostructure. Corroboration of these geophysical observations with permafrost core samples’ stratigraphies and salinity measurements further validates these findings. This combination of geophysical and temperature sensing methods along with permafrost core sampling confirms a robust approach for assessing permafrost’s spatial variability in coastal environments. Our results also indicate that civil infrastructure systems such as gravel roads and pile foundations affect permafrost by thickening the active layer, lowering the V s , and reducing heterogeneity. We show how the resulting V s profiles can be used to estimate key parameters for designing buildings in permafrost regions and maintaining existing infrastructure in polar regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enriched Molecular-Level View of Saline Wetland Soil Carbon by Sensitivity-Enhanced Solid-State NMR

Soil organic matter (SOM) plays a major role in mitigating greenhouse gas emission and regulating earth’s climate, carbon cycle, and biodiversity. Wetland soils account for one-third of all SOM; however, globally, coastal wetland soils are eroding faster due to increasing sea-level rise. Our understanding of carbon sequestration dynamics in wetlands lags behind that of upland soils. Here, we employ solid-state nuclear magnetic resonance (ssNMR) to investigate the molecular-level structure of biopolymers in wetland soils spanning 11 centuries. High-resolution multidimensional spectra, enabled by dynamic nuclear polarization (DNP), demonstrate enduring preservation of molecular structures within herbaceous plant cores, notably condensing aromatic motifs and carbohydrates, even over a millennium, with the preserved cores constituting a decreasing minority among molecules from decomposition and repolymerization with depth and age. Such preserved cores occur alongside molecules from the decomposition of loosely packed parent biopolymers. These findings emphasize the relative vulnerability of coastal wetland SOM when exposed to oxygenated water due to geological and anthropogenic changes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Century/millennium internal climate oscillations in an ocean-atmosphere-continental ice sheet model

We demonstrate in a simple climate model that there exist nonlinear feedbacks between the atmosphere, ocean, and ice sheets capable of producing century/millennium timescale internal oscillations resembling those seen in the paleoclimate record. Feedbacks involve meridional heat and salt transports in the North Atlantic, surface ocean freshwater fluxes associated with melting and growing continental ice sheets in the northen hemisphere and with Atlantic to Pacific water vapor transport. The positive feedback between the production of North Atlantic Deep Water (NADW) and the meridional salt transport by the Atlantic thermohaline circulation tends to destabilize the climate system, while the negative feedback between the freshwater flux, either to or from the continental ice sheets, and meridional heat flux to the high-latitude North Atlantic, accomplished by the thermohaline circulation, stabilizes the system. The thermohaline circulation plays a central role in both positive and negative feedbacks because of its transport of both heat and salt. Because of asymmetries between the growth and melt phases the oscillations are, in general, accompanied by a growing or decreasing ice volume over each cycle, which in the model is reflected by increasing or decreasing mean salinity.

Birchfield, Edward G.↗
DOE OSTI2021

Biogeochemical control points of connectivity between a tidal creek and its floodplain

As global climates shift, coastal systems experience changes that alter function within the tidal zone. However, it remains uncertain how changes in tidal extent and magnitude will alter coastal biogeochemical cycling. We present high-frequency data collected in-situ along two transects across a tidal creek and floodplain to capture how changes in vertical and lateral connectivity act as biogeochemical control points. Using wavelet analysis, we identified short periods of strong vertical connectivity during spring tides followed by extended periods of floodplain isolation from tides. During a drier period, neap tides were linked to lateral creek-floodplain connectivity, detected by discharge of saline, oxygen-depleted groundwater to the creek on ebb tides. We estimate up to half of creek water throughout the tidal cycle was associated with floodplain drainage. From point measurements of carbon, nitrogen, and greenhouse gases along the creek-floodplain transect, we infer that lateral connectivity functions as a biogeochemically significant control point.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ENERGY DISPERSIVE DIFFRACTION TOMOGRAPHY OF SHARK VERTEBRAL CENTRA

