Search NASA⌕ Search

SEARCH · Search NASA

Results for “Methods: data analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

DOE/DOT Crude Oil Characterization Research Study, Task 2 Test Report on Evaluating Crude Oil Sampling and Analysis Methods, Revision 1 - Winter Sampling

The Crude Oil Characterization Research Study is designed to evaluate whether crude oils currently transported in North America, including those produced from “tight” formations, exhibit physical or chemical properties that are distinct from conventional crudes, and how these properties associate with combustion hazards that may be realized during transportation and handling. The current report presents results from Task 2, investigating which commercially available methods can accurately and reproducibly collect and analyze crude oils for vapor pressure and composition, including dissolved gases. This issue, Revision 1 – Winter Sampling, incorporates additional seasonal data and compositional analysis results that have become available since publication of a prior report, SAND2017-12482, released in December 2017. Both reports compare performance of commercially available methods to that of a well-established mobile laboratory system that currently serves as the baseline instrument system for the U.S. Strategic Petroleum Reserve Crude Oil Vapor Pressure Program. The experimental matrix evaluates the performance of selected methods for (i) capturing, transporting, and delivering hydrocarbon fluid samples from the field to the analysis laboratory, coupled with (ii) analyzing for properties related to composition and volatility of the oil, including vapor pressure, gas-oil ratio, and dissolved gases and light hydrocarbons. Several combinations of sample capture and analysis were observed to perform well in both summer and winter sampling environments, though conditions apply that need to be considered carefully for given applications. Methods that perform well from Task 2 will then be utilized in subsequent Task 3 (combustion studies) and Task 4 (compositional analyses of multiple crude types), to be addressed in subsequent reports.

02 PETROLEUM↗
DOE OSTI2023

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗
DOE OSTI2013

Objected constrained registration and manifold learning: A new patient setup approach in image guided radiation therapy of thoracic cancer

Purpose: The management of thoracic malignancies with radiation therapy is complicated by continuous target motion. In this study, a real time motion analysis approach is proposed to improve the accuracy of patient setup. Methods: For 11 lung cancer patients a long training fluoroscopy was acquired before the first treatment, and multiple short testing fluoroscopies were acquired weekly at the pretreatment patient setup of image guided radiotherapy (IGRT). The data analysis consisted of three steps: first a 4D target motion model was constructed from 4DCT and projected to the training fluoroscopy through deformable registration. Then the manifold learning method was used to construct a 2D subspace based on the target motion (kinetic) and location (static) information in the training fluoroscopy. Thereafter the respiratory phase in the testing fluoroscopy was determined by finding its location in the subspace. Finally, the phase determined testing fluoroscopy was registered to the corresponding 4DCT to derive the pretreatment patient position adjustment for the IGRT. The method was tested on clinical image sets and numerical phantoms. Results: The registration successfully reconstructed the 4D motion model with over 98% volume similarity in 4DCT, and over 95% area similarity in the training fluoroscopy. The machine learning method derived the phase values in over 98% and 93% test images of the phantom and patient images, respectively, with less than 3% phase error. The setup approach achieved an average accumulated setup error less than 1.7 mm in the cranial-caudal direction and less than 1 mm in the transverse plane. All results were validated against the ground truth of manual delineations by an experienced radiation oncologist. The expected total time for the pretreatment setup analysis was less than 10 s. Conclusions: By combining the registration and machine learning, the proposed approach has the potential to improve the accuracy of pretreatment setup for patients with thoracic malignancy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Comparison of Gafchromic EBT2 and EBT3 for patient-specific quality assurance: Cranial stereotactic radiosurgery using volumetric modulated arc therapy with multiple noncoplanar arcs

