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DOE OSTI2024

WE-Validate: An Open-Source Framework For Wind Power Validation

Grid operators rely on historical weather time series at existing and planned wind power plants to make informed decisions when planning for a future power grid with very high penetration of renewable power. While synthetic wind power time series have been developed based on historical weather models, their validation with actual power production data remains complex due to variations in modeling practices and methodologies. This paper introduces the WE-Validate framework, originally designed for wind speed validation and now enhanced for wind power validation with a graphical user interface to support users with minimal programming experience. Validation of wind power with WE-Validate is based on robust metrics consisting of RMSE, centered RMSE, average bias, average percent bias, mean absolute error, mean absolute percent error, cross correlation, and calculation of ramping magnitude, rate, and duration. This paper showcases WE-Validate with validation of synthetically derived power for a wind plant in Washington state for one month in 2018. Validation of the synthetic power from two comparison data sets compared with observations shows both comparison series have strong correlation with observed across weekly and monthly aggregations while suffering from persistent negative bias. The suite of metrics within WE-Validate facilitates immediate insight into the utility of the comparison data sets through compression across multiple axes. This user-friendly, open-source tool can be extended beyond wind power, making it a valuable resource for system planners and operators in different domains.

Moncheur de Rieudotte, Malcolm P.↗

NASA NTRS1987

Data volume reduction for imaging radar polarimetry

Two data reduction algorithms developed using the scattering and phase matrix approaches are described. In the scattering matrix approach, the scattering matrices of four consecutive along-track pixels are averaged and in the phase matrix approach, the phase matrices of four consecutive along-track pixels are averaged. The basic procedures necessary to generate a synthetic polarization image from original data sets are discussed. The two algorithms are evaluated in terms of data volume reduction and the number of errors introduced in the synthesized images. It is observed that the reduced data set produced by the scattering matrix algorithm is smaller than that generated by the phase matrix algorithm; however, greater errors are introduced into the data set by the scattering matrix algorithm than the phase algorithm. Flowcharts for the scattering and phase matrix approaches and for synthesis of uncompressible data are presented.

Dubois, Pascale C.↗

NASA NTRS1994

The two-way time synchronization system via a satellite voice channel

A newly developed two-way time synchronization system is described in this paper. The system uses one voice channel at a SCPC satellite digital communication earth station, whose bandwidth is only 45 kHz, thus saving satellite resources greatly. The system is composed of one master station and one or several, up to sixty-two, secondary stations. The master and secondary stations are equipped with the same equipment, including a set of timing equipment, a synthetic data terminal for time synchronizing, and a interface unit between the data terminal and the satellite earth station. The synthetic data terminal for time synchronization also has an IRIG-B code generator and a translator. The data terminal of master station is the key part of whole system. The system synchronization process is full automatic, which is controlled by the master station. Employing an autoscanning technique and conversational mode, the system accomplishes the following tasks: linking up liaison with each secondary station in turn, establishing a coarse time synchronization, calibrating date (years, months, days) and time of day (hours, minutes, seconds), precisely measuring the time difference between local station and the opposite station, exchanging measurement data, statistically processing the data, rejecting error terms, printing the data, calculating the clock difference and correcting the phase, thus realizing real-time synchronization from one point to multiple points. We also designed an adaptive phase circuit to eliminate the phase ambiguity of the PSK demodulator. The experiments have shown that the time synchronization accuracy is better than 2 mu S. The system has been put into regular operation.