Shark vertebrae and their centra (vertebral bodies) are high-performance structures able to survive millions of cycles of high amplitude strain despite lacking a repair mechanism for accumulating damage. Shark centra consist of mineralized cartilage, a biocomposite of bioapatite (bAp), and collagen, and the nanocrystalline bAp's contribution to functionality remains largely uninvestigated. Using the multiple detector energy-dispersive diffraction (EDD) system at 6-BM-B, the Advanced Photon Source, and 3D tomographic sampling, the 3D functionality of entire centra were probed. Immersion in ethanol vs phosphate-buffered saline produces only small changes in bAp d-spacing within a great hammerhead centrum. EDD mapping under in situ loading was performed an entire blue shark centrum, and 3D maps of bAp strain showed the two structural zones of the centrum, the corpus calcareum and intermedialia, contained opposite-signed strains approaching 0.5%, and application of similar to 8% nominal strain did not alter these strain magnitudes and their spatial distribution.

Park, Jun-Sang↗
NASA NTRS2012

SMOS L1C and L2 Validation in Australia

Extensive airborne field campaigns (Australian Airborne Cal/val Experiments for SMOS - AACES) were undertaken during the 2010 summer and winter seasons of the southern hemisphere. The purpose of those campaigns was the validation of the Level 1c (brightness temperature) and Level 2 (soil moisture) products of the ESA-led Soil Moisture and Ocean Salinity (SMOS) mission. As SMOS is the first satellite to globally map L-band (1.4GHz) emissions from the Earth?s surface, and the first 2-dimensional interferometric microwave radiometer used for Earth observation, large scale and long-term validation campaigns have been conducted world-wide, of which AACES is the most extensive. AACES combined large scale medium-resolution airborne L-band and spectral observations, along with high-resolution in-situ measurements of soil moisture across a 50,000km2 area of the Murrumbidgee River catchment, located in south-eastern Australia. This paper presents a qualitative assessment of the SMOS brightness temperature and soil moisture products.

Rudiger, Christoph↗
NASA NTRS2024

Characterizing the California Current System through Sea Surface Temperature and Salinity

Characterizing temperature and salinity (T-S) conditions is a standard framework in oceanography to identify and describe deep water masses and their dynamics. At the surface, this practice is hindered by multiple air–sea–land processes impacting T-S properties at shorter time scales than can easily be monitored. Now, however, the unsurpassed spatial and temporal coverage and resolution achieved with satellite sea surface temperature (SST) and salinity (SSS) allow us to use these variables to investigate the variability of surface processes at climate-relevant scales. In this work, we use SSS and SST data, aggregated into domains using a cluster algorithm over a T-S diagram, to describe the surface characteristics of the California Current System (CCS), validating them with in situ data from uncrewed Saildrone vessels. Despite biases and uncertainties in SSS and SST values in highly dynamic coastal areas, this T-S framework has proven useful in describing CCS regional surface properties and their variability in the past and in real time, at novel scales. This analysis also shows the capacity of remote sensing data for investigating variability in land–air–sea interactions not previously possible due to limited in situ data.

Marisol García-Reyes↗
NASA NTRS2012

Estimating Sea Surface Salinity and Wind Using Combined Passive and Active L-Band Microwave Observations

