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At least 1,063 records · Page 59

Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE

Here, we use machine learning (ML) enhanced computational reverse engineering analysis of scattering experiments (CREASE) to interpret small-angle X-ray scattering (SAXS) data obtained from a system of nanoparticles without a priori knowledge of their exact shapes (e.g. spheres or ellipsoids), sizes (0.5–50 nm) and distributions. The SAXS measurements yielded three categories of scattering profiles exhibiting 'strong', 'weak' and 'no' features. Diminishing features (e.g. broadening or disappearing peaks) in scattering profiles have always been attributed to the presence of significant dispersity in the system. Such featureless SAXS data are not suitable for traditional analysis using analytical models. If one were to fit a relevant analytical model (e.g. the lmfit analytical model for polydisperse spheres) to these 'weak' and 'no' SAXS profiles from our nanoparticle systems, one would obtain non-unique interpretations of the data. Relying on electron microscopy to identify the distributions of nanoparticle shapes and sizes is also unfeasible, especially in high-throughput synthesis and characterization loops. In such situations, to identify the distributions of particle sizes and shapes that could be present in the sample, one must rely on methods like ML-CREASE to interpret the data quickly and output all relevant interpretations about the structure present in the system. The ML-CREASE optimization loop takes the experimental scattering profile as input and outputs multiple candidate solutions whose computed scattering profiles match the SAXS profile input. The ML-CREASE method outputs distributions of relevant structural features, such as the volume fraction of the nanoparticles in the system and the mean and standard deviation of the particle size and aspect ratio, assuming a type of distribution (e.g. normal, log-normal) for size and aspect ratio. We find that, for the SAXS profiles analyzed here, accounting for the shape dispersity along with size dispersity of the nanoparticles using ML-CREASE improved the match between the computed scattering profiles and input experimental profiles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Early burial mud diapirism and its impact on stratigraphic architecture in the Carboniferous of the Shannon Basin, County Clare, Ireland

Delta fronts are often characterized by high rates of sediment supply that result in unstable slopes and a wide variety of soft-sediment deformation, including the formation of overpressured and mobile muds that may flow plastically during early burial, potentially forming mud diapirs. The coastal cliffs of County Clare, western Ireland, expose Pennsylvanian (Namurian) delta-front deposits of the Shannon Basin at large scale and in three dimensions. These deposits include decametre-scale, internally chaotic mudstone masses that clearly impact the surrounding sedimentary strata. Evidence indicates that these were true mud (unlithified sediment) diapirs that pierced overlying strata. This study documents a well-exposed ca 20 m tall mud diapir and its impact on the surrounding mouth-bar deposits of the Tullig Cyclothem. A synsedimentary fault and associated rollover dome, evident from stratal thicknesses and the dip of the beds, define one edge of the diapir. These features are interpreted as recording the reactive rise of the mud diapir in response to extensional faulting along its margin. Above the diapir, heterolithic sandstones and siltstones contain evidence for the creation of localized accommodation, suggesting synsedimentary filling, tilting and erosion of a shallow sag basin accommodated by the progressive collapse of the diapir. Two other diapirs are investigated using three-dimensional models built from ‘structure from motion’ drone imagery. Both diapirs are interpreted to have grown predominantly through passive rise (downbuilding). Stratal relationships for all three diapirs indicate that they were uncompacted and fluid-rich mud beds that became mobilized through soft-sediment deformation during early burial (i.e. <50 m, likely <10 m depth). Each diapir locally controlled the stratigraphic architecture in the shallow subsurface and potentially influenced local palaeocurrents on the delta. The mud diapirs studied herein are distinct from deeper ‘shale diapirs’ that have been inferred from seismic sections worldwide, now largely disputed.

defects, mechanical behavior, carbon sequestration↗

Enhancing trap and fault seal analyses by integrating observations from HR3D seismic data with well logs and conventional 3D seismic data, Texas inner shelf