Heng-Qiu, Zheng↗

NASA NTRS2020

Terrain Relative Navigation for Guided Descent on Titan

Titan’s dense atmosphere, low gravity, and high winds at high altitudes create descent times of >90 minutes with standard entry/descent/landing (EDL) architectures and result in large unguided landing ellipses, with 99% values of 110x110 km and 149x72 km in recent Titan lander proposals. Enabling precision landing on Titan could increase science return for the types of missions proposed to date and make additional types of landing sites accessible, opening up new possibilities for science investigations. Precision landing on Titan has unique challenges, because the hazy atmosphere makes it difficult to see the surface and because it requires guided descent with divert ranges that are one to two orders of magnitude larger than needed for other target bodies, i.e. up to on the order of 100 km. It is conceivable that such a divert capability could be provided economically by a parafoil or other steerable aerodynamic decelerator deployed several 10s of km above the surface. The long descent times lead to large inertial navigation errors, hence a need for terrain relative navigation (TRN). This would require a TRN capability that can operate at such altitudes, despite challenges of seeing the surface sufficiently clearly and of depending on map products that are two orders of magnitude lower in spatial resolution than those for Mars and airless bodies. We then develop algorithms for map matching and feature tracking with descent images and test these with synthetic images created from Cassini/Huygens data sets and our radiative transfer model. We also introduce new possibilities for TRN based on the potential to discriminate some specific types of terrain onboard in descent imagery, such as lake vs adjacent ground and dune vs interdune. We use sensor measurement noise models in simulations of state estimation with an extended Kalman filter that includes coordinates of a set of tracked features in the state vector. Case studies were done for two notional landing sites, one in a site with only dry ground and one in a Titan lake district. In both cases, the filter error model shows 3 position error at touchdown on the order of 2 km. More work is needed to validate these results with higher fidelity camera models and larger data sets, but this is very promising.

Matthies, Larry↗

DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

In real-world applications like spectrum management and interference detection, dealing with unseen electromagnetic waveforms is critical. Although some methods attempt to simulate open set data using generator models, they face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. This results in difficulties capturing distinctive features across classes, especially in dynamic scenarios where new classes emerge. To detect unseen waveforms, we propose combining time and frequency domain features with cosine similarity loss to enhance feature distinctiveness and enabling more accurate predictions. This approach efficiently captures more comprehensive information than single-domain representations or approaches without cosine loss. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10\% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗

NASA NTRS1990

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method.

Kim, Hakil↗

DOE OSTI2021

Adjoint-Based Inversion of Geodetic Data for Sources of Deformation and Strain

An adjoint-based formulation leads to a particularly efficient approach for inverting geodetic measurements for the source of the deformation. Specifically, the quantities necessary to iteratively improve the fit to the observations can be computed with just three forward calculations, one to obtain the current residuals, another to solve the adjoint problem, and a third to compute the step length. An inversion algorithm utilizing the adjoint-based gradient is applied to a set of Interferometric Synthetic Aperture Radar (InSAR) data gathered between 2016 and 2018 over the Tulare Basin in California's Central Valley. Because the measured deformation is due to groundwater withdrawal, a penalty function is included in the inversion to avoid placing aquifer volume change in locations that are far from any documented wells. The solution of the inverse problem provides estimates of aquifer compaction that provide a match to the observed range changes while honoring the well data. The solution indicates an average aquifer volume loss of 2.17 km 3 /year over the two year period from January 2016 to January 2018, encompassing one drought year (2016) and one wet year (2017). Finally, this magnitude of lost volume is compatible with the 3.1 km 3 /year decrease in water volume for the entire Central Valley, estimated from GRACE satellite gravity data.

58 GEOSCIENCES↗

NASA NTRS2019

Discovery of Activities via Statistical Clustering of Fixation Patterns

Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a pilot flying an airplane; in this case, activities with distinct visual signatures might be things like communicating, navigating, monitoring, etc. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. We compare, not individual fixations, but groups of fixations aggregated over a fixed time interval (Tau). We assume that the environment has been divided into a finite set of discrete areas-of-interest (AOIs). For a given time interval, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector, where N is the number of AOIs. These proportions can be converted to integer counts by multiplying by Tau divided by the average fixation duration, a parameter that we fix at 283 milliseconds. We compare different intervals by computing the chi-squared statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We cluster the intervals, first by merging adjacent intervals that are sufficiently similar, optionally shifting the boundary between non-merged intervals to maximize the difference. Then we compare and cluster non-adjacent intervals. The method is evaluated using synthetic data generated by a hand-crafted set of activities. While the method generally finds more activities than put into the simulation, we have obtained agreement as high as 80 percent between the inferred activity labels and ground truth.

