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DOE OSTI2024

Predictive Indicators of the Performance of Large Language Models

In several mission contexts, it is desirable to estimate the performance of large language models (LLMs) on tasks that we cannot run directly. In light of published “scaling laws” our hypothesis is that some tasks should be consistently more challenging than others based on characteristics of the task. The goal of this project was to begin quantifying how much information about LLM performance can be gained from the features of a model and a task. Two of our statistical models struggled to converge. Pass/fail test results may provide limited information for inference beyond model quality and task difficulty, but we see no evidence at this time for significant feature interaction effect sizes, arguing for simple models. Future work extending the models to capitalize on perplexity of ground truth answers is suggested. This project also introduces “Depth of Knowledge Variant Testing” as a strategy for more finely assessing language models on open domain question and answer tasks. We developed sets of questions that ask a language model to produce similar information while demonstrating increasing depth of knowledge, and also relabeled existing Q&A test questions with their depth of knowledge. Our results suggest further consideration of Bloom’s taxonomy and further refinement of prompts to properly elicit information at varying depths. In the course of this work, we set up a basic infrastructure for standardizing tasks and testing many language models on these tasks. In addition to testing the predictive quality of model features and performance across test suites, with this project we have introduced two new task features to contextualize each test question: the Dewey Classification main category of information covered, and the Bloom’s taxonomy level that corresponds to the depth of knowledge probed by the question. Splits across these and other features produced over five hundred task subtypes with distinct feature vectors, which we tested on half a dozen models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Active Optical Control with Machine Learning: A Proof of Concept for the Vera C. Rubin Observatory

Abstract The Active Optics System of the Vera C. Rubin Observatory (Rubin) uses information provided by four wave front sensors to determine deviations between the reconstructed wave front and the ideal wave front. The observed deviations are used to adjust the control parameters of the optical system to maintain image quality across the 3.°5 field of view. The baseline approach from the project is to obtain amplitudes of the Zernike polynomials describing the distorted wave front from out-of-focus images collected by the wave front sensors. These Zernike amplitudes are related via an “influence matrix” to the control parameters necessary to correct the wave front. In this paper, we use deep-learning methods to extract the control parameters directly from the images captured by the wave front sensors. Our neural net model uses anti-aliasing pooling to boost performance, and a domain-specific loss function to aid learning and generalization. The accuracy of the control parameters derived from our model exceeds Rubin requirements even in the presence of full-moon background levels and mis-centering of reference stars. Although the training process is time consuming, model evaluation requires only a few milliseconds. This low latency should allow for the correction of the optical configuration during the readout and slew interval between successive exposures.

Astronomy & Astrophysics↗

DOE OSTI2022

Physics‐Informed Machine Learning Method for Large‐Scale Data Assimilation Problems

Abstract We develop a physics‐informed machine learning approach for large‐scale data assimilation and parameter estimation and apply it for estimating transmissivity and hydraulic head in the two‐dimensional steady‐state subsurface flow model of the Hanford Site given synthetic measurements of said variables. In our approach, we extend the physics‐informed conditional Karhunen‐Loéve expansion (PICKLE) method to modeling subsurface flow with unknown flux (Neumann) and varying head (time‐dependent Dirichlet) boundary conditions. We demonstrate that the PICKLE method is comparable in accuracy with the standard maximum a posteriori (MAP) method, but is significantly faster than MAP for large‐scale problems. Both methods use a mesh to discretize the computational domain. In MAP, the parameters and states are discretized on the mesh; therefore, the size of the MAP parameter estimation problem directly depends on the mesh size. In PICKLE, the mesh is used to evaluate the residuals of the governing equation, while the parameters and states are approximated by the truncated conditional Karhunen‐Loéve expansions with the number of parameters controlled by the smoothness of the parameter and state fields, and not by the mesh size. For a considered example, we demonstrate that the computational cost of PICKLE increases near linearly (as N 1.15 ) with the number of grid nodes N , while that of MAP increases much faster (as N 3.28 ). We also show that once trained for one set of Dirichlet boundary conditions (i.e., one river stage), the PICKLE method provides accurate estimates of the hydraulic head for any value of the Dirichlet boundary conditions (i.e., for any river stage).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Boundary condition effects on runaway electron mitigation coil modeling for the SPARC and DIII-D tokamaks