An understanding of trap and fault seal quality is critical for assessing hydrocarbon prospectivity. To achieve this, modern analytical techniques leverage well data and conventional industry-standard 3D seismic data to evaluate the trap, and any faults displacing the reservoir and top seal intervals. Above all, geological interpretation provides the framework of trap and fault seal analyses, but can be hindered by the data resolution, quality and acquisition style of the conventional seismic data. Furthermore, limiting the analysis to only the petroleum system at depth may lead to erroneous perceptions because interpreting overburden features, such as shallow faults or gas chimneys, can provide valuable observations with respect to container performance, and can to help validate trap and fault seal predictions. Here, a supplement to conventional 3D data are high-resolution 3D seismic (HR3D) data, which provide detailed images of the overburden geology. This study utilizes an HR3D seismic volume in the San Luis Pass area of the Texas inner shelf, where shallow fault tips and a sizeable gas chimney are interpreted over an unsuccessful hydrocarbon prospect. Static post-drill fault seal and trap analyses suggest that the primary fault displacing the structural closure could have withheld columns of gas c. 100 m high, but disagree with our HR3D seismic interpretations and dry-well analyses. From our results, we hypothesize that tertiary gas migration through fault conduits reduced the hydrocarbon column in the prospective Early Miocene reservoir, and may have resulted from continued movement along the intersecting faults. Overall, this study reinforces the importance of understanding the overburden geology and geohistory of faulted prospects, and demonstrates the utility of pre-drill HR3D acquisition when conducting trap and fault seal analyses.

3D seismic↗

Linear Time-Invariant Models of a Large Cumulus Ensemble

Abstract Methods in system identification are used to obtain linear time-invariant state-space models that describe how horizontal averages of temperature and humidity of a large cumulus ensemble evolve with time under small forcing. The cumulus ensemble studied here is simulated with cloud-system-resolving models in radiative–convective equilibrium. The identified models extend steady-state linear response functions used in past studies and provide accurate descriptions of the transfer function, the noise model, and the behavior of cumulus convection when coupled with two-dimensional gravity waves. A novel procedure is developed to convert the state-space models into an interpretable form, which is used to elucidate and quantify memory in cumulus convection. The linear problem studied here serves as a useful reference point for more general efforts to obtain data-driven and interpretable parameterizations of cumulus convection.

Meteorology & Atmospheric Sciences↗

Modern analogs for understanding pollen-vegetation dynamics in a Mediterranean mosaic landscape (Balearic Islands, Western Mediterranean)

This paper presents the study of modern pollen analogs from the Balearic Islands. While similar studies have been largely applied to mainland areas, research focused on modern vegetation dynamics on Mediterranean islands remains very rare. In this research, we combine vegetation surveys, pollen analysis and multivariate statistics to understand landscape composition. The main objectives of are: (1) to examine pollen-vegetation relationships in relation to environmental and land-use variables; (2) to understand modern pollen representation in a mosaic landscape structure; and (3) to propose pollen indicators that characterize the primary vegetation types from the Balearic Islands to better interpret past pollen records in Mediterranean island environments. Pollen results and Redundancy Analysis (RDA) distinguish three major groups: (a) Holm oak and box formations; (b) maquis and garrigues; and (c) anthropogenic and open habitats. Landscape form, mean decadal rainfall, mean decadal temperature, fire activity, trampling, slope percentage, wet/flooded soil, saline soil, distance to agropastoral cells, gHM index, domestic herbivory presence, agropastoral use, and soil type are the major variables explaining modern pollen assemblage variation in our research. Poaceae undiff., Plantago sp., Apiaceae undiff., Cerealia-t, and Cichorieae are highly correlated to human activities but should be interpreted cautiously when occurring in low values. Quercus ilex-t, Hypericum, and Buxus are correlated to humid locations while Pistacia, Pinus, Juniperus-t, and Olea to high mean decadal temperatures. Our study indicates how pollen analysis and multivariate analysis are powerful tools for characterizing the mosaic landscape, with special focus on the main vegetation types of the Balearic Islands.

Geology↗

A Procedure for Evaluating Modified Pulsed-Neutron- Source Experiments in Subcritical Nuclear Reactors

A theoretical interpretation of the modified pulsed-neutron-source experiments of Sjöstrand, Gozani, and Garelis and Russell is given using exact steady-state Boltzmann equations. Here, interpretation is based on a phenomenological description of the experiments and is patterned after work done on the Garelis-Russell method by Corngold. The basic approximation made is that the fundamental prompt-mode decay constant is much larger than any delayed-neutron precursor decay constant. The theoretical interpretation allows the reactivities measured by the above three modified pulsed-source techniques to be easily calculated and compared to more conventional definitions of reactivity. The calculations can be performed by any standard source-iteration code that has been modified to solve the inhomogeneous problem. Experiments were performed on the Cornell University Critical Assembly and interpreted with the aid of the above theory. Calculations and experiments agree to within 20%. Sjöstrand's method is found to give the best result for this reactor.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Dixie Valley Engineered Geothermal System Exploration Methodology Project, Baseline Conceptual Model Report