Purpose: Patient-specific quality assurance in volumetric modulated arc therapy (VMAT) brain stereotactic radiosurgery raises specific issues on dosimetric procedures, mainly represented by the small radiation fields associated with the lack of lateral electronic equilibrium, the need of small detectors and the high dose delivered (up to 30 Gy). Gafchromic{sup TM} EBT2 and EBT3 films may be considered the dosimeter of choice, and the authors here provide some additional data about uniformity correction for this new generation of radiochromic films.Methods: A new analysis method using blue channel for marker dye correction was proposed for uniformity correction both for EBT2 and EBT3 films. Symmetry, flatness, and field-width of a reference field were analyzed to provide an evaluation in a high-spatial resolution of the film uniformity for EBT3. Absolute doses were compared with thermoluminescent dosimeters (TLD) as baseline. VMAT plans with multiple noncoplanar arcs were generated with a treatment planning system on a selected pool of eleven patients with cranial lesions and then recalculated on a water-equivalent plastic phantom by Monte Carlo algorithm for patient-specific QA. 2D quantitative dose comparison parameters were calculated, for the computed and measured dose distributions, and tested for statistically significant differences.Results: Sensitometric curves showed a different behavior above dose of 5 Gy for EBT2 and EBT3 films; with the use of inhouse marker-dye correction method, the authors obtained values of 2.5% for flatness, 1.5% of symmetry, and a field width of 4.8 cm for a 5 × 5 cm{sup 2} reference field. Compared with TLD and selecting a 5% dose tolerance, the percentage of points with ICRU index below 1 was 100% for EBT2 and 83% for EBT3. Patients analysis revealed statistically significant differences (p < 0.05) between EBT2 and EBT3 in the percentage of points with gamma values <1 (p= 0.009 and p= 0.016); the percent difference as well as the mean difference between calculated and measured isodoses (20% and 80%) were found not to be significant (p= 0.074, p= 0.185, and p= 0.57).Conclusions: Excellent performances in terms of dose homogeneity were obtained using a new blue channel method for marker-dye correction on both EBT2 and EBT3 Gafchromic{sup TM} films. In comparison with TLD, the passing rates for the EBT2 film were higher than for EBT3; a good agreement with estimated data by Monte Carlo algorithm was found for both films, with some statistically significant differences again in favor of EBT2. These results suggest that the use of Gafchromic{sup TM} EBT2 and EBT3 films is appropriate for dose verification measurements in VMAT stereotactic radiosurgery; taking into account the uncertainty associated with Gafchromic film dosimetry, the use of adequate action levels is strongly advised, in particular, for EBT3.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

SUGAR: An improved empirical model of Type Ia supernovae based on spectral features

Type Ia supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. This model was constructed from SNe Ia spectral properties and spectrophotometric data from the Nearby Supernova Factory collaboration. In a first step, a principal component analysis-like method was used on spectral features measured at maximum light, which allowed us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties were used to extract the average extinction curve. Third, an interpolation using Gaussian processes facilitated using data taken at different epochs during the lifetime of an SN Ia and then projecting the data on a fixed time grid. Finally, the three steps were combined to build the SED model as a function of time and wavelength. This is the SUGAR model. Results. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity and the second correlates with their calcium lines. The addition of these parameters, as well as the high quality of the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade. The performance of this model makes it an excellent SED model for experiments like the Zwicky Transient Facility, the Large Synoptic Survey Telescope, or the Wide Field Infrared Survey Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Scientific Case for Concurrent Neutron and X-ray Scattering and Spectroscopy

