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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

VLT/Magellan spectroscopy of 29 strong lensing selected galaxy clusters

We present an extensive spectroscopic follow-up campaign of 29 strong lensing (SL) selected galaxy clusters discovered primarily in the Second Red-Sequence Cluster Survey (RCS-2). Our spectroscopic analysis yields redshifts for 52 gravitational arcs present in the core of our galaxy clusters, which correspond to 35 distinct background sources that are clearly distorted by the gravitational potential of these clusters. These lensed galaxies span a wide redshift range of 0.8 ≤ z ≤ 2.9, with a median redshift of z{sub s} = 1.8 ± 0.1. We also measure reliable redshifts for 1004 cluster members, allowing us to obtain robust velocity dispersion measurements for 23 of these clusters, which we then use to determine their dynamical masses by using a simulation-based σ{sub DM} − M{sub 200} scaling relation. The redshift and mass ranges covered by our SL sample are 0.22 ≤ z ≤ 1.01 and 5×10{sup 13}⩽M{sub 200}/h{sub 70}{sup −1} M{sub ⊙}⩽1.9×10{sup 15}, respectively. We analyze and quantify some possible effects that might bias our mass estimates, such as the presence of substructure, the region where cluster members are selected for spectroscopic follow-up, the final number of confirmed members, and line-of-sight effects. We find that 10 clusters of our sample with N{sub mem} ≳ 20 show signs of dynamical substructure. However, the velocity data of only one system is inconsistent with a uni-modal distribution. We therefore assume that the substructures are only marginal and not of comparable size to the clusters themselves. Consequently, our velocity dispersion and mass estimates can be used as priors for SL mass reconstruction studies and also represent an important step toward a better understanding of the properties of the SL galaxy cluster population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Correlations between Salt-Induced Crystallization, Morphology, Segmental Dynamics, and Conductivity in Amorphous Block Copolymer Electrolytes

Block copolymer electrolytes have been shown to increase the cycle life of rechargeable batteries that utilize high capacity lithium anodes. Most block copolymer electrolyte studies have been centered on polystyrene-b-poly(ethylene oxide) (SEO) mixed with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt and are limited to salt concentrations in the vicinity of r[Li]/[EO] = 0.1, where [Li] and [EO] are the concentration of lithium and ethylene oxide moieties, respectively, as the conductivity of poly(ethylene oxide) (PEO) homopolymer electrolytes is maximized at this concentration.Here, in this paper, we study the morphology and conductivity of electrolytes derived from a high molecular weight SEO block copolymer over the wide LiTFSI concentration range of 0 ≤ r ≤ 0.550. For electrolytes with r ≥ 0.125, the crystallization of PEO-LiTFSI complexes with stoichiometric ratios of 6:1, 3:1, or 2:1 (EO:Li) is shown to correlate with morphology as determined by small-angle X-ray scattering (SAXS) and scanning transmission electron microscopy (STEM): some samples that are semicrystalline at room temperature exhibit regions of periodic lamellar ordering, whereas those that do not crystallize have aperiodic packed-ellipsoid morphologies. SAXS profiles below and above the melting temperatures of the crystals are identical, indicating that the crystals are confined within the block copolymer domains. The conductivities of SEO/LiTFSI mixtures with r = 0.275, r = 0.300, and r = 0.350 are within experimental error of PEO/LiTFSI at the same salt concentrations, in spite of the presence of insulating polystyrene (PS) domains in the SEO/LiTFSI samples. We attribute this to enhanced segmental dynamics of the PEO chains in the SEO electrolytes, based on differential scanning calorimetry (DSC) measurements of glass transition temperatures. The dependence of ionic conductivity on salt concentration in the SEO/LiTFSI electrolytes at temperatures above the melting temperature of the crystalline complexes shows three local maxima that correlate with the formation of crystalline complexes when r ≥ 0.125. Finally, these maxima arise due to a combination of the enhanced segmental dynamics and changes in morphology observed by SAXS and STEM.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Direct evaluation of dynamical large-deviation rate functions using a variational ansatz