The Engineered Geothermal System (EGS) Exploration Methodology Project is developing an exploration approach for EGS through the integration of geoscientific data. The Project chose the Dixie Valley Geothermal System in Nevada as a field laboratory site for methodology calibration purposes because, in the public domain, it is a highly characterized geothermal system in the Basin and Range with a considerable amount of geoscience and most importantly, well data. The overall project area is 2500km2 with the Calibration Area (Dixie Valley Geothermal Wellfield) being about 170km2. The project was subdivided into five tasks (1) collect and assess the existing public domain geoscience data; (2) design and populate a GIS database; (3) develop a baseline (existing data) geothermal conceptual model, evaluate geostatistical relationships, and generate baseline, coupled EGS favorability/trust maps from +1km above sea level (asl) to -4km asl for the Calibration Area at 0.5km intervals to identify EGS drilling targets at a scale of 5km x 5km; (4) collect new geophysical and geochemical data, and (5) repeat Task 3 for the enhanced (baseline + new ) data. Favorability maps were based on the integrated assessment of the three critical EGS exploration parameters of interest: rock type, temperature and stress. A complimentary trust map was generated to compliment the favorability maps to graphically illustrate the cumulative confidence in the data used in the favorability mapping. The Final Scientific Report (FSR) is submitted in two parts with Part I describing the results of project Tasks 1 through 3 and Part II covering the results of project Tasks 4 through 5 plus answering nine questions posed in the proposal for the overall project. FSR Part I presents (1) an assessment of the readily available public domain data and some proprietary data provided by Terra-Gen Power, LLC, (2) a re-interpretation of these data as required, (3) an exploratory geostatistical data analysis, (4) the baseline geothermal conceptual model, and (5) the EGS favorability/trust mapping. The conceptual model presented applies to both the hydrothermal system and EGS in the Dixie Valley region. FSR Part II presents (1) 278 new gravity stations; (2) enhanced gravity-magnetic modeling; (3) 42 new ambient seismic noise survey stations; (4) an integration of the new seismic noise data with a regional seismic network; (5) a new methodology and approach to interpret this data; (5) a novel method to predict rock type and temperature based on the newly interpreted data; (6) 70 new magnetotelluric (MT) stations; (7) an integrated interpretation of the enhanced MT data set; (8) the results of a 308 station soil CO2 gas survey; (9) new conductive thermal modeling in the project area; (10) new convective modeling in the Calibration Area; (11) pseudo-convective modeling in the Calibration Area; (12) enhanced data implications and qualitative geoscience correlations at three scales (a) Regional, (b) Project, and (c) Calibration Area; (13) quantitative geostatistical exploratory data analysis; and (14) responses to nine questions posed in the proposal for this investigation. Enhanced favorability/trust maps were not generated because there was not a sufficient amount of new, fully-vetted (see below) rock type, temperature, and stress data. The enhanced seismic data did generate a new method to infer rock type and temperature. However, in the opinion of the Principal Investigator for this project, this new methodology needs to be tested and evaluated at other sites in the Basin and Range before it is used to generate the referenced maps. As in the baseline conceptual model, the enhanced findings can be applied to both the hydrothermal system and EGS in the Dixie Valley region.

15 GEOTHERMAL ENERGY↗

Dixie Valley Engineered Geothermal System Exploration Methodology Project, Baseline Conceptual Model Report