Abstract Extended-MHD modeling of planned Runaway Electron Mitigation Coils (REMC) for SPARC and DIII-D is performed with the NIMROD code. A coil has been designed for each machine, with the two differing in shape and location, but both having n = 1 symmetry (with n the toroidal mode number). Compared to previous modeling efforts, three improvements are made to the simulations boundary conditions. First a resistive wall model is used in place of an ideal wall. Second, the ThinCurr code is used to compute the time-dependent 3D fields used as magnetic boundary conditions for the simulations. Third, the simulation boundary is moved from the first-wall location to the Vacuum Vessel (VV), which extends the boundary past the location of the internal REMC. To remove the 3D coil from the simulation domain, an equivalent set of 3D fields is calculated at the VV boundary that produce approximately the same field distribution at the last closed flux surface assuming vacuum between the two. Each of these three boundary condition improvements leads to an improvement in the predicted performance of the REMC for both machines. The resistive wall alone primarily effects the resonance of the coil with the plasma after the TQ, affecting the q-profile evolution in the SPARC modeling, and allowing the applied spectrum to be modified in response to the plasma in the DIII-D modeling. The movement of the simulation boundary has the most significant effect on the RE confinement overall, including in the early stages, particularly for a DIII-D inner wall limited equilibrium, where the RE loss fraction increases from 90% to > 99%, with SPARC RE losses also occurring much earlier when the boundary is placed at the VV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions

This paper investigates applying artificial intelligence (AI) algorithms to attitude control system of satellites to optimally tune the controller using high performance computing. This methodology is applied to the Virtual Telescope for X-ray Observation mission, which is a precise formation of two separate spacecraft observing multiple objects in the space in the X-ray domain. The mission is divided into phases based on the instrumentation and the mission goal. To reach an stable precise formation robust to stochastic slew and slew rate (i.e., Euler angles and angular velocities) in a minimal constrained time T , consumed energy of the attitude control system, denoted as E, and root-mean-square state error of attitude control system, denoted as e, are minimized. Monte-Carlo simulation is used for the sensitivity analysis of optimization and designing a controller. Deep neural networks (DNN), Gaussian processes (GP), and support vector regression (SVR) learn this optimization as a surrogate model, while their hyperparameters are optimized in a novel approach. THETA supercomputer at Argonne Leadership Computing Facility (ALCF) is used for optimizing the hyperparameters of DNN. The surrogate model meets the requirements of the mission, and it shows a better performance over the optimization and Monte-Carlo. The optimal DNN can satisfy the mission requirements e and T while reducing E for 90% compared to the other given methods.

42 ENGINEERING↗

DOE OSTI2020

Innovation in Radiological Security, Part 1 of 2 – Using a Cloud Solution to Compress the Timely Detection Model

The design and implementation of new security technologies must account for numerous and complex operational challenges. For example, traditionally isolated systems must now survive amid the proliferation of network connectivity and endure the dynamic environments created by organizations tolerating bring-your-own-device policies. When evaluating the cyber, physical, or cyber-physical security of an asset, a common practice in the security industry is to apply the Timely Detection Model (TDM)—a versatile concept that relates the security functions of detection, delay, and response to the progression of an oppositional force’s attack timeline. If implemented correctly, security enhancements can compress the TDM to offer efficiencies to the stakeholders responsible for adjudicating threat scenarios. The efficiencies gained by the response force allow for more effective protection strategies to be realized. One such enhancement, deployed within the radiological security domain—the Sentry-Remote Monitoring System (Sentry-RMS)—is a stand-alone security system that detects, assesses, and communicates priority alarms as a means of thwarting internal and external threats. The SEntry-RMS CommUnications and REsponse (Sentry-SECURE) platform—an optional feature of the Sentry-RMS—is being developed to facilitate more efficient alarm adjudication by site stakeholders and, if necessary, a faster response by law enforcement. This Cloud-hosted platform receives protected information from deployed Sentry-RMS units and relays it to stakeholders that have vested interests in maintaining an elevated level of situational awareness. Operationally, this enables real-time delivery of high-priority alarm and video imagery directly to an identified response stakeholder, such as local law enforcement or site management, via natively developed mobile applications or full platform integration. The Sentry-SECURE platform, as implemented by the U.S. Department of Energy’s Office of Radiological Security, is an example of an innovative security technology that compresses the TDM by (1) enabling a more efficient time to target and (2) better informing a response force’s predetermined tactics, techniques, and procedures. This paper explores the platform’s operational roles, highlights its principal functions, and presents a use case that demonstrates how the platform provides enhanced situational awareness when adjudicating priority alarms.