Eye Movements↗

NASA NTRS1979

Improvement of selected satellite applications through the use of microwave data

This paper describes an analysis of a data set in which satellite acquired microwave data (Seasat Synthetic Aperture Radar) have been registered with Landsat Multispectral Scanner data and the combined data processed using conventional multichannel spectral pattern recognition programs. Results of this analysis indicate that the combined data set offers improvement in surface classification that is significant to certain applications. A brief description of the registration procedure is given. The improvement in results rendered to selected resource management applications is discussed. The presented results are preliminary due to the short time that the satellite microwave data have been available; however, a more comprehensive analysis is in progress and will be completed during the coming year.

Mooneyhan, D. W.↗

DOE OSTI2023

Machine learning-based inversion for acoustic impedance with large synthetic training data: Workflow and data characterization

Where wells are sparse or training data are difficult to label with high-quality wireline-derived impedance logs, machine learning (ML)-based inversion of acoustic impedance typically depends on small training data sets, leading to biased prediction. We have advanced a novel workflow that applies large synthetic seismic training data to reduce facies-related bias. Using a geologically realistic model as the truth model, we randomly select sparse seed wells to perform sequential Gaussian simulation (SGS) for impedance models of the same geometry and simulate facies variability. We implement random forest regression on 30 features extracted from the synthetic volume. We observe that more seed wells tend to reduce facies-induced bias by sampling more types of facies, resulting in a better prediction. We then focus on the responses of SGS models to facies changes, the number of seed wells necessary for a useful synthetic model, and how much a synthetic model can help ML-based inversion. Here, we observe that the SGS synthetic training model outperforms well-direct training in general. For modeled clastic shore-zone systems in Miocene Gulf of Mexico, two or more seed wells are necessary for a significant reduction of root-mean-square error and outliners, and improvement of facies imaging. In a field-data test, we apply a similar workflow to quantitatively predict acoustic impedance, which is then converted to a sand-volume map at a high-frequency sequence (10–100 m), revealing detailed facies and sandstone patterns. Such results are valuable in many geologic and engineering applications, such as hydrocarbon and CO 2 reservoir prospecting, reserve estimation, simulation, etc.

3D seismic↗

DOE OSTI2023

Explaining machine-learning models for gamma-ray detection and identification

As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

DESI DR1 Ly α 1D power spectrum: Validation of estimators

The Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI) is the largest sample to date for small-scale Lyα forest cosmology, accessed through its one-dimensional power spectrum (P 1D ). The Lyα forest P 1D is extracted from quasar spectra that are highly inhomogeneous (both in wavelength and between quasars) in noise properties due to intrinsic properties of the quasar, atmospheric and astrophysical contamination, and also sensitive to low-level details of the spectral extraction pipeline. We employ two estimators in DR1 analysis to measure P 1D : the optimal estimator and the fast Fourier transform (FFT) estimator. To ensure robustness of our DR1 measurements, we validate these two power spectrum and covariance matrix estimation methodologies against the challenging aspects of the data. First, using a set of 20 synthetic 1D realizations of DR1, we derive the masking bias corrections needed for the FFT estimator and the continuum fitting bias needed for both estimators. We demonstrate that both estimators, including their covariances, are unbiased with these corrections using the Kolmogorov-Smirnov test. Second, we substantially extend our previous suite of CCD image simulations to include 675,000 quasars, allowing us to accurately quantify the pipeline's performance. This set of simulations reveals biases at the highest k values, corresponding to a resolution error of a few percent. We base the resolution systematics error budget of DR1 P 1D on these values, but do not derive corrections from them since the simulation fidelity is insufficient for precise corrections.

Lyman alpha forest↗

DOE OSTI2025

Anomaly Detection in Seismic Data with Deep Learning: Application for Instrument Failure Detection and Forecasting

Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.