The Engineered Geothermal System (EGS) Exploration Methodology Project is developing an exploration approach for EGS through the integration of geoscientific data. The overall project area is 2500km2 with the Calibration Area (Dixie Valley Geothermal Wellfield) being about 170km2. The Final Scientific Report (FSR) is submitted in two parts (I and II). FSR part I presents (1) an assessment of the readily available public domain data and some proprietary data provided by terra-gen power, llc, (2) a re-interpretation of these data as required, (3) an exploratory geostatistical data analysis, (4) the baseline geothermal conceptual model, and (5) the EGS favorability/trust mapping. The conceptual model presented applies to both the hydrothermal system and EGS in the Dixie Valley region. FSR Part II presents (1) 278 new gravity stations; (2) enhanced gravity-magnetic modeling; (3) 42 new ambient seismic noise survey stations; (4) an integration of the new seismic noise data with a regional seismic network; (5) a new methodology and approach to interpret this data; (5) a novel method to predict rock type and temperature based on the newly interpreted data; (6) 70 new magnetotelluric (MT) stations; (7) an integrated interpretation of the enhanced MT data set; (8) the results of a 308 station soil CO2 gas survey; (9) new conductive thermal modeling in the project area; (10) new convective modeling in the Calibration Area; (11) pseudo-convective modeling in the Calibration Area; (12) enhanced data implications and qualitative geoscience correlations at three scales (a) Regional, (b) Project, and (c) Calibration Area; (13) quantitative geostatistical exploratory data analysis; and (14) responses to nine questions posed in the proposal for this investigation. Enhanced favorability/trust maps were not generated because there was not a sufficient amount of new, fully-vetted (see below) rock type, temperature, and stress data. The enhanced seismic data did generate a new method to infer rock type and temperature (However, in the opinion of the Principal Investigator for this project, this new methodology needs to be tested and evaluated at other sites in the Basin and Range before it is used to generate the referenced maps. As in the baseline conceptual model, the enhanced findings can be applied to both the hydrothermal system and EGS in the Dixie Valley region).

15 GEOTHERMAL ENERGY↗

Steptoe Valley NV Data Compilation: Understanding a Stratigraphic Hydrothermal Resource through Geophysical Imaging

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. Provided here are a compilation of newly acquired data over the area and select modeling efforts. This encompasses a 3D geological model (inclusive of full Leapfrog files, Leapfrog viewer files, and XYZ data for faults and stratigraphy) with embedded geophysical modeling, controlled-source electromagnetic (CSEM) and magnetotelluric (MT) data packages, aqueous spring geochemistry data, seismic reflection interpretations, and a gravity data package. The stratigraphic reservoir in Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, added data which further highlighted potential resource targets in the basin. Geophysical surveys, complimented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a power-capable reservoir is economically accessible in the Paleozoic carbonates of the deep/central basin. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the Steptoe resource. The geophysical tools, interpretations, lessons learned, and publicly available data generated by this study establish an exploration methodology to inform decisions for successful development of stratigraphic reservoirs.

15 GEOTHERMAL ENERGY↗

Experimental Observations of the Topology of Convolutional Neural Network Activations

Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture resulting in high-dimensional, difficult to interpret internal representations of input data. As DNNs become more ubiquitous across multiple sectors of our society, there is increasing recognition that mathematical methods are needed to aid analysts, researchers, and practitioners in understanding and interpreting how these models' internal representations relate to the final classification. In this paper we apply cutting edge techniques from TDA with the goal of gaining insight towards interpretability of convolutional neural networks used for image classification. We use two common TDA approaches to explore several methods for modeling hidden layer activations as high-dimensional point clouds, and provide experimental evidence that these point clouds capture valuable structural information about the model's process. First, we demonstrate that a distance metric based on persistent homology can be used to quantify meaningful differences between layers and discuss these distances in the broader context of existing representational similarity metrics for neural network interpretability. Second, we show that a mapper graph can provide semantic insight as to how these models organize hierarchical class knowledge at each layer. These observations demonstrate that TDA is a useful tool to help deep learning practitioners unlock the hidden structures of their models.

topological data analysis, deep learning↗

Evidence for a Single Holocene Paleoseismic Event on the Pajarito Fault, Northern New Mexico