The interrogation of materials with X-rays or neutrons to determine the structure, energetics, and dynamics of materials is fundamental to advancing materials' physical and chemical science and developing innovative material technologies. A transcending challenge in developing novel materials is that progress hinges on understanding the structure and dynamics across multiple time and length scales in complex materials that feature multiple components, interfaces, and compositions. Despite the ever-growing demands on materials’ characterization, existing approaches are almost exclusively based on isolated X-ray or neutron scattering, i.e., an approach commensurate with the more narrowly defined needs of fifty years ago. A three-day workshop sponsored by the U.S. National Science Foundation (NSF) analyzed the demand for concurrent neutron and X-ray (NeX) experiments. It was held at the Spring Hill Suites, San Jose, California, from June 2 to 4, 2022. In this workshop, 70 national and international experts ascertained the crucial need to establish NeX capabilities to advance the science of complex materials and systems in the US. Here, we illustrate the need for NeX scattering and spectroscopy experiments by showcasing examples that span areas as diverse as biomaterials, energy science, soft matter, and nanomaterials. To provide NeX capability will require new instrumentation that enables concurrent experiments. Affected areas include chemistry, soft matter, quantum materials, pure and applied chemistry, bioscience, geoscience, and applied materials. NeX benefits research outcomes due to the complementarity of the two techniques, which is essential for better model refinement. While joint refinement of data from separate neutron and X-ray experiments is critical to avoid ambiguities, especially in multiphase-multicomponent materials, concurrent experiments overcome scientific and technical barriers associated with single measurements, separated by location and, thus, time. Among all the examples, these factors introduce uncertainties in the results that complicate data analysis. [1,2] [3] While models are strongly sample-dependent, the principles of joint refinement are generally applicable to these disciplines, including the development of advanced parameterization, modeling, and analysis techniques that also consider the temporal and spatial resolutions of the two methods, leading to unambiguous data interpretation. Solutions for technical barriers must be found to realize NeX experiments, including developing robust sample environments that meet the optical requirements of neutrons and X-rays.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking

Malware is one of the most persistent and costly cyber threats endangering reputation, confidentiality, integrity, and availability for organizations and national security. Consequently, many of the incident detection and prevention systems, and incident responders have begun to utilize machine learning as a helper in the fight against malware and other cyber threats. However, cyber defenders rely on interpretability and generalizability, yet the popular machine learning methods are black-box and often use traditional supervised solutions that do not generalize to novel malware. Therefore, there is a need to improve the existing solutions. At the same time, the majority of the prior research ignored essential evaluation criteria when reporting the results of their methods, which disables the safe reproducibility of the methods in a production environment. Tensor decomposition, on the other hand, enables interpretable unsupervised analysis of the large-scale data for the discovery of hidden patterns. Our findings, performed on real-world and large-scale experiments, show that tensor factorization-based methods yield performance results that surpasses or competes with existing supervised solutions with the added benefit of interpretability and generalizability. With the ability to analyze complex and large-scale data using tensors, we report results that reflect real-world production environments. We propose to develop new game- changing tools for malware identification and characterization that can trace malware evolution, rank the infected or malicious hosts, and streamline the work of incident response teams, malware analysts, and incident detection and prevention systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Last Journey. I. An Extreme-scale Simulation on the Mira Supercomputer

The Last Journey is a large-volume, gravity-only, cosmological N-body simulation evolving more than 1.24 trillion particles in a periodic box with a side length of 5.025 Gpc. It was implemented using the HACC simulation and analysis framework on the BG/Q system Mira. The cosmological parameters are chosen to be consistent with the results from the Planck satellite. A range of analysis tools have been run in situ to enable a diverse set of science projects and, at the same time, keep the resulting data amount manageable. Analysis outputs have been generated starting at redshift z ~ 10 to allow for construction of synthetic galaxy catalogs using a semianalytic modeling approach in postprocessing. As part of our in situ analysis pipeline, we employ a new method for tracking halo substructures, introducing the concept of subhalo cores. The production of multiwavelength synthetic sky maps is facilitated by generating particle light cones in situ, also beginning at z ~ 10. We provide an overview of the simulation setup and generated data products; a first set of analysis results is presented. A subset of the data is publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Integration of Gas Chromatography Mass Spectrometry Methods for Differentiating Ricin Preparation Methods