We describe a simple form of importance sampling designed to bound and compute large-deviation rate functions for time-extensive dynamical observables in continuous-time Markov chains. We start with a model, defined by a set of rates, and a time-extensive dynamical observable. We construct a reference model, a variational ansatz for the behavior of the original model conditioned on atypical values of the observable. Direct simulation of the reference model provides an upper bound on the large-deviation rate function associated with the original model, an estimate of the tightness of the bound, and, if the ansatz is chosen well, the exact rate function. The exact rare behavior of the original model does not need to be known in advance. We use this method to calculate rate functions for currents and counting observables in a set of network- and lattice models taken from the literature. Straightforward ansätze yield bounds that are tighter than bounds obtained from Level 2.5 of large deviations via approximations that involve uniform scalings of rates. Here, we show how to correct these bounds in order to recover the rate functions exactly. Our approach is complementary to more specialized methods and offers a physically transparent framework for approximating and calculating the likelihood of dynamical large deviations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The eROSITA Final Equatorial-Depth Survey (eFEDS)

In 2019, the eROSITA telescope on board the Russian-German satellite Spectrum-Roentgen-Gamma (SRG) began to perform a deep all-sky X-ray survey with the aim of identifying ~100 000 clusters and groups over the course of four years. As part of its performance verification phase, a ~140 deg 2 survey, called eROSITA Final Equatorial-Depth Survey (eFEDS), was performed. With a depth typical of the all-sky survey after four years, it allows tests of tools and methods as well as improved predictions for the all-sky survey. As part of this effort, a catalog of 542 X-ray selected galaxy group and cluster candidates was compiled. Here, in this paper, we present the optical follow-up, with the aim of providing redshifts and cluster confirmation for the full sample. Furthermore, we aim to provide additional information on the dynamical state, richness, and optical center of the clusters. Finally, we aim to evaluate the impact of optical cluster confirmation on the purity and completeness of the X-ray selected sample. We used optical imaging data from the Hyper Suprime-Cam Subaru Strategic Program and from the Legacy Survey to identify optical counterparts to the X-ray detected cluster candidates. We make use of the multi-component matched filter cluster confirmation tool (MCMF), as well as of the optical cluster finder CAMIRA to derive cluster redshifts and richnesses. MCMF provided the probabilities with which an optical structure would be a chance superposition with the X-ray candidate. These probabilities were used to identify the best optical counterpart as well as to confirm an X-ray candidate as a cluster. The impact of this confirmation process on catalog purity and completeness was estimated using optical to X-ray scaling relations as well as simulations. The resulting catalog was furthermore matched with public group and cluster catalogs. Optical estimators of the cluster dynamical state were constructed based on density maps of the red-sequence galaxies at the cluster redshift. By providing redshift estimates for all 542 candidates, we construct an optically confirmed sample of 477 clusters and groups with a residual contamination of 6%. Of these, 470 (98.5%) are confirmed using MCMF, and 7 systems are added through cross-matching with spectroscopic group catalogs. Using observable-to-observable scaling and the applied confirmation threshold, we predict that 8 ± 2 real systems have been excluded with the MCMF cut required to build this low-contamination sample. This number agrees well with the 7 systems found through cross-matching that were not confirmed with MCMF. The predicted redshift and mass distribution of this catalog agree well with simulations. Thus, we expect that these 477 systems include >99% of all true clusters in the candidate list. Using an MCMF-independent method, we confirm that the catalog contamination of the confirmed subsample is 6 ± 3%. Application of the same method to the full candidate list yields 17 ± 3%, consistent with estimates coming from the fraction of confirmed systems of ~17% and with expectations from simulations of ~20%. We also present a sample of merging cluster candidates based on the derived estimators of the cluster dynamical state.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Mass calibration of the CODEX cluster sample using SPIDERS spectroscopy – II. The X-ray luminosity–mass relation