Low-slip rate fault systems tend to be less studied than their high-slip rate counterparts, and paleoseismic techniques used to study them may pose challenges in interpretation that differ from high-slip rate systems. A good example of this is the Pajarito fault system (PFS), a normal fault complex within the Rio Grande rift. Despite numerous previous paleoseismic trenching studies conducted on the PFS between 1990 and 2003, considerable uncertainty remains regarding its Holocene paleoseismic history, particularly for the primary Pajarito fault (PF). To further clarify the PF paleoseismic history, we present data from paleoseismic investigations of 6 trenches at 3 distinct locations along the PF. Though the totality of the age and structural data obtained in this study is complex and not entirely consistent with any one interpretation, a single Holocene paleoearthquake occurring younger than ∼1,600 to 2,300 kcal yr BP is the simplest interpretation. It is possible that the PF records two Holocene events, with a penultimate event 6.9–2.4 kcal yr BP event and the aforementioned most recent event (MRE) between 2.3 and 1.6 kcal yr BP. However, only a single wall of one trench, out of a total of 12 walls in our 6 trenches, provides evidence supporting that interpretation. This study finds evidence of a single late Holocene paleoseismic event on the PF and sparse evidence for 2 Holocene paleoseismic events on the PF and highlights the benefits of logging multiple trench walls to better understand the complexity that results from this low-slip rate, low-deposition-rate fault system.

58 GEOSCIENCES↗

Methodologies for Reservoir Characterization Using Fluid Inclusion Gas Chemistry

The purpose of this project was to: 1) evaluate the relationship between geothermal fluid processes and the compositions of the fluid inclusion gases trapped in the reservoir rocks; and 2) develop methodologies for interpreting fluid inclusion gas data in terms of the chemical, thermal and hydrological properties of geothermal reservoirs. Phase 1 of this project was designed to conduct the following: 1) model the effects of boiling, condensation, conductive cooling and mixing on selected gaseous species; using fluid compositions obtained from geothermal wells, 2) evaluate, using quantitative analyses provided by New Mexico Tech (NMT), how these processes are recorded by fluid inclusions trapped in individual crystals; and 3) determine if the results obtained on individual crystals can be applied to the bulk fluid inclusion analyses determined by Fluid Inclusion Technology (FIT). Our initial studies however, suggested that numerical modeling of the data would be premature. We observed that the gas compositions, determined on bulk and individual samples were not the same as those discharged by the geothermal wells. Gases discharged from geothermal wells are CO 2 -rich and contain low concentrations of light gases (i.e. H 2 , He, N, Ar, CH4). In contrast many of our samples displayed enrichments in these light gases. Efforts were initiated to evaluate the reasons for the observed gas distributions. As a first step, we examined the potential importance of different reservoir processes using a variety of commonly employed gas ratios (e.g. Giggenbach plots). The second technical target was the development of interpretational methodologies. We have develop methodologies for the interpretation of fluid inclusion gas data, based on the results of Phase 1, geologic interpretation of fluid inclusion data, and integration of the data. These methodologies can be used in conjunction with the relevant geological and hydrological information on the system to create fluid models for the system. The hope is that the methodologies developed will allow bulk fluid inclusion gas analysis to be a useful tool for estimating relative temperatures, identifying the sources and origins of the geothermal fluids, and developing conceptual models that can be used to help target areas of enhanced permeability.

