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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deep Hierarchical Super Resolution for Scientific Data

We present a novel technique for hierarchical super resolution (SR) with neural networks (NNs), which upscales volumetric data represented with an octree data structure to a high-resolution uniform gridwith minimal seam artifacts on octree node boundaries. Our method uses existing state-of-the-art SR models and adds flexibility to upscale input data with varying levels of detail across the domain, instead of only uniform grid data that are supported in previous approaches.The key is to use a hierarchy of SR NNs, each trained to perform 2x SR between two levels of detail, with a hierarchical SR algorithm that minimizes seam artifacts by starting from the coarsest level of detail and working up.We show that our hierarchical approach outperforms baseline interpolation and hierarchical upscaling methods, and demonstrate the usefulness of our proposed approach across three use cases including data reduction using hierarchical downsampling+SR instead of uniform downsampling+SR, computation savings for hierarchical finite-time Lyapunov exponent field calculation, and super-resolving low-resolution simulation results for a high-resolution approximation visualization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2022

Physical and dynamical characterization of hyperbolic comet C/2017 U7 (PANSTARRS)

Here we present here a dynamical and observational study of the comet C/2017 U7 (PANSTARRS). This comet was discovered in 2017 and found to have a hyperbolic orbit. Our dynamical analysis shows that the object has probably originated in the Oort cloud, however an interstellar origin cannot be discarded. The observations were obtained in 2018 and 2019 using the Goodman High Throughput Spectrograph (GHTS) at the SOAR telescope. We obtained visible spectra covering the wavelength range of 0.5 - 0.9 μm and also images in the SDSS filters system. Both the low-resolution reflectance spectrum and the reflectance spectra derived from the SDSS filters show an atypical band at ~ 0.595 μm. We conducted a comparative study of the colors and reflectance spectra of different small body populations (e.g., comets, Centaurs, and trans-Neptunian objects or TNOs) from the literature and concluded that the spectra and the colors of this comet are atypical, showing only some overlap with those of some known members of the TNOs and Centaurs, within the large uncertainties of the measurements of those populations. It is found that the feature and overall spectral shape can be reproduced by laboratory spectra of kerite, a template for aliphatic-rich hydrocarbons that has been previously identified in NIR cometary spectra absorptions. It is tentatively proposed that the unusual spectral shape is the result of a particle size distribution of dust grains in the coma or on the surface that has arisen due to a low grain ejection velocity from the surface and large nucleus size.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

High-resolution infrared spectroscopy of supersonically cooled singlet carbenes: Bromomethylene (HCBr) in the CH stretch region

First high-resolution spectra of cold (~35 K) singlet bromomethylene HCBr in the CH stretching (v 1 ) region from 2770 to 2850 cm-1 are reported using near quantum shot-noise limited laser absorption methods in a slit jet supersonic discharge expansion source. Three rovibrational bands are identified at high S/N (20:1 - 40:1) and rotationally assigned to i) the CH stretch fundamental (v 1 ) band $\tilde{X}$(1,0,0) ← $\tilde{X}$ (0,0,0) and ii) vibrational hot bands ($\tilde{X}$(1,1,0) ← $\tilde{X}$(0,1,0) and $\tilde{X}$(1,0,1) ← $\tilde{X}$(0,0,1)) arising from vibrationally excited HCBr populated in the discharge with single quanta in either the H–C–Br bend (v 2 ) or C–Br stretch (v 3 ) modes. Precision rotational constants are reported for a total of six states, with an experimentally determined CH stretch vibrational frequency (2799.38 cm -1 ) in good agreement with previous low-resolution fluorescence studies. Detailed analysis of the fundamental v 1 band highlights the presence of perturbations in the $\tilde{X}$(1,0,0) level, which we tentatively attribute to arise from the nearby triplet state $\tilde{a}$(0,0,1) through spin-orbit interaction or the multiple quanta $\tilde{X}$(0,2,1) singlet state via c-type Coriolis coupling. Reduced-Doppler resolution (60 MHz) in the slit-jet IR spectrometer permits clear observation of nuclear spin hyperfine structure, with experimental line shapes well reproduced by nuclear quadrupole/spin-rotation coupling constants from microwave studies. Lastly, the a-type to b-type transition intensity ratio for the fundamental CH stretch band is notably larger than predicted by a bond-dipole model, which from high level ab initio quantum calculations (CCSD(T)/PVQZ) can be attributed to vibrationally induced “charge-sloshing” of electron density along the polar C–Br bond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1997