ABSTRACT We perform the calibration of the X-ray luminosity–mass scaling relation on a sample of 344 CODEX clusters with z < 0.66 using the dynamics of their member galaxies. Spectroscopic follow-up measurements have been obtained from the SPIDERS survey, leading to a sample of 6658 red member galaxies. We use the Jeans equation to calculate halo masses, assuming an NFW mass profile and analysing a broad range of anisotropy profiles. With a scaling relation of the form $L_{\rm {X}} \propto \text{A}_{\rm {X}}M_{\text{200c}}^{\text{B}_{\rm {X}}} E(z)^2 (1+z)^{\gamma _{\rm {X}}}$, we find best-fitting parameters $\text{A}_{\rm {X}}=0.62^{+0.05}_{-0.06} (\pm 0.06)\times 10^{44}\, \mathrm{erg\, s^{-1}}$, $\text{B}_{\rm {X}}=2.35^{+0.21}_{-0.18}(\pm 0.09)$, $\gamma _{\rm {X}}=-2.77^{+1.06}_{-1.05}(\pm 0.79)$, where we include systematic uncertainties in parentheses and for a pivot mass and redshift of $3\times 10^{14}\, \mathrm{M}_\odot$ and 0.16, respectively. We compare our constraints with previous results, and we combine our sample with the SPT SZE-selected cluster subsample observed with XMM–Newton extending the validity of our results to a wider range of redshifts and cluster masses.

Capasso, R.↗
DOE OSTI2017

Combining configurational energies and forces for molecular force field optimization

While quantum chemical simulations have been increasingly used as an invaluable source of information for atomistic model development, the high computational expenses typically associated with these techniques often limit thorough sampling of the systems of interest. It is therefore of great practical importance to use all available information as efficiently as possible, and in a way that allows for consistent addition of constraints that may be provided by macroscopic experiments. We propose a simple approach that combines information from configurational energies and forces generated in a molecular dynamics simulation to increase the effective number of samples. Subsequently, this information is used to optimize a molecular force field by minimizing the statistical distance similarity metric. We also illustrate the methodology on an example of a trajectory of configurations generated in equilibrium molecular dynamics simulations of argon and water and compare the results with those based on the force matching method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

Programmable temperature control system for biological materials

A system was constructed which allows programmable temperature-time control for a 5 cu cm sample volume of arbitrary biological material. The system also measures the parameters necessary for the determination of the sample volume specific heat and thermal conductivity as a function of temperature, and provides a detailed measurement of the temperature during phase change and a means of calculating the heat of the phase change. Steady-state and dynamic temperature control is obtained by supplying heat to the sample volume through resistive elements constructed as an integral part of the sample container. For cooling purposes, this container is totally immersed into a cold heat sink. Using a mixture of dry ice and alcohol at 79 C, the sample volume can be controlled from +40 to -60 C at rates from steady state to + or - 65 C/min. Steady-state temperature precision is better than 0.2 C, while the dynamic capability depends on the temperature rate of change as well as the mass of both the sample and the container.

Anselmo, V. J.↗
NASA NTRS2016

A Quantum-Assisted Algorithm for Sampling Applications in Machine Learning

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact in deep learning and other machine learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggests it will do so with an instance-dependent effective temperature, different from the physical temperature of the device. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this talk, we present a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a kind of restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature we find a performance close to that of CD-100 for the case studied here. We discuss generalizations of the algorithm to other more expressive generative models, beyond restricted Boltzmann machines.

Perdomo-Ortiz, Alejandro↗
NASA NTRS2024

Accelerating Continuous Variable Coherent Ising Machines Via Momentum

The Coherent Ising Machine (CIM) is a non-conventional architecture that takes inspiration from physical annealing processes to solve Ising problems heuristically. Its dynamics are naturally continuous and described by a set of ordinary differential equations that have been proven to be useful for the optimization of continuous variables nonconvex quadratic optimization problems. The dynamics of such Continuous Variable CIMs (CV-CIM) encourage optimization via optical pulses whose amplitudes are determined by the negative gradient of the objective; however, standard gradient descent is known to be trapped by local minima and hampered by poor problem conditioning. In this work, we propose to modify the CV-CIM dynamics using more sophisticated pulse injections based on tried-and-true optimization techniques such as momentum and Adam. Through numerical experiments, we show that the momentum and Adam updates can significantly speed up the CV-CIM’s convergence and improve sample diversity over the original CV-CIM dynamics. We also find that the Adam-CV-CIM’s performance is more stable as a function of feedback strength, especially on poorly conditioned instances, resulting in an algorithm that is more robust, reliable, and easily tunable. More broadly, we identify the CIM dynamical framework as a fertile opportunity for exploring the intersection of classical optimization and modern analog computing.