15 GEOTHERMAL ENERGY↗

Seismic Fracture Characterization Methodologies for Enhanced Geothermal Systems

Executive Summary The overall objective of this work was the development of surface and borehole seismic methodologies using both compressional and shear waves for characterizing faults and fractures in Enhanced Geothermal Systems. We used both surface seismic and vertical seismic profile (VSP) methods. We adapted these methods to the unique conditions encountered in Enhanced Geothermal Systems (EGS) creation. These conditions include geological environments with volcanic cover, highly altered rocks, severe structure, extreme near surface velocity contrasts and lack of distinct velocity contrasts at depth. One of the objectives was the development of methods for identifying more appropriate seismic acquisition parameters for overcoming problems associated with these geological factors. Because temperatures up to 300º C are often encountered in these systems, another objective was the testing of VSP borehole tools capable of operating at depths in excess of 1,000 m and at temperatures in excess of 200º C. A final objective was the development of new processing and interpretation techniques based on scattering and time-frequency analysis, as well as the application of modern seismic migration imaging algorithms to seismic data acquired over geothermal areas. The use of surface seismic reflection data at Brady's Hot Springs was found useful in building a geological model, but only when combined with other extensive geological and geophysical data. The use of fine source and geophone spacing was critical in producing useful images. The surface seismic reflection data gave no information about the internal structure (extent, thickness and filling) of faults and fractures, and modeling suggests that they are unlikely to do so. Time-frequency analysis was applied to these data, but was not found to be significantly useful in their interpretation. Modeling does indicate that VSP and other seismic methods with sensors located at depth in wells will be the most effective seismic tools for getting information on the internal structure of faults and fractures in support of fluid flow pathway management and EGS treatment. Scattered events similar to those expected from faults and fractures are seen in the VSP reported here. Unfortunately, the source offset and well depth coverage do not allow for detailed analysis of these events. This limited coverage also precluded the use of advanced migration and imaging algorithms. More extensive acquisition is needed to support fault and fracture characterization in the geothermal reservoir at Brady's Hot Springs. The VSP was effective in generating interval velocity estimates over the depths covered by the array. Upgoing reflection events are also visible in the VSP results at locations corresponding to reflection events in the surface seismic. Overall, the high temperature rated fiber optic sensors used in the VSP produced useful results. Modeling has been found useful in the interpretation of both surface reflection seismic and VSP data. It has helped identify possible near surface scattering in the surface seismic data. It has highlighted potential scattering events from deeper faults in the VSP data. Inclusion of more detailed fault and fracture specific stiffness parameters are needed to fully interpret fault and fracture scattered events for flow properties (Pyrak-Nolte and Morris, 2000, Zhu and Snieder, 2002). Shear wave methods were applied in both the surface seismic reflection and VSP work. They were not found to be effective in the Brady's Hot Springs area. This was due to the extreme attenuation of shear waves in the near surface at Brady's. This does not imply that they will be ineffective in general. In geothermal areas where good shear waves can be recorded, modeling suggests they should be very useful for characterizing faults and fractures.

15 GEOTHERMAL ENERGY↗

Electrical Resistivity Tomography of the 216-U-5 and 216-U-6 WA-1 Waste Sites

The subsurface below the 216-U-5 and 216-U-6 sites needs to be characterized as part of the remedial investigation of the 200-WA-1 operable unit at the Hanford Site. Electrical resistivity tomography (ERT) was selected as a first step in this characterization effort to provide information about the three-dimensional distribution of historically disposed waste fluids in the subsurface beneath these sites. ERT does not directly image specific contaminants. However, ERT provides a three-dimensional image of subsurface zones where the bulk electrical conductivity is higher than surrounding background values. Disposed waste fluids can increase the bulk electrical conductivity in the subsurface where they are present by increasing the moisture content and because the high concentration of waste-fluid constituents such as nitrate cause the fluid to have a higher specific conductance than background pore water. Thus, zones of increased bulk electrical conductivity in the subsurface can be interpreted as zones where waste fluids are present. An ERT survey of the subsurface beneath the 216-U-5 and 216-U-6 sites was conducted to collect ERT data suitable for interpreting the bulk electrical conductivity distribution in the subsurface. Data interpretation was applied using the NQA-1 qualified E4D ERT parallel modelling and inversion code (Johnson 2014). A summary cross-section and interpretation of the 216-U-5 and 216-U-6 ERT is provided in Figure S.1. The ERT image reveals two distinct zones of anomalous bulk conductivity emanating from 216-U-5 and 216-U-6 that merge at a depth of approximately 14 m below the surface and continue to a depth of no more than 26 m below the surface, which is approximately 60 m above the current water table elevation. The image also suggests that lateral transport of waste streams disposed into 216-U-5 and 216-U-6 was limited. These results are consistent with historical groundwater monitoring, which indicated that the disposed waste had not migrated to the water table, possibly due to the relatively low volumes of waste discharge into 216-U-5 and 216-U-6. The ERT information provides a line of evidence supporting this conclusion and can be used to guide the selection of characterization borehole(s) location and depth as needed for additional characterization of the contaminant distribution and properties at these sites. Locations of bulk conductivity anomalies associated with 216-U-5 and 216-U-6 releases are offset approximately 10 m to the west of the locations of 216-U-5 and 216-U-6 as recorded in the Waste Information Data System database. There are two predominant regions of elevated bulk conductivity in addition to those associated with 216-U-5 and 216-U-6. The first is located beneath Beloit Ave. from approximately 190 to 195 m in elevation. The small region of high conductivity directly beneath Beloit Ave. is likely an artifact resulting from the failure to account for the Beloit Ave. topographic rise in the ERT modeling, as that information was unavailable. Information concerning the precise positioning of the railroad tracks shown was also unavailable, particularly in the vertical dimension. The high conductivity anomaly beneath the assumed railroad track location is likely caused by misplacement of the tracks in the ERT model.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Foundations of Molecular 'Isotomics'