The Link Between UV Extinction and Infrared Cirrus

Low resolution spectra from the International Ultraviolet Explorer satellite were used to derive ultraviolet extinction curves for stars in four clusters away from the galactic plane. The extinction in three of the clusters is very similar to the general interstellar curve defined by Seaton. Stars in the fourth region, near the Rho Ophiuci dark cloud, have extinction curves that are characterized by a small "linear" term component. The star BD +36 deg 781 is unique amongst the 20 stars observed in that it shows evidence for extinction by diamond grains near 1700 angstroms. We used data from the final release of the IRAS Sky Survey Atlas (ISSA) to determine the 60 micron to 100 micron intensity ratio for the infrared cirrus. The ISSA data, which have been corrected for zodiacal light, gave intensity ratios that are more robust and self-consistent than for other data sets that we used. When the infrared and ultraviolet data are combined, we see a general trend for low values of the ultraviolet "linear term" (al) to correlate with high values of 60 micron/100 micron ratio. This implies that, in regions where the average dust temperature is hotter (high 60 micron/100 micron ratio), there is a relative absence of the small silicate grains that are responsible for the ultraviolet linear term. However, the new data do not bear out our earlier contention that the 60 micron and 100 micron emissions are poorly correlated spatially in regions where the 60 micron/100 micron ratio is low. Only NGC 1647 shows this result. It may be that the different dust types are particularly poorly mixed in this area.

Hackwell, John A.↗
NASA NTRS2002

What Is Happening at Spectral Type F5 in Hyades F Stars?

Aiming at a better understanding of the mechanisms heating the chromospheres, transition regions, and coronae of cool stars, we study ultraviolet, low-resolution Hubble Space Telescope/Space Telescope Imaging Spectrograph spectra of Hyades main-sequence F stars. We study the B-V dependence(s) of the chromospheric and transition layer emission line fluxes and their dependences on rotational velocities. We find that the transition layer emission line fluxes and also those of strong chromospheric lines decrease steeply between B-V = 0.42 and 0.45, i.e., at spectral type F5, for which the rotational velocities also decrease steeply. The magnitude of the line-flux decrease increases for lines of ions with increasing degree of ionization. This shows that the line-flux decrease is not due to a change in the surface filling factor but rather due to a change of the relative importance of different heating mechanisms. For early F stars with B-V < 0.42 we find for the transition layer emission lines increasing fluxes for increasing v sin i, indicating magnetohydrodynamic heating. The v sin i dependence is strongest for the high-ionization lines. On the other hand, the low chromospheric lines show no dependence on v sin i, indicating acoustic shock heating for these layers. This also contributes to the heating of the transition layers. The Mg II and Ca II lines show decreasing fluxes for increasing v sin i, as long as v sin i is less than approx. 40 km/s. The coronal X-ray emission also decreases for increasing v sin i, except for v sin i larger than approx. 100 km/s. We have at present no explanation for this behavior. For late F stars the chromospheric lines show v sin i dependences similar to those observed for early F stars, again indicating acoustic heating for these layers. We were unable to determine the v sin i dependence of the transition layer lines because of too few single star targets. The decrease of emission line fluxes at the spectral type F5, with steeply decreasing v sin i, indicates, however, a decreasing contribution of magnetohydrodynamic heating for the late F stars. The X-ray emission for the late F stars increases for increasing v sin i, indicating magnetohydrodynamic heating for the coronae of the late F stars, different from the early F stars.

Boehm-Vitense, Erika↗
DOE OSTI2024

A Machine Learning Bias Correction on Large–Scale Environment of High–Impact Weather Systems in E3SM Atmosphere Model

Large–scale dynamical and thermodynamical processes are common environmental drivers of high–impact weather systems causing extreme weather events. However, such large–scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high–impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large–scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ~1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high–impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large–scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias–corrected large–scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large–scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large–scale storm environments simulated by low–resolution climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using row-resolution (i.e., 15 minutes) smart meter data. We first demonstrate that the electricity consumption profiles on mild-temperature days can be used to approximate the base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed from the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and verify the proposed methods. Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers.

