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DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗

NASA NTRS1975

Calculations for interpretation of solar vector magnetograph data

Computer techniques for data analysis of sunspot observations are presented. Photographic spectra were converted to digital form and analyzed. Methods of determining magnetic field strengths, i.e., the Zeeman effect, are discussed. Errors originating with telescope equipment and the magnetograph are treated. Flow charts of test programs and procedures of the data analysis are shown.

Dunn, A. R.↗

DOE OSTI2023

An adjoint-based optimization method for jointly inverting heterogeneous material properties and fault slip from earthquake surface deformation data

SUMMARY Analysis of tectonic and earthquake-cycle associated deformation of the crust can provide valuable insights into the underlying deformation processes including fault slip. How those processes are expressed at the surface depends on the lateral and depth variations of rock properties. The effect of such variations is often tested by forward models based on a priori geological or geophysical information. Here, we first develop a novel technique based on an open-source finite-element computational framework to invert geodetic constraints directly for heterogeneous media properties. We focus on the elastic, coseismic problem and seek to constrain variations in shear modulus and Poisson’s ratio, proxies for the effects of lithology and/or temperature and porous flow, respectively. The corresponding nonlinear inversion is implemented using adjoint-based optimization that efficiently reduces the cost function that includes the misfit between the calculated and observed displacements and a penalty term. We then extend our theoretical and numerical framework to simultaneously infer both heterogeneous Earth’s structure and fault slip from surface deformation. Based on a range of 2-D synthetic cases, we find that both model parameters can be satisfactorily estimated for the megathrust setting-inspired test problems considered. Within limits, this is the case even in the presence of noise and if the fault geometry is not perfectly known. Our method lays the foundation for a future reassessment of the information contained in increasingly data-rich settings, for example, geodetic GNSS constraints for large earthquakes such as the 2011 Tohoku-oki M9 event, or distributed deformation along plate boundaries as constrained from InSAR.

Geochemistry & Geophysics↗

NASA NTRS1992

Nonlinear aerodynamics and the design of wing tips

This report describes results of research conducted from April 1991 through March 1992. The general objective was to improve an existing wing optimization method, and apply the method to specific problems of interest. The method, while a valuable tool for wing tip design studies, can be applied to more general problems, and has been applied to some of these other problems during its development. Specific goals that were accomplished are listed and explained in more detail in the report. First, improvements were made to the portability and control flow of the existing code. The major iteration loop dealing with structural design was sped up and an alternate approach, using the optimizer to do structural sizing, was studied. Second, analysis methods were improved in the areas of structural and high lift modeling. The structural method was revised to give total wing weight and verified against data for particular commercial aircraft. The high lift analysis was improved to provide reasonable estimates of C(sub L max) in the flaps down condition. These improvements enabled making wing area a design variable, where it had been a fixed variable in the original method. Third, the method was applied to the design of wings for a Learjet. Rough studies were done to determine the effects of laminar flow design on wing shape. Finally, studies on wingtip shape were begun.

Kroo, Ilan↗

DOE OSTI2018

On the scattering correction of fast-ion D-alpha signals on NSTX-U

Analysis of fast-ion D-alpha (FIDA) data on National Spherical Torus Experiment-Upgrade (NSTX-U) shows that the cold Dα line contaminates the FIDA baseline. The scattered light is comparable to the FIDA emission. A scattering correction is required to extract the FIDA signal. Two methods that relate the scattered light contamination to the intensity of the cold Dα line are employed. Here, one method uses laboratory measurements with a calibration lamp; the other method uses data acquired during plasma operation and singular value decomposition analysis. Finally, after correction, both the FIDA spectra and spatial profile are in better agreement with theoretical predictions.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

On the scattering correction of fast-ion D-alpha signal on NSTX-U

Analysis of fast-ion D-alpha (FIDA) data on National Spherical Torus Experiment-Upgrade (NSTX-U) shows that the cold Dα line contaminates the FIDA baseline. The scattered light is comparable to the FIDA emission. A scattering correction is required to extract the FIDA signal. Two methods that relate the scattered light contamination to the intensity of the cold Dα line are employed. One method uses laboratory measurements with a calibration lamp; the other method uses data acquired during plasma operation and singular value decomposition analysis. After correction, both the FIDA spectra and spatial profile are in better agreement with theoretical predictions.