Sentry-RMS, Timely Detection Model, Sentry-SECURE↗

DOE OSTI2022

Interface Capturing Simulations on Pool Boiling Performance With Multiple Nucleation Sites

High-fidelity pool boiling simulations can provide a numerical database for improving mechanistic boiling models by allowing for specific evaluation of interactions among bubbles. Previously published pool boiling simulations investigated two nucleation sites in which bubble growth at one site suppressed nucleation at the other site. Based on previous study results, more complicated interface-capturing simulations on pool boiling were conducted using PHASTA code with locally refined unstructured mesh.First, different boundary conditions (BCs) were assessed to support robustness and reproducibility of the boiling model. Then, a scale study was conducted at a larger domain with nine nucleation sites, four of which were activated. Involving more nucleation sites increased the complexity of bubble interactions from surrounding sites. Finally, bubble departure behavior influenced by wall heat flux was investigated. When heat flux was increased, the order of bubble departure changed, but diagonal bubbles always departed after one another. The departure time interval between the first and second bubble reduced as heat flux increased. The corresponding frequency was almost linearly proportional to the heat flux. In addition, bubble departure behavior was found to be greatly influenced by the nucleation site pattern. Multiple nucleation sites resulted in superimposed inhibitive effects from surrounding sites to each bubble, which extensively delayed the departure.The work presented here provides new insight on the fundamental understanding of boiling phenomena, contributes to the development of a 3D multiphase computational fluid dynamics (M-CFD) model, and provides a more comprehensive database for data-driven pool boiling studies.

Fan, Joy↗

DOE OSTI2024

Unraveling the Molecular Origin of Prey-Wrapping Spider Silk's Unique Mechanical Properties and Assembly Process Using NMR

Prey wrapping spider silk's unique mechanical properties are investigated confirming the silk's high degree of extensibility and superior toughness compared to other types of spider silk. For the first time, the pre-spinning dope phase is studied in isotope-enriched intact aciniform (AC) silk glands using solution NMR that reveals a combination of α-helical domains linked by disordered random coil chains consistent with previously proposed “beads-on-a-string” models. The model is further refined through the AlphaFold2 protein structure prediction tool. Finally, extensive magic angle spinning (MAS) solid-state (SS) NMR data for isotopically-enriched fibers is used to refine the structural model for AC silk from two species, A. aurantia and A. argentata. The SSNMR data shows that the AC silk fibers are highly α-helical, coiled-coil in structure but, also exhibit significant β-sheet components that can be traced back to the Gly-rich disordered linker regions in the pre-spinning dope phase that are converted to β-sheet structures during fiber formation. This combination of mechanical and structural characterization enhances the understanding of AC silk's liquid-to-solid transition and structure-mechanics relationship. In conclusion, these prey wrap silk results and models will provide the basis for the design of biomimetic materials inspired by the AC spider silk system.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Scalable Data-Intensive Geocomputation: A Design for Real-Time Continental Flood Inundation Mapping

The convergence of data-intensive and extreme-scale computing enables an integrated software and data ecosystem for scientific discovery. Developments in this realm will fuel transformative research in data-driven interdisciplinary domains. Geocomputation provides computing paradigms in Geographic Information Systems (GIS) for interactive computing of geographic data, processes, models, and maps. Because GIS is data-driven, the computational scalability of a geocomputation workflow is directly related to the scale of the GIS data layers, their resolution and extent, as well as the velocity of the geo-located data streams to be processed. Unique in high user interactivity and low end-to-end latency requirements, geocomputation applications will dramatically benefit from the convergence of high-end data analytics (HDA) and high-performance computing (HPC). The application level challenge, however, is to identify and eliminate computational bottlenecks that arise along a geocomputation workflow. Indeed, poor scalability at any of the workflow components is detrimental to the entire end-to-end pipeline. Here, we study a large geocomputation use case in flood inundation mapping that handles multiple national-scale geospatial datasets and targets low end-to-end latency. We discuss benefits and challenges for harnessing both HDA and HPC for data-intensive geospatial data processing and intensive numerical modeling of geographic processes. We propose an HDA+HPC geocomputation architecture design that couples HDA (e.g., Spark)-based spatial data handling and HPC-based parallel data modeling. Key techniques for coupling HDA and HPC to bridge the two different software stacks are reviewed and discussed.