Kim, Hyeonjin↗
NASA NTRS2012

Metallicities of Emission-Line Galaxies from HST ACS PEARS and HST WFC3 ERS Grism Spectroscopy at 0.6 is less than z is less than 2.4

Galaxies selected on the basis of their emission line strength. show low metallicities, regardless of their redshifts. We conclude this from a sample of faint galaxies at redshifts between 0.6 < z < 2.4, selected by their prominent emission lines in low resolution grism spectra in the optiCa.i with the Advanced Camera for Surveys (ACS) on the Hubble Space Telescope (HST) and in the near-infrared using Wide-Field Camera 3 (WFC3). Using a sample of 11 emission line galaxies (ELGs) at 0.6 < z < 2.4 with luminosities of -22 approx < MB approx -19 which have [OII], H-Beta, and [OIII] line flux measurements from the combination of two grism spectral surveys, we use the R23 method to derive the gas-phase oxygen abundances: 7.5 <12+log(0/H)<8.5. The galaxy stellar masses are derived using Bayesian based Markov Chain Monte Carlo (pi MC(exp 2)) fitting of their Spectral Energy Distribution (SED), and span the mass range 8.1 < log(M(stellar)/M(solar)) < 10.1. These galaxies show a mass-metal1icity (M-L) and Luminosity-Metallicity (LZ) relation, which is offset by -<J.6 dex in meta1licity at given absolute magnitude and stellar mass relative to the local SDSS galaxies, as well as continuum selected DEEP2 samples at similar redshifts. The emission-line selected galaxies most resemble the local "green peas" galaxies and Lyman-alpha galaxies at z approx = 0.3 and z approx = 2.3 in the M-Z and L-Z relations and their morphologies. The G - M(sub 20) morphology analysis shows that 10 out of 11 show disturbed morphology, even as the star-forming regions are compact. These galaxies may be intrinsically metal poor, being at early stages of formation, or the low metallicities may be due to gas infall and accretion due to mergers.

Xia, Lifang↗
DOE OSTI2020

Variability and Diversity Load Model Tool [SWR-20-03]

The motivation for the development of this tool and the underlying algorithms and methods was to enable the development of high-temporal resolution, realistic time-series data for quasi-static time-series (QSTS) analysis of distribution systems. Often, aggregated load profile data for a distribution circuit is available (e.g. feeder loading data collected via SCADA at the utility substation) and, while this data is typically accurate it masks the considerable variability of the 100’s or 1000’s of individual loads connected on the circuit. This tool was developed to model both the increased variability expected for these individual loads (e.g. the load of a single distribution transformer connected to 8-12 houses) and the expected diversity between loads on the circuit. It is important to note that the difference in variability and diversity, in the context of this tool, is that variability modeling only adds representative variability due to disaggregated load characteristics (e.g. the presence in the load profile of loads turning off and on like an air conditioner/oven) while the average energy profile remains the same as the user supplied power profile. Diversity modeling generates multiple individual load profiles which, in aggregate, sum to the user supplied power profile. Diversity is effectively variability in the energy usage over longer periods of time than seen in the variability model. Put another way, variability modeling supplies the expected variability due to the operation of various end-use loads and diversity modeling supplies the usage differences due to human behavior, schedules, etc. This load modeling tool was developed for use in generating data for distribution systems. Modeling is summarized by two major functions: 1) taking low resolution load profiles and adding intra-seconds variability onto the profiles, and 2) taking a user supplied load profile and distribution factors and adding both diversity and variability to the user supplied profile.

Zhu, Xiangqi↗
NASA NTRS1988

Imaging and spectroscopy of Comet P/Halley

The goals of this investigation are the analysis of a large set of high-resolution echelle/reticon spectra, and the reduction and analysis of a set of narrow-band-filtered charge coupled device (CCD) images of Comet Halley taken during the preperihelion period at Oak Ridge Observatory by Dr. R. E. McCrosky. The scientific objectives associated with these goals are the determination of the spatial distributions of several important radicals, atoms and ions in the coma. These include C2, CN, C3, H2O(+) and CO(+) from the image data. The analysis of the neutral species distribution with Monte Carlo models will aid in the understanding of their production and decay mechanisms as well as serve as an important indicator of the physical conditions in the inner coma. The spatial distributions of the ions will serve as a guide to constrain the complex model necessary for understanding the interaction of the solar wind and the cometary ions. Work during this past year has been devoted largely to the reduction of the standard star photometry for the CCD image data set, as well as the re-flat-fielding of a number of the comet images. We are pleased to report that despite a number of setbacks and the small effort devoted to this work (2 1/2 months for the PI and a generous share of completely unsupported time by Dr. McCrosky) that this portion of the work has been successfully completed. The goals for the upcoming final year of this project (under a new project number) are to complete the calibration of the CCD image data for inclusion in the IHW archive, to analyze a select portion of the neutral radical images with our Monte Carlo models, and to present the results of the 6300/region spectra as a guide to low-resolution spectral observers in order to yield the unambiguous separation of the contributions of cometary O(1D), airglow O(1D), and the numerous NH2 lines in that region of the spectrum.