The naturally occurring rare isotopes are versions of common elements, such as hydrogen, carbon and oxygen, that contain a larger than usual number of neutrons in their atomic nuclei and therefore are higher in mass than the common atoms of that element. Isotopes exist for most elements and are found in most natural and synthetic materials, but are uneven in their distribution because chemical and physical processes are isotope-selective (e.g., a chemical reaction may proceed more rapidly for one isotope than for another). For this reason, abundances of isotopes in a material of interest can provide a record, or ‘signature’ of various features of that material’s origin and history. These signatures have been used in the geo, life, chemical and physical sciences in a wide variety of ways over close to 8 decades. However, many such applications struggle to reach unique interpretations of isotopic data because multiple factors combine to control a given sample’s overall isotopic content. That is, the factors controlling isotopic content are too numerous and complex to fully constrain from a simple measurement of a material’s isotope abundances. However, the distribution of isotopes within materials, at molecular scales potentially provides a vastly larger number and diversity of constraints on the chemical and physical processes that comprise a material’s history. The rare isotopes may be concentrated into one atomic position in a molecule relative to another, some proportion of molecules in a sample may contain two or more rare isotopes, and those multiply-isotope-substituted forms of molecules may also have uneven distributions of those isotopes across individual atomic sites. For these reasons, even small, seemingly simple molecules, such as sugars, amino acids or drug compounds, actually exist in a vast number of isotopically unique forms (often millions or more), and each one of those forms is in some sense an independent ‘vote’ on that sample’s history. This project has focused on opening this rich archive of information by enabling the creation of routinely and widely applicable ways of measuring and interpreting isotopic structures of molecules. This work has included the development of core technologies and analytical methods, advancing fundamental understanding of the physical and chemical properties of isotopic versions of molecules, and conducting proof of concept studies of illustrative geochemical, cosmochemical and forensic problems in order to show how these technologies, methods and principles come together to solve problems in new ways. A key to the success of this project was the adaptation of ‘Fourier transform mass spectrometry’ (FTMS) to the task of precisely measuring proportions of the rare, naturally occurring isotopic forms of molecules. FTMS is a highly specialized form of mass spectrometry that traps ions within magnetic or electrostatic cavities and, effectively, ‘listens’ (through registering of subtle electrical signals) to the harmonic signals they make while rapidly orbiting within those cavities. These signals have periods that are a function of their mass and strength (or ‘loudness’) that is proportional to their abundances. Thus, these signals constrain relative amounts of molecules that differ in their mass due to various isotopic substitutions. This technology has been essential to the identification of organic molecules in the life, chemical and environmental sciences for over 4 decades, but generally has lacked the control, stability and precision to meaningfully measure rare isotope forms of molecules. This project’s most fundamental contribution has been to modify FTMS, both in terms of hardware and methods, to enable such measurements. The raw data of molecular isotopic structure is tremendously voluminous and complex, so another important activity of this project has been developing the theoretical and data-science tools needed to interpret the data generated by this new form of isotopic measurement. A particularly challenging part of this task has been predicting molecular isotopic structure, as only through the comparison of measurements with predictions can we make progress on hypothesis driven research questions. We have attacked this this prediction task through a combination of first-principles chemical-physics models of the effects of isotope substitution on molecule properties and data-science models that permit us to generalize that chemical physics to cases that have not yet been studied by detailed chemical physics theory. The proof of concept applications we have pursued over the course of this study include biological reactions of amino acids and other biomolecules, non-biological synthesis of organic molecules in extra-terrestrial settings such as meteorites, petroleum geoscience questions concerning the origin and evolution of natural gas, oil and kerogen compounds, and forensic questions such as the sourcing of chemical weapons. The successes of these applications have laid the groundwork for the next phase of this field’s development, which will include larger scale and more ambitious studies of molecular isotopic structure as a means of diagnosing human diseases, such as cancer, and reconstructing detailed interpretations of the origin and evolution of organic molecules in modern and geological environments.