Liu, Yan↗

DOE OSTI2022

Comparative Small-Signal Stability Analysis of Grid-Forming and Grid-Following Inverters in Low-Inertia Power Systems: Preprint

Inverter-based resources (IBRs) are gradually replacing conventional fossil energy in modern power systems, which could make the system susceptible due to insufficient inertia, especially when moving towards a 100% inverter-based power grid. Grid-forming (GFM) and grid-following (GFL) controls are two common inverter control algorithms. However, the corresponding stability issues along with a comparative study on low-inertia power systems should still be investigated. This paper focuses on small-signal stability and conducts a comprehensive analysis of GFM and GFL controls, with emphases on the interactions between synchronous generators and IBRs. The detailed modeling of GFM and GFL inverters, and synchronous generators (SGs) are established. The coupling mechanism of IBRs and SGs is then conducted through participation factor analysis. The impacts of critical parameters on system stability are finally identified. The theoretical analyses are validated through time-domain simulation in MATLAB/Simulink.

grid-following↗

DOE OSTI2022

The Bouger's Law Shell Ionospheric Transfer Function

Ionospheric transfer function (ITF) algorithms determine the effects of the ionosphere on an electromagnetic (EM) radio-frequency (RF) signal as it propagates through. In this report, the Bouger’s law shell model is outlined. This ITF is very similar to the Snell’s Law Shell ITF; however, in this formulation, plasma parameters in the ionosphere are allowed to change radially. This algorithm is expressed in the frequency domain. In this way, it is applied as linear time invariant (LTI) filter function. Signals in this report are assumed to have only a single component (i.e. x, y or z in a rectangular coordinate system). Multi-component signals can be treated simply by applying the specific ITF to each component separately.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Effect of time and spatial domains on monolayer 2D material interface thermal conductance measurement using ns ET-Raman

Raman-based approaches to characterizing thermal properties of nanoscale and microscale materials have proven to be powerful experimental methods. Here, this work explores the potential of energy-transport state-resolved Raman (ET-Raman) for the precise determination of interfacial thermal conductance (G) between monolayer 2D materials and their substrates. ET-Raman employs a continuous wave laser and nanosecond (ns) pulsed laser to induce both steady state and transient heating while simultaneously collecting 2D material temperature information from the Raman signal. A 3D numerical model built using the finite volume method simulates the two differentiated thermal transport processes. The simulated temperature rises are compared with experimental Raman temperature information to extract G. In this study, we investigate the temporal effects of the pulse frequency and pulse width of the ns laser on precise characterization of G. By running multiple simulations for a range of pulsed laser frequencies and pulse widths, we determine that ET-Raman is most sensitive to precise interfacial thermal conductance measurements with short pulse widths (~below 20 ns) and with pulsing frequencies that ensure sufficient cooling time between consecutive pulses (~below 500 kHz). We also conclude that substrate thickness and laser spot size play critical roles in heat distribution and subsequent sensitivity to meaningful G measurements. As both laser spot size and SiO 2 substrate thickness decrease, detection of interfacial thermal transport becomes more difficult. These results offer essential guidance in the design of future Raman-based approaches to thermal measurements using pulsed lasers.