Several L-band microwave radiometer and radar missions have been, or will be, operating in space for land and ocean observations. These include the NASA Aquarius mission and the Soil Moisture Active Passive (SMAP) mission, both of which use combined passive/ active L-band instruments. Aquarius s passive/active L-band microwave sensor has been designed to map the salinity field at the surface of the ocean from space. SMAP s primary objectives are for soil moisture and freeze/thaw detection, but it will operate continuously over the ocean, and hence will have significant potential for ocean surface research. In this innovation, an algorithm has been developed to retrieve simultaneously ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction, thus minimizing the least squares error (LSE) measure, which signifies the difference between measurements and model functions of brightness temperatures and radar backscatter. The algorithm uses the conjugate gradient method to search for the local minima of the LSE. Three LSE measures with different measurement combinations have been tested. The first LSE measure uses passive microwave data only with retrieval errors reaching 1 to 2 psu (practical salinity units) for salinity, and 1 to 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, the third LSE measure uses measurement combinations invariant under the Faraday rotation. For Aquarius, the expected RMS SSS (sea surface salinity) error will be less than about 0.2 psu for low winds, and increases to 0.3 psu at 25 m/s wind speed for warm waters (25 C). To achieve the required 0.2 psu accuracy, the impact of sea surface roughness (e.g. wind-generated ripples) on the observed brightness temperature has to be corrected to better than one tenth of a degree Kelvin. With this algorithm, the accuracy of retrieved wind speed will be high, varying from a few tenths to 0.6 m/s. The expected direction accuracy is also excellent (less than 10 ) for mid to high winds, but degrades for lower speeds (less than 7 m/s).

Yueh, Simon H.↗
DOE OSTI2021

Experimental Study of Transport Behavior of Swellable Microgel Particles in Superpermeable Channels for Conformance Control

Summary Gel treatment is an effective way to attack excessive water production during oil development. The transport behavior of gel materials in reservoirs is of crucial importance to the effectiveness of gel treatments. The aim of this paper is investigating the transport behavior of swellable micrometer-sized preformed particle gels (PPGs, or microgels) through superpermeable (super-K) channels. Sandpacks with permeabilities ranging from 27 to 221 darcies were used to mimic the super-K channels. Multiple pressure sensors were applied along the sandpack models to monitor the propagation behavior of the microgels. The tested microgel particles could transport through the super-K channels, and a higher driving pressure gradient was required when the particle/pore size ratio was larger. The pressure gradient distribution along the super-K channels was relatively uniform when the particle/pore ratio was low (less than 1.3). However, the inlet section would show increasingly higher pressure gradients as the particle/pore ratio was increased, indicating increased difficulty in propagation. The propagation of the gel particles was significantly slower compared with the carrying fluid. The delayed propagation behavior was more pronounced when the particle/pore ratio was higher. The injection pressure was much less sensitive to the injection flow rate compared with a Newtonian fluid. The gel dispersion exhibited an apparent shear thinning (pseudoplastic) behavior when transporting through the porous channels. Breakage of the gel particles was observed especially at high superficial velocities. The particle breakage was partially responsible for the apparent shear thinning behavior. The breakage phenomenon was in favor of deep placement of the gel particles. The channel permeabilities were significantly reduced by the microgels, bringing sufficient resistance to subsequent waterflooding (more than 99.5%). At given matching size conditions, softer gels were more likely to establish in-depth placement and uniform water blocking capacity in the channels. The microgel particles exhibited salinity-responsive behavior to the post-brine flush. The gel particles could shrink and reswell according to the salinity of the injected water. Possibilities were discussed to use this salinity-responsive behavior. Also, the microgels exhibited a particular disproportionate permeability reduction (DPR) effect. After gel injection, the channel permeability to water flow was reduced by more than 20 to 92 times of the permeability to oil flow. This work provides important support to understand the transport behavior of gel particles in super-K channels. The achievements are helpful for gel product selection and gel treatment design.