Ising Model↗
NASA NTRS1990

The mass function of Seyfert 1 nuclei

The first mass function of Seyfert 1 nuclei is derived from optical spectra of the complete CfA sample of Seyfert galaxies by estimating the mass for each object from a dynamical relation. An independent estimate is also derived using a complete infrared-selected sample. The two mass functions are indistinguishable. The mean mass of Seyfert 1 nuclei is about 2 x 10 to the 7th solar masses, and the integrated mass density is about 6 x 10 to the 11th solar masses/cu Gpc. This is approximately two orders of magnitude less than the value inferred from the energetics associated with quasar counts. A careful analysis of the various parameters and assumptions involved suggests that this large difference is not due to systematic errors in the determinations. Therefore, the bulk of mass related to the accretion processes connected with past quasar activity does not reside in Seyfert 1 nuclei. Instead, the remnants of past activity must be present in a much larger number of galaxies, and a one-to-one relation between distant and local active galactic nuclei seems then to be excluded.

Padovani, P.↗
NASA NTRS1988

Comparative properties of Virgo Cluster dwarf irregulars and spirals

The optical and neutral hydrogen data for all spiral and late-type dwarf irregular galaxies in the Virgo Cluster catalog are analyzed. In particular, the continuity of optical properties, hydrogen masses, and dynamical properties are examined as functions of morphology and luminosity from the largest spirals through the faintest dwarfs (omitting blue compact dwarf galaxies); the effects of environment on H I content; mass segregation; and the Tully-Fisher relations. The spiral plus dwarf sample forms a continuous but nonhomologous sequence. Indicative dynamical mass-to-light ratios are relatively constant throughout; hydrogen mass-to-light ratios show only a slight increase with decreasing luminosity. The Tully-Fisher relations extend with continuous slope from spirals through dwarfs. The dwarfs show some evidence of ram-pressure stripping by the intracluster medium, but as a group do not seem to be stripped more heavily than spirals. There is no evidence of mass segregation even for the very low mass dwarfs versus giant spirals.

Hoffman, G. Lyle↗
NASA NTRS2011

Three-Dimensional Model for Preservation and Restoration of Architectural Heritage

Thc aim of the research will be to create a model, three-dimensional mathematical. implementation. consultation and assistance to "large" restoration projects that will assist the structural analysis, allowing easier display of dynamic strain. analysis and lighting noise. It could also be a valuable tool for decision support. therefore. may simulate several possible scenarios for intervention, This model appears therefore an excellent support for recovering. ordering and monitoring information about materials and data (stage of restoration. photographs. sampling points. results of diagnostic tests, etc.) collected dynamically during the "life" of the cultural heritage. allowing to document its complete history

Marchis, Elena↗
DOE OSTI2022

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

Grid operators can address the inherently stochastic nature of renewables by solving a two-stage stochastic programming model that minimizes the cost of dispatch decisions while accounting for the complex grid dynamics. It is common to use a sample average approximation to estimate the expectation of the second stage costs in this model. However, the large sample count needed for numerical accuracy makes effective modeling large-scale electric grids computationally intractable. We introduce a control variate multi-fidelity estimator for the second-stage recourse that enables high quality dispatch decisions in real-time with a reduced computational burden. We obtain a hierarchy of model fidelities by linearizing the AC power flow system representation to DC power flow, and by relaxing transmission and voltage network constraints. We evaluate the performance of our proposed method on a synthetic grid with 73 buses against a deterministic baseline with persistence forecast and a high-fidelity reference. Our analysis shows a computational speed-up of 7.62x with a minimal loss in accuracy. The multi-fidelity method is well suited to fidelity combinations that use a simplified network topology in their lower fidelity model and is an attractive option for applications where accurate grid modeling needed on a limited computational budget.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Shock compression of crystalline TeO 2 to the high-pressure fluid regime: Insights from ab initio molecular dynamics simulations