Single value decomposition (SVD)↗

DOE OSTI2024

Blueprints for Training Information Bottlenecks for Collider Analyses

Dimensionality reduction is a crucial aspect of data analysis in high energy physics, even if accompanied by information loss. Several methods, including histogram- and kernel-based analyses, are only computationally feasible for low-dimensional data. Furthermore, simulation models used in HEP can often only be validated for low-dimensional data. We provide several blueprints for using machine learning to create low-dimensional data representations (continuous event variables and discrete classification labels) for use in signal discovery and parameter estimation tasks. We also describe how to design the learned representation to facilitate a) searches with unknown model parameters and b) validation of simulation models in data control regions.

43 PARTICLE ACCELERATORS↗

NASA NTRS1993

Wavelet Compression of Seismic Data

While wavelet transforms have been used in the analysis of seismic signals, the methods used for the compression of seismic signals have been limited to Linear Predictive Coding and Transform Coding.

wavelet compression seismic data linear predictive↗

DOE OSTI2022

Decision Science for Machine Learning (DeSciML)

The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

The DECADE cosmic shear project III: validation of analysis pipeline using spatially inhomogeneous data

We present the pipeline for the cosmic shear analysis of the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog consisting of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The catalog derives from a large number of disparate observing programs and is therefore more inhomogeneous across the sky compared to existing lensing surveys. First, we use simulated data-vectors to show the sensitivity of our constraints to different analysis choices in our inference pipeline, including sensitivity to residual systematics. Next we use simulations to validate our covariance modeling for inhomogeneous datasets. Finally, we show that our choices in the end-to-end cosmic shear pipeline are robust against inhomogeneities in the survey, by extracting relative shifts in the cosmology constraints across different subsets of the footprint/catalog and showing they are all consistent within 1σ to 2σ. This is done for forty-six subsets of the data and is carried out in a fully consistent manner: for each subset of the data, we re-derive the photometric redshift estimates, shear calibrations, survey transfer functions, the data vector, measurement covariance, and finally, the cosmological constraints. Our results show that existing analysis methods for weak lensing cosmology can be fairly resilient towards inhomogeneous datasets. This also motivates exploring a wider range of image data for pursuing such cosmological constraints.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

An introduction to the significance of sample size in particle analyses for nuclear forensics and radiological investigations

Particulate isotopic analysis in nuclear forensics has developed rapidly during the past two decades due to technical advances in determining the isotopic composition of individual particles. This paper introduces basic statistical concepts that can be applied by analysts to understand the importance of statistical adequacy when interpretating particle data. While these basic statistical methods provide a useful point-of-entry to particle data analysis, more sophisticated statistical and modeling approaches are needed to extract maximal information from such datasets in the future.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

NASA NTRS1973

Methods for structural design at elevated temperatures

A procedure which can be used to design elevated temperature structures is discussed. The desired goal is to have the same confidence in the structural integrity at elevated temperature as the factor of safety gives on mechanical loads at room temperature. Methods of design and analysis for creep, creep rupture, and creep buckling are presented. Example problems are included to illustrate the analytical methods. Creep data for some common structural materials are presented. Appendix B is description, user's manual, and listing for the creep analysis program. The program predicts time to a given creep or to creep rupture for a material subjected to a specified stress-temperature-time spectrum. Fatigue at elevated temperature is discussed. Methods of analysis for high stress-low cycle fatigue, fatigue below the creep range, and fatigue in the creep range are included. The interaction of thermal fatigue and mechanical loads is considered, and a detailed approach to fatigue analysis is given for structures operating below the creep range.

Ellison, A. M.↗

NASA NTRS1990

Loop current eddy shedding estimated using Geosat altimeter data

A method for extracting periodic signals from altimeter data is applied in the Gulf of Mexico using data from the first two years of the Geosat Exact Repeat Mission. The least squares fitting of selected frequencies of sine and cosine functions to the time series of sea surface height at each point of the Geosat ground track is used to estimate the dominant frequency in the gulf for this time period. Optimal interpolation of the sine and cosine coefficients at the dominant frequency to a uniformly spaced grid allows reconstruction of the time series in the gulf and shows eddies shedding from the Loop Current with a period of approximately 10.5 months, consistent with shedding periods determined from numerical models and analysis of satellite infrared data. This technique is a novel method for investigation of periodic signals in Geosat altimetry and is not severely affected by the data outages present in the time series.

Jacobs, Gregg A.↗

NASA NTRS1982

Computer tomography of flows external to test models

Computer tomographic techniques for reconstruction of three-dimensional aerodynamic density fields, from interferograms recorded from several different viewing directions were studied. Emphasis is on the case in which an opaque object such as a test model in a wind tunnel obscures significant regions of the interferograms (projection data). A method called the Iterative Convolution Method (ICM), existing methods in which the field is represented by a series expansions, and analysis of real experimental data in the form of aerodynamic interferograms are discussed.