Combi, Michael R.↗
NASA NTRS1993

Collisional and dynamical history of Gaspra

Interpretation of the impact record on Gaspra requires understanding of the effects of collisions on a target body of Gaspra's size and shape, recognition of impact features that may have different morphologies from craters on larger planets, and models of the geological processes that erase and modify impact features. Crater counts on the 140 sq km of Gaspra imaged at highest resolution by the Galileo spacecraft show a steep size-frequency distribution (cumulative power-law index near -3.5) from the smallest resolvable size (150 m diameter) up through the large feature (1.5 km diameter crater) of familiar crater-like morphology. In addition, there appear to be as many as eight roughly circular concavities with diameters greater than 3 km visible on the asteroid. If we restrict our crater counts to features with traditionally recognized crater morphologies, these concavities would not be included. However, if we define craters to include any concave structures that may represent local or regional damage at an impact size, then the larger features on Gaspra are candidates for consideration. Acceptance of the multi-km features as craters has been cautious for several reasons. First, scaling laws (the physically plausible algorithms for extrapolating from experimental data) indicate that Gaspra could not have sustained such large-crater-forming impacts without being disrupted; second, aside from concavity, the larger structures have no other features (e.g. rims) that can be identified with known impact craters; and third, extrapolation of the power-law size distribution for smaller craters predicts no craters larger than 3 km over the entire surface. On the other hand, recent hydrocode modeling of impacts shows that for given impact (albeit into a sphere), the crater size is much larger than given by scaling laws. Gaspra-size bodies can sustain formation of up to 8-km craters without disruption. Besides allowing larger impact craters, this result doubles the lifetime since the last catastrophic fragmentation event up to one billion years. Events that create multi-km craters also globally damage the material structure, such that regolith is produced, whether or not Gaspra 'initially' had a regolith, contrary to other models in which initial regolith is required in order to allow current regolith. Because the globally destructive shock wave precedes basin formation, crater size is closer to the large size extrapolated from gravity-scaling rather than the strength-scaling that had earlier been assumed for such small bodies. This mechanism may also help explain the existence of Stickney on Phobos. Moreover, rejection of the large concavities as craters based on unfamiliar morphology would be premature, because (aside from Stickney) we have no other data on such large impact structures on such a small, irregular body. The eight candidate concavities cover an area greater than that counted for smaller craters, because they are most apparent where small craters cannot be seen: on low resolution images and at the limb on high resolution images. We estimate that there are at least two with diameter greater than 4 km per 140 sq km, which would have to be accounted for in any model that claims these are impact craters.

Greenberg, R.↗
DOE OSTI2023

Additive Manufacturing of Self‐Sensing Carbon Fiber Composites

Carbon fiber-reinforced polymer (CFRP) composites have gained substantial attention across various industries owing to their exceptional mechanical properties and lightweight nature. The emergence of additive manufacturing technologies brings new opportunities to the industry, offering advantages such as design freedom, rapid prototyping, and customization. However, the fabrication of CFRP composites through 3D printing techniques poses challenges pertaining to low resolution and limitations in complex geometry realization. This work introduces digital light processing printing as a versatile, high-resolution method ideal for CFRP composite fabrication. Furthermore, the development and characterization of CFRP are focused on and the manipulation of mechanical properties through variations in matrix resins and fiber loadings is investigated, showcasing the versatility of CFRP composites for tailored applications. Additionally, the integration of self-sensing capabilities in CFRP structures is explored, which opens up opportunities for applications in smart components for automotive and structural health monitoring.

carbon fiber composites↗
DOE OSTI2025

Structures of a synthetic antibody selected against and bound to the C‐terminal domain of Clostridium perfringens enterotoxin

Abstract Clostridium perfringensenterotoxin (CpE) causes cytotoxic gastrointestinal disease in mammalian epithelium by binding membrane protein receptors called claudins. Claudins direct the formation of cell/cell tight junctions through oligomerization and govern the transport of molecules between individual cells. CpE binds claudins through its C‐terminal domain (cCpE) and induces cytotoxicity through its N‐terminal domain. The non‐toxic cCpE is a useful tool to study claudins, tight junctions, and for translational applications, such as increasing the permeability of restrictive tissues like the blood–brain barrier or selective targeting of claudin overexpressing cancers. Conversely, there are no specialized molecular tools to study CpE or cCpE, or to modulate or inhibit their functions. We previously reported the development of synthetic antigen‐binding fragments (sFabs) that bind cCpE, and low‐resolution structures of them bound to claudin/cCpE complexes. Here, we determine high‐resolution structures of sFab COP‐2 bound to cCpE using X‐ray crystallography and cryogenic electron microscopy. The structures and biophysical findings provide the mechanism of COP‐2 binding to cCpE and the molecular determinants driving their interactions. These insights can advance the design of new antibody‐based tools from our COP‐2 scaffold to study or alter cCpE function and give rise to a “Trojan horse” strategy that exploits cCpE's tight junction barrier disrupting function to selectively deliver conjugated therapeutics through normally impermeable tissues.