The shock response of fully-dense and porous crystalline tellurium dioxide (TeO 2 ⁠) to the high-pressure and high-temperature fluid regime was investigated within the framework of density functional theory with Mermin’s generalization to finite temperatures. The principal and porous shock Hugoniot curves were predicted from canonical ab initio molecular dynamics (AIMD) simulations, with the phase space sampled along isotherms up to 80 000 K, for densities ranging from ρ = 3 to 17 g/cm 3 . The polymorphs investigated are α-TeO 2 paratellurite (⁠P4 1 2 1 2), TeO 2 cotunnite (⁠Pnma⁠), and TeO 2 post-cotunnite (⁠P2 1 /m⁠). Based on the discontinuity found in the calculated U s – u p slope of TeO 2 post-cotunnite at a shock velocity of U s ≃ 8.35km/s and a particle velocity of u p ≃ 3.64 km/s, the shock melting temperature and pressure are predicted to be ≃ 6500 K and ≃ 170 GPa. Results from the AIMD simulations are in line with the static compression data of TeO 2 paratellurite and cotunnite, and with the recent shock Hugoniot data for single-crystal α- TeO 2 for pressures up to 85 GPa, obtained using the inclined-mirror method and the velocity interferometer system for any reflector combined with powder gun and two-stage light-gas gun.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Field Degassing as a New Sampling Method for 14 C Analyses in Old Groundwater

Radiocarbon ( 14 C) activity in groundwater can be used to determine subsurface residence time up to ~40 kyr, providing crucial information on dynamic properties of groundwater and on paleoclimate. However, commonly applied sampling methods for dissolved inorganic carbon (DIC- 14 C) are prone to low level of modern atmospheric contamination, resulting in underestimation of groundwater ages that cluster around 30–40 kyr. We extract CO 2 gas from groundwater using a device originally developed for studies of noble gas radionuclides. Carbon is collected in the gas phase, eliminating the possibility of fostering microbial activities and aqueous chemical reactions during sample storage. This method collects CO 2 - 14 C and radiokrypton ( 81 Kr and 85 Kr) samples simultaneously. The presence of any shorter-lived 85 Kr is used to evaluate the degree of atmospheric contamination during sampling or mixing of young groundwater. Most groundwater samples showed lower CO 2 - 14 C activities than those of DIC- 14 C, presumably due to the absence of atmospheric contamination. Samples with 81 Kr age exceeding 150 kyr have no detectable CO 2 - 14 C except where mixing sources of young groundwater is suspected. Furthermore these field data serve as confirmations for the reliability of the newly presented sample collection and CO 2 - 14 C method, and for the outstanding roles of radiokrypton isotopes in characterizing old groundwater.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting 3D moisture sorption behavior of materials from 1D investigations

Abstract Moisture in materials can be a source of future outgassing and exacerbate unwanted changes in physical and chemical properties. Here, we investigate the effect of sample size and shape on the moisture transport phenomena through a combined experimental and modeling approach. Several different materials varying in size and shape were investigated over a wide range of relative humidities (0–90%) and temperatures ( $$30{-}70^{\,\circ } \hbox {C}$$ 30 - 70 ∘ C ) using gravimetric type dynamic vapor sorption (DVS). A dynamic triple-mode sorption model, developed previously, was employed to describe the experimental results with good success; the model includes absorption, adsorption, pooling (clustering) of species, and molecular diffusion. Here we show that the full triple-mode sorption model is robust enough to predict the dynamic uptake and outgassing of 3-dimensional (3D) samples using parameters derived from quasi-1D samples. This successful demonstration on three different materials (filled polydimethylsiloxane (PDMS), unfilled PDMS, and ceramic inorganic composite) illustrates that the model is robust at describing the scale-independent physics and chemistry of moisture sorption and diffusion materials. This work demonstrates that while sorption mechanisms manifest in testing of all sample sizes, some of these mechanisms were so subtle that they were overlooked in our initial modeling and assessment, illustrating the importance of multi-scale experiments in the development of robust predictive capabilities. Our study also outlines the challenges and viable solutions for global optimization of a multi-parameter model. The ability to quantify moisture sorption and diffusion, independent of scale, using 1D lab-scale experiments enables prediction of long-term bulk materials behavior in real applications.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological–thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon–climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological–thermal processes associated with annual freeze–thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets – including soil liquid water content, temperature and electrical resistivity tomography (ERT) data – to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological–thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface–subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice–liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological–thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological–thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface–subsurface, deterministic–stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological–thermal dynamics.

58 GEOSCIENCES↗