Lin, Jiun-Ting [Lawrence Livermore National Labora↗

DOE OSTI2020

Automatic Échelle Spectrograph Wavelength Calibration

Time-domain astronomy and the increasing number of exoplanet candidates call for reliable, robust, and automatic wavelength calibration. We present an algorithm for wavelength-calibrating échelle spectrographs that uses order-by-order extracted spectra and a list of laboratory wavelengths. Our approach is fully automatic and does not need the pixel locations of certain spectral features with which to anchor the wavelength solution, nor the true order number of each diffraction order. We use spectral features that are duplicated in adjacent orders to establish the scale-invariant component of the wavelength solution. We then match the central wavelengths of spectral features to laboratory wavelengths to establish the scale and higher-order components of the wavelength solution. We demonstrate our method on the four spectrographs of Las Cumbres Observatory’s Network of Robotic Échelle Spectrographs (NRES), on the High Accuracy Radial Velocity Planet Searcher (HARPS) spectrograph, and on synthetic data. We obtain a velocity-equivalent precision of ∼10 m s{sup −1} on NRES. We achieve ∼1 m s{sup −1} on HARPS, which agrees with the precision reported by the HARPS team. On synthetic data, we achieve the velocity precision set by Gaussian centroiding errors. Our algorithm likely holds for a wide range of spectrographs beyond the five presented here. We provide an open-source Python package, xwavecal, which outputs wavelength-calibrated spectra as well as the wavelengths of spectral features.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Validation of seismic velocity models in southern California with full-waveform simulations

SUMMARY Crustal seismic velocity models provide essential information for many applications including earthquake source properties, simulations of ground motion and related derivative products. We present a systematic workflow for assessing the accuracy of velocity models with full-waveform simulations. The framework is applied to four regional seismic velocity models for southern California: CVM-H15.11, CVM-S4.26, CVM-S4.26.M01 that includes a shallow geotechnical layer, and the model of Berg et al. For each model, we perform 3-D viscoelastic wave propagation simulations for 48 virtual seismic noise sources (down to 2 s) and 44 moderate-magnitude earthquakes (down to 2 s generally and 0.5 s for some cases) assuming a minimum shear wave velocity of 200 m s–1. The synthetic waveforms are compared with observations associated with both earthquake records and noise cross-correlation data sets. We measure, at multiple period bands for well-isolated seismic phases, traveltime delays and normalized zero-lag cross-correlation coefficients between the synthetic and observed data. The obtained measurements are summarized using the mean absolute derivation of time delay and the mean correlation coefficient. These two metrics provide reliable statistical representations of model quality with consistent results in all data sets. In addition to assessing the overall (average) performance of different models in the entire study area, we examine spatial variations of the models’ quality. All examined models show good phase and waveform agreements for surface waves at periods longer than 5 s, and discrepancies at shorter periods reflecting small-scale heterogeneities and near-surface structures. The model performing best overall is CVM-S4.26.M01. The largest misfits for both body and surface waves are in basin structures and around large fault zones. Inaccuracies generated in these areas may affect tomography and model simulation results at other regions. The seismic velocity models for southern California can be improved by adding better resolved structural representations of the shallow crust and volumes around the main faults.

58 GEOSCIENCES↗

DOE OSTI2023

Demographic Information Incorporated Household Energy Consumption Analysis

The high energy consumption from residential buildings provides them large potential to participate in demand response programs. To design appropriate demand response programs for residential buildings, it is important for electric utilities to know the energy consumption characteristics for different types of households so that utilities can send requests to the groups with a higher possibility to successfully respond. In this paper, we develop a load model to generate synthetic load profiles for different types of households incorporating demographical information including Current Population Survey data set and American Time Use Survey data set. The details of each data set and the details of the load models are presented. The synthetic household load profiles are generated by the load model and clustered into different groups based on state, age, number of occupants, income level, and city of the household. The average energy consumption characteristics for different groups of households are analyzed and compared, which will help electric utilities issue demand response signals to appropriate households.

building loads↗

DOE OSTI2023

Demographic Information Incorporated Household Energy Consumption Analysis: Preprint

building loads↗

DOE OSTI2023

Demographic Information Incorporated Household Energy Consumption Analysis

building loads↗