Biochemistry & Molecular Biology↗
DOE OSTI2024

FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization

Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free-stream wind conditions. Physics-based estimates of wind farm power production typically rely on low-fidelity engineering wake models that approximate the steady-state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed-form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient-based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.

17 WIND ENERGY↗
DOE OSTI2020

A comparative look at structural variation among RC–LH1 ‘Core’ complexes present in anoxygenic phototrophic bacteria

All purple photosynthetic bacteria contain RC–LH1 ‘Core’ complexes. The structure of this complex from Rhodobacter sphaeroides, Rhodopseudomonas palustris and Thermochromatium tepidum has been solved using X-ray crystallography. Recently, the application of single particle cryo-EM has revolutionised structural biology and the structure of the RC–LH1 ‘Core’ complex from Blastochloris viridis has been solved using this technique, as well as the complex from the non-purple Chloroflexi species, Roseiflexus castenholzii. It is apparent that these structures are variations on a theme, although with a greater degree of structural diversity within them than previously thought. Furthermore, it has recently been discovered that the only phototrophic representative from the phylum Gemmatimonadetes, Gemmatimonas phototrophica, also contains a RC–LH1 ‘Core’ complex. At present only a low-resolution EM-projection map exists but this shows that the Gemmatimonas phototrophica complex contains a double LH1 ring. This short review compares these different structures and looks at the functional significance of these variations from two main standpoints: energy transfer and quinone exchange.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Three-dimensional seismic characterization and imaging of the Soda Lake geothermal field

Accurate characterization of subsurface geophysical properties and detection of the fault system are essential for geothermal energy exploration and production. The Soda Lake geothermal field is in western Nevada with a complex fault system. Previous seismic characterization only produced a low-resolution, smooth velocity model along with a simple, conceptual fault model. Using optimized correlation-based full-waveform inversion, wavefield-separation-based reverse-time migration, and automatic fault detection techniques, we present 3D seismic characterization for the Soda Lake geothermal field using 3D surface seismic data acquired with Vibroseis sources. Here, we obtain 3D high-resolution velocity, density, and acoustic impedance models, 3D seismic images with different grid spacings, and a high-resolution fault system. Consistency check between the constructed faults and currently active injection and production geothermal wells verifies that our seismic inversion and imaging results and detected faults are reliable. These results can provide valuable information for optimizing well placement and geothermal energy production at the Soda Lake geothermal field.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Interferometric SAR modelling of near surface data to improve geological model in the Surat Basin, Australia

Here we present a study on geophysical inverse modelling for subsurface structural properties of an unconventional hydrocarbon site that was monitored previously by Interferometric Synthetic Aperture Radar (InSAR) technology for surface deformation. A static three-dimensional geomodel along with extracted property maps replicates the depth of each underlying stratigraphic unit and structural feature with the density of each geological layer. We examine the hypothesis that integration of elastic properties of each formation layer with InSAR observations in a stratified elastic medium will lead to a viscoelastic geophysical inverse problem that can be solved to estimate fractional volume change at the reservoir level. Moreover, we examine synthetic scenarios in which the elastic properties of the formations are perturbed before determining the resulting impact on the rate of surface deformation. The results show that although the slope of underlying formations, their density and depth can define the extent and pattern of a deformation signal, their properties have a marginal impact on volumetric change compared to the dense network of shallow depth Coal Seam Gas (CSG) mining wells. Besides, it is also demonstrated that the inversion of InSAR deformation maps can resolve the uncertainties associated with low-resolution seismic interpretation as well as filling the data gaps within seismic acquisitions. A significant contribution of this investigation to the geological basin modelling involves a) introducing a remote and non-invasive technology such as InSAR to improve geophysical mapping of subsurface structures such as faults in areas with sparse or no reflective seismic information, and b) applying a multi-layer viscoelastic geophysical source model for an unconventional hydrocarbon reservoir such as CSG.

58 GEOSCIENCES↗