Cesar, Jaime↗

FORESTR: Finding, Organizing, Representing, Explaining, Summarizing, and Thinning Random forests

Random forests have become popular models used for data driven predictions. As a result, random forests are currently used or being considered for high-consequence mission applications in national security, such as the prediction of yield from optical signals and malware detection. While random forests may provide accurate predictions, the complexity of the algorithm causes a lack of interpretability. Random forests are an ensemble of regression or decision trees. Individual regression and decision trees are interpretable, but ensembles are inherently difficult to interpret due to the compilation of many models. We aim to increase the interpretability of random forests by finding patterns in the ensemble of trees that can be used to “thin” (or remove) trees. As a starting point, in this report, we develop a new distance metric for quantifying the similarity between trees based on their topologies (i.e., shapes). We base the metric on a novel distance metric for graphs that is a proper mathematical distance, is invariant to transformations, has registration between graphs, and computes topological evolutions between graphs. We use the tree distance metric to compute tree statistics such as a “mean tree” and to identify clusters of trees. We apply the developed methodology to a toy dataset and a mission relevant product inspection dataset to demonstrate how the metric can provide insight into random forests. Furthermore, we discuss the limitations of the approach and ideas for future research into how the metric could be used as a thinning tool to develop less complex models.

97 MATHEMATICS AND COMPUTING↗

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women

Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep learning and can capture the contextual relationships between words. In this work, we integrated Artificial Intelligence (AI) modules to LDA and BERTopic and provided a comprehensive comparison on the analysis of prescription opioid-related cardiovascular risks in women. Opioid use can increase the risk of cardiovascular problems in women such as arrhythmia, hypotension etc. 1,837 abstracts were retrieved and downloaded from PubMed as of April 2024 using three Medical Subject Headings (MeSH) words: “opioid,” “cardiovascular,” and “women.” Machine Learning of Language Toolkit (MALLET) was employed for the implementation of LDA. BioBERT was used for document embedding in BERTopic. Eighteen was selected as the optimal topic number for MALLET and 23 for BERTopic. ChatGPT-4-Turbo was integrated to interpret and compare the results. The short descriptions created by ChatGPT for each topic from LDA and BERTopic were highly correlated, and the performance accuracies of LDA and BERTopic were similar as determined by expert manual reviews of the abstracts grouped by their predominant topics. The results of the t-SNE (t-distributed Stochastic Neighbor Embedding) plots showed that the clusters created from BERTopic were more compact and well-separated, representing improved coherence and distinctiveness between the topics. Our findings indicated that AI algorithms could augment both traditional and contemporary topic modeling techniques. In addition, BERTopic has the connection port for ChatGPT-4-Turbo or other large language models in its algorithm for automatic interpretation, while with LDA interpretation must be manually, and needs special procedures for data pre-processing and stop words exclusion. Therefore, while LDA remains valuable for large-scale text analysis with resource constraints, AI-assisted BERTopic offers significant advantages in providing the enhanced interpretability and the improved semantic coherence for extracting valuable insights from textual data.

Research & Experimental Medicine↗

It takes a village: using a crowdsourced approach to investigate organic matter composition in global rivers through the lens of ecological theory

Though community-based scientific approaches are becoming more common, many scientific efforts are conducted by small groups of researchers that together develop a concept, analyze data, and interpret results that ultimately translate into a publication. Here, we present a community effort that breaks these traditional boundaries of the publication process by engaging the scientific community from initial hypothesis generation to final publication. We leverage community-generated data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to study organic matter composition through the lens of ecological theory. This community endeavor will use a suite of paired physical and chemical datasets collected from 97 river corridors across the globe. With our first step aimed at ideation, we engaged a community of scientists from 20 countries and 60 institutions, spanning disciplines and career stages by holding a virtual workshop (April 2021). In the workshop, participants generated content for questions, hypotheses, and proposed analyses based on the WHONDRS dataset. These ideation efforts resulted in several narratives investigating different questions led by different teams, which will be the basis for research articles in a Frontiers in Water collection. Currently, the community is collectively analyzing, interpreting, and synthesizing these data that will result in seven crowdsourced articles using a single, existing WHONDRS dataset. The use of a shared dataset across articles not only lowers barriers for broad participation by not requiring generation of new data, but also provides unique opportunities for emergent learning by connecting outcomes across studies. Here we will explain methods used to enable this community endeavor aimed to promote a greater diversity of thinking on river corridor biogeochemistry through community science.

Borton, Mikayla A.↗