2D materials↗

DOE OSTI2023

The NANOGrav 15 yr Data Set: Search for Signals from New Physics

The 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) shows positive evidence for the presence of a low-frequency gravitational-wave (GW) background. In this paper, we investigate potential cosmological interpretations of this signal, specifically cosmic inflation, scalar-induced GWs, first-order phase transitions, cosmic strings, and domain walls. We find that, with the exception of stable cosmic strings of field theory origin, all these models can reproduce the observed signal. When compared to the standard interpretation in terms of inspiraling supermassive black hole binaries (SMBHBs), many cosmological models seem to provide a better fit resulting in Bayes factors in the range from 10 to 100. However, these results strongly depend on modeling assumptions about the cosmic SMBHB population and, at this stage, should not be regarded as evidence for new physics. Furthermore, we identify excluded parameter regions where the predicted GW signal from cosmological sources significantly exceeds the NANOGrav signal. These parameter constraints are independent of the origin of the NANOGrav signal and illustrate how pulsar timing data provide a new way to constrain the parameter space of these models. Finally, we search for deterministic signals produced by models of ultralight dark matter (ULDM) and dark matter substructures in the Milky Way. We find no evidence for either of these signals and thus report updated constraints on these models. In the case of ULDM, these constraints outperform torsion balance and atomic clock constraints for ULDM coupled to electrons, muons, or gluons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Label-free characterization of organic nanocarriers reveals persistent single molecule cores for hydrocarbon sequestration

Self-assembled molecular nanostructures embody an enormous potential for new technologies, therapeutics, and understanding of molecular biofunctions. Their structure and function are dependent on local environments, necessitating in-situ/operando investigations for the biggest leaps in discovery and design. However, the most advanced of such investigations involve laborious labeling methods that can disrupt behavior or are not fast enough to capture stimuli-responsive phenomena. We utilize X-rays resonant with molecular bonds to demonstrate an in-situ nanoprobe that eliminates the need for labels and enables data collection times within seconds. Our analytical spectral model quantifies the structure, molecular composition, and dynamics of a copolymer micelle drug delivery platform using resonant soft X-rays. We additionally apply this technique to a hydrocarbon sequestrating polysoap micelle and discover that the critical organic-capturing domain does not coalesce upon aggregation but retains distinct single-molecule cores. This characteristic promotes its efficiency of hydrocarbon sequestration for applications like oil spill remediation and drug delivery. Such a technique enables operando, chemically sensitive investigations of any aqueous molecular nanostructure, label-free.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Efficient 3-D velocity model building using joint inline and crossline plane-wave wave-equation migration velocity analyses

SUMMARY Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and backprojects residual CIG moveouts into wavefield wave paths with a reflection tomographic kernel. However, 3-D PWEMVA is very computationally expensive because 3-D reflection tomographic inversion requires at least five 3-D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3-D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3-D velocity model update through 1-D spline interpolation of 2-D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wave paths, and the method can take less than 1 per cent of the computational cost of full 3-D PWEMVA. To construct unaliased plane waves for our joint inline and crossline PWEMVA, we develop a 3-D data interpolation method in the frequency–wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3-D synthetic seismic data set and a land 3-D field seismic data set acquired at the Farnsworth CO2-EOR (enhanced oil recovery) field to demonstrate the efficacy of our velocity model building and data interpolation methods.

Liu, Xuejian↗

DOE OSTI2020

PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data

Spectral induced polarization (SIP) is a non-intrusive geophysical method that collects chargeability information (the ability of a material to retain charge) in the time domain or its phase shift in the frequency domain. Although SIP is a temporal method, it cannot measure the dynamics of flow and solute/species transport in the subsurface over long times (i.e., 10–100 s of years). Data collected with the SIP technique need to be coupled with fluid flow and reactive-transport models in order to capture long-term dynamics. To address this challenge, PFLOTRAN-SIP was built to couple SIP data to fluid flow and solute transport processes. Specifically, this framework couples the subsurface flow and transport simulator PFLOTRAN and geoelectrical simulator E4D without sacrificing computational performance. PFLOTRAN solves the coupled flow and solute-transport process models in order to estimate solute concentrations, which were used in Archie’s model to compute bulk electrical conductivities at near-zero frequency. These bulk electrical conductivities were modified while using the Cole–Cole model to account for frequency dependence. Using the estimated frequency-dependent bulk conductivities, E4D simulated the real and complex electrical potential signals for selected frequencies for SIP. These frequency-dependent bulk conductivities contain information that is relevant to geochemical changes in the system. This study demonstrated that the PFLOTRAN-SIP framework is able to detect the presence of a tracer in the subsurface. SIP offers a significant benefit over ERT in the form of greater information content. It provided multiple datasets at different frequencies that better constrained the tracer distribution in the subsurface. Consequently, this framework allows for practitioners of environmental hydrogeophysics and biogeophysics to monitor the subsurface with improved resolution.

54 ENVIRONMENTAL SCIENCES↗