Prikryl, I.↗

NASA NTRS2010

Methodology for the Assessment of 3D Conduction Effects in an Aerothermal Wind Tunnel Test

This slide presentation reviews a method for the assessment of three-dimensional conduction effects during test in a Aerothermal Wind Tunnel. The test objectives were to duplicate and extend tests that were performed during the 1960's on thermal conduction on proturberance on a flat plate. Slides review the 1D versus 3D conduction data reduction error, the analysis process, CFD-based analysis, loose coupling method that simulates a wind tunnel test run, verification of the CFD solution, Grid convergence, Mach number trend, size trends, and a Sumary of the CFD conduction analysis. Other slides show comparisons to pretest CFD at Mach 1.5 and 2.16 and the geometries of the models and grids.

Oliver, Anthony Brandon↗

DOE OSTI2017

Preliminary Analysis of FFTF Loss-of-Flow Tests with SAS4A/SASSYS-1

The Fast Flux Test Facility (FFTF) was the most recent Sodium-cooled Fast Reactor (SFR) built in the United States. The Passive Safety Test (PST) program was carried out on FFTF between 1984 and 1986 to demonstrate the capability of passive reactor shutdown and decay heat removal in response to protected and unprotected transients. In order to recover the information and knowledge that has been gained from FFTF, a research project was initiated focusing on the simulation of Unprotected Loss of Flow Without Scram (LOFWOS) tests similar to the knowledge preservation activities conducted for EBR-II by Argonne National Laboratory in earlier years. These knowledge preservation activities are intended to support the validation of computational tools and methods in the area of SFR passive safety. Comparisons with experimental data and other safety codes provide opportunities to improve SFR design and safety analysis codes and methods.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2005

Quantitative Analysis of Venus Radar Backscatter Data in ArcGIS

Ongoing mapping of the Ganiki Planitia (V14) quadrangle of Venus and definition of material units has involved an integrated but qualitative analysis of Magellan radar backscatter images and topography using standard geomorphological mapping techniques. However, such analyses do not take full advantage of the quantitative information contained within the images. Analysis of the backscatter coefficient allows a much more rigorous statistical comparison between mapped units, permitting first order selfsimilarity tests of geographically separated materials assigned identical geomorphological labels. Such analyses cannot be performed directly on pixel (DN) values from Magellan backscatter images, because the pixels are scaled to the Muhleman law for radar echoes on Venus and are not corrected for latitudinal variations in incidence angle. Therefore, DN values must be converted based on pixel latitude back to their backscatter coefficient values before accurate statistical analysis can occur. Here we present a method for performing the conversions and analysis of Magellan backscatter data using commonly available ArcGIS software and illustrate the advantages of the process for geological mapping.

Long, S. M.↗

DOE OSTI2016

WE-AB-207A-04: Random Undersampled Cone Beam CT: Theoretical Analysis and a Novel Reconstruction Method

Purpose: Reducing x-ray exposure and speeding up data acquisition motived studies on projection data undersampling. It is an important question that for a given undersampling ratio, what the optimal undersampling approach is. In this study, we propose a new undersampling scheme: random-ray undersampling. We will mathematically analyze its projection matrix properties and demonstrate its advantages. We will also propose a new reconstruction method that simultaneously performs CT image reconstruction and projection domain data restoration. Methods: By representing projection operator under the basis of singular vectors of full projection operator, matrix representations for an undersampling case can be generated and numerical singular value decomposition can be performed. We compared properties of matrices among three undersampling approaches: regular-view undersampling, regular-ray undersampling, and the proposed random-ray undersampling. To accomplish CT reconstruction for random undersampling, we developed a novel method that iteratively performs CT reconstruction and missing projection data restoration via regularization approaches. Results: For a given undersampling ratio, random-ray undersampling preserved mathematical properties of full projection operator better than the other two approaches. This translates to advantages of reconstructing CT images at lower errors. Different types of image artifacts were observed depending on undersampling strategies, which were ascribed to the unique singular vectors of the sampling operators in the image domain. We tested the proposed reconstruction algorithm on a Forbid phantom with only 30% of the projection data randomly acquired. Reconstructed image error was reduced from 9.4% in a TV method to 7.6% in the proposed method. Conclusion: The proposed random-ray undersampling is mathematically advantageous over other typical undersampling approaches. It may permit better image reconstruction at the same undersampling ratio. The novel algorithm suitable for this random-ray undersampling was able to reconstruct high-quality images.

60 APPLIED LIFE SCIENCES↗