Engineering↗
NASA NTRS2004

Aquarius Instrument Science Calibration During the Risk Reduction Phase

This final report presents the results of work performed under NASA Grant NAG512726 during the period 15 January 2003 through 30 June 2004. An analysis was performed of a possible vicarious calibration method for use by Aquarius to monitor and stabilize the absolute and relative calibration of its microwave radiometer. Stationary statistical properties of the brightness temperature (T(sub B)) measured by a low Earth orbiting radiometer operating at 1.4135 GHz are considered as a means of validating its absolute calibration. The global minimum, maximum, and average T(sub B) are considered, together with a vicarious cold reference method that detects the presence of a sharp lower bound on naturally occurring values for T(sub B). Of particular interest is the reliability with which these statistics can be extracted from a realistic distribution of T(sub B) measurements that would be observed by a typical sensor. Simulations of measurements are performed that include the effects of instrument noise and variable environmental factors such as the global water vapor and ocean surface temperature, salinity and wind distributions. Global minima can vary widely due to instrument noise and are not a reliable calibration reference. Global maxima are strongly influenced by several environmental factors as well as instrument noise and are even less stationary. Global averages are largely insensitive to instrument noise and, in most cases, to environmental conditions as well. The global average T(sub B) varies at only the 0.1 K RMS level except in cases of anomalously high winds, when it can increase considerably more. The vicarious cold reference is similarly insensitive to instrument effects and most environmental factors. It is not significantly affected by high wind conditions. The stability of the vicarious reference is, however, found to be somewhat sensitive (at the several tenths of Kelvins level) to variations in the background cold space brightness, T(sub c). The global average is much less sensitive to this parameter and so using two approaches together can be mutually beneficial.

Ruf, Christopher S.↗
NASA NTRSDate not supplied

Predicting the Seawater Chemistry of an Ocean World Using Machine Learning on Isotopic Measurements of Volatile CO2

Introduction: Given the long time intervals required for data transmission to and from ocean worlds targets, low bandwidth for data transmission, time required for data processing and analysis, and potentially extreme radiation environments (e.g., Europa), it is clear that ocean worlds missions will need more autonomous flight instruments and software in order to achieve established science goals. Protracted time intervals for data analysis (e.g., Europa Lander) strongly motivates the development of rapid, consistent and streamlined methods for interpreting data from flight mass spectrometers to e.g., determine how mass spectra from a plume or surface liquid/ice relates to the surface/subsurface. Since mass spectrometry also has the potential to correctly identify biosignatures[1], it is imperative that such methods for interpreting data are consistent and accurate. We used 848 isotope ratio mass spectra from laboratory analyses of CO2 that interacted with ocean worlds-relevant seawaters as a ‘training’ dataset for ‘unsupervised’ machine learning. In unsupervised learning, characteristics of the data are not labeled or linked, and any similarities found only result from the neural network. CO2 isotopologues analyzed for this dataset mimic the remote measurements of CO2 by a flight mass spectrometer, and are detailed in Theiling [2]. From this dataset, we used measured features of the spectra, such as retention time, intensity, and (isotopologue) mass ratios as inputs for our autoencoder neural network. Our neural network was trained to find similarities in these and other spectral features for seawaters of a particular composition and amount of initial CO2. Successful training then created an output of these similarities for various seawaters, which included MgSO4, Na2SO4, NaCl, MgCl2, KCl, and NaHCO3, and combinations of these salts. We then applied dimensionality reduction techniques such as Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (TSNE), and Uniform Manifold Approximation and Projection (UMAP) to demonstrate latent data features as a two-dimensional projection in a unitless, high-dimensional space. In this projection, a data point represents the combined effect of spectral features such as intensity, retention time, and isotope ratio. Our initial UMAP demonstrates data clustering (organization of the data by the neural network) based on the amount of CO2 that had initially interacted with each seawater. Further training using more ‘supervised’ learning techniques demonstrate strong clustering of preliminary data based on initial CO2 concentration, seawater chemical composition, and ionic strength (salinity). Our preliminary work therefore suggests that machine learning has the potential to identify compositional variants of an ocean world seawater based on mass spectra from volatile CO2 measurements. Acknowledgments: This work was funded through a Strategic Task Group at NASA Goddard Space Flight Center. The training dataset was collected through funding from the Oklahoma Space Grant Consortium. References: [1] Pappalardo, R. et al. (2013) Astrobiology, 13, 740–773. [2] Theiling (2020) Icarus, 114216.

Europa↗