The investigation of crimes involving chemical or biological agents is infrequent, but presents unique analytical challenges. The protein toxin ricin is encountered more frequently than other agents and is found in the seeds of the castor plant Ricinus communis. Typically, the toxin is extracted from castor seeds utilizing a variety of different recipes that result in varying purity of the toxin. Moreover, these various purification steps can also leave or differentially remove a variety of exogenous and endogenous residual components with the toxin that may indicate the type and number of purification steps involved. We have applied three gas chromatographic - mass spectrometric (GC-MS) based analytical methods to measure the variation in seed carbohydrates and castor oil ricinoleic acid as well as the presence of solvents used for purification. These methods were applied to the same samples prepared using four previously identified toxin preparation methods starting from four varieties of castor seeds. The individual data sets for seed carbohydrate profiles, ricinoleic acid or acetone amount each provided information capable of differentiating different types of toxin preparations across seed types. However, the integration of the data sets using multivariate factor analysis provided a clear distinction of all samples based on the preparation method and independent of the seed source. In particular the abundance of mannose, arabinose, fucose, ricinoleic acid and acetone were shown to be important differentiating factors. These complementary tools provide a more confident determination of the method of toxin preparation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Reproducibility and Replication of Experimental Particle Physics Results

Recently, much attention has been focused on the replicability of scientific results, causing scientists, statisticians, and journal editors to examine closely their methodologies and publishing criteria. Experimental particle physicists have been aware of the precursors of non-replicable research for many decades and have many safeguards to ensure that the published results are as reliable as possible. The experiments require large investments of time and effort to design, construct, and operate. Large collaborations produce and check the results, and many papers are signed by more than three thousand authors. This paper gives an introduction to what experimental particle physics is and to some of the tools that are used to analyze the data. It describes the procedures used to ensure that results can be computationally reproduced, both by collaborators and by non-collaborators. It describes the status of publicly available data sets and analysis tools that aid in reproduction and recasting of experimental results. It also describes methods particle physicists use to maximize the reliability of the results, which increases the probability that they can be replicated by other collaborations or even the same collaborations with more data and new personnel. Examples of results that were later found to be false are given, both with failed replication attempts and one with alarmingly successful replications. While some of the characteristics of particle physics experiments are unique, many of the procedures and techniques can be and are used in other fields.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Flow Analysis on a Limited Volume Chilled Water System

LANL Currently has a limited volume chilled water system for use in a glove box, but the system needs to be updated. Before we start building our new system, a flow analysis is needed to ensure that there are no high flow rates, extreme pressures, or any other hazards involved in the system. In this project the piping system is extremely important to us because it directly affects the overall design of the entire system. The primary components necessary for the chilled water piping system are shown in the design. They include the pipes themselves (perhaps of more than one diameter), the various fitting used to connect the individual pipes to form the desired system, the flow rate control devices (valves), and the pumps that add energy to the fluid. Even the most simple pipe systems are actually quite complex when they are viewed in terms of rigorous analytical considerations. I used an 'exact' analysis and dimensional analysis considerations combined with experimental results for this project. When 'real-world' effects are important (such as viscous effects in pipe flows), it is often difficult or impossible to use only theoretical methods to obtain the desired results. A judicious combination of experimental data with theoretical considerations and dimensional analysis are needed in order to reduce risks to an acceptable level.

42 ENGINEERING↗
DOE OSTI2021

Identification and correction of temporal and spatial distortions in scanning transmission electron microscopy

Scanning transmission electron microscopy (STEM) has become the technique of choice for quantitative characterization of atomic structure of materials, where the minute displacements of atomic columns from high-symmetry positions can be used to map strain, polarization, octahedra tilts, and other physical and chemical order parameter fields. The latter can be used as inputs into mesoscopic and atomistic models, providing insight into the correlative relationships and generative physics of materials on the atomic level. However, these quantitative applications of STEM necessitate understanding the microscope induced image distortions and developing the pathways to compensate them both as part of a rapid calibration procedure for in situ imaging, and the post-experimental data analysis stage. Here, we explore the spatiotemporal structure of the microscopic distortions in STEM using multivariate analysis of the atomic trajectories in the image stacks. Based on the behavior of principal component analysis (PCA), we develop the Gaussian process (GP)-based regression method for quantification of the distortion function. The limitations of such an approach and possible strategies for implementation as a part of in-line data acquisition in STEM are discussed. Here, the analysis workflow is summarized in a Jupyter notebook that can be used to retrace the analysis and analyze the reader's data.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Spatially extended and high-velocity dispersion molecular component in spiral galaxies: Single-dish versus interferometric observations

Recent studies of the molecular medium in nearby galaxies have provided mounting evidence that the molecular gas can exist in two phases: one that is clumpy and organized as molecular clouds and another one that is more diffuse. This last component has a higher velocity dispersion than the clumpy one. In order to investigate these two molecular components further, we compare the fluxes and line widths of CO in NGC 4736 and NGC 5055, two nearby spiral galaxies for which high-quality interferometric as well as single-dish data sets are available. Our analysis leads to two main results: (1) employing three different methods, we determine the flux recovery of the interferometer as compared to the single-dish to be within a range of 35%–74% for NGC 4736 and 81%–92% for NGC 5055, and (2) when focusing on high (S/N ≥ 5) lines of sight (LOSs), the single-dish line widths are larger by ∼(40 ± 20)% than the ones derived from interferometric data, which is in agreement with stacking all LOSs. These results point to a molecular gas component that is distributed over spatial scales larger than 30″(∼1 kpc), and is therefore filtered out by the interferometer. The available observations do not allow us to distinguish between a truly diffuse gas morphology and a uniform distribution of small clouds that are separated by less than the synthesized beam size (∼3″ or ∼100 pc), as they would both be invisible for the interferometer. This high velocity dispersion component has a dispersion similar to what is found in the atomic medium, as traced through observations of the H i line.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Cell G 7606A Mockup Measurement Analysis Methods and Results

The production of research isotopes at the Radiochemical Engineering Development Center (REDC) invariably leads to the accumulation of radioactive waste. Storage of this waste onsite at ORNL serves as an interim solution prior to shipment to long term storage facilities, however onsite capacity is limited. Proper characterization of the activity of waste products is essential for determining the appropriate waste stream and, ultimately, mitigating the cost of disposal. This is typically done via gamma spectrometry and use of the In-Situ Object Counting System (ISOCS), a software package from Mirion that serves as an accepted community standard. However, variability in the contents, density, and activity distribution in waste containers can introduce large errors in ISOCS quantification. A measurement campaign using a mockup of the proposed setup in Cell G seeks to quantify the magnitude and source of these systematic errors. Analysis of this data will guide the creation of ISOCS geometry templates and measurement methods designed to minimize overall uncertainty in reported activities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

A Geometry-Driven Longitudal Topic Model

A simple and scalable framework for longitudinal analysis of Twitter data is developed that combines latent topic models with computational geometric methods. Dimensionality reduction tools from computational geometry are applied to learn the intrinsic manifold on which the latent, temporal topics reside. Then shortest path distances on the manifold are used to link together these topics. The proposed framework permits visualization of the low-dimensional embedding which provides clear interpretation of the complex, high-dimensional trajectories that may exist among latent topics. Practical application of the proposed framework is demonstrated through its ability to capture and effectively visualize natural progression of latent COVID-19 related topics learned from Twitter data. Interpretability of the trajectories is achieved by comparing to real-world events. In addition, the framework permits study of spatial variation in Twitter behavior for learned topics. The analysis demonstrates that the proposed framework is able to capture granular-level impact of COVID-19 on public discussions. We end by arguing that Twitter data, when analyzed within the proposed framework, can serve as a valuable supplementary data stream for COVID-related studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Graph Analytics and Optimization Methods for Insights from the Uber Movement Data

In this work we leverage the Uber movement dataset for the Los Angeles (LA) area where partial TAZ to TAZ (Traffic Analysis Zone) trip time data is available. We first create a TAZ-TAZ network based on nearest neighbors and propose a model that allows us to complete the $(O-D)$ (Origin-Destination) travel time matrix, using optimization methods such as non-negative least squares. We apply these algorithms for several communities in the TAZ-TAZ network and present insights in the form of completed $(O-D)$ matrices and associated temporal trends. We qualify the error performance and scalability of our flows. We conclude by pointing out the directions in our ongoing work to improve the quality and scale of travel time estimation.

Visweswara Sathanur, Arun↗
DOE OSTI2011

Data and Communications in Basic Energy Sciences: Creating a Pathway for Scientific Discovery

This report is based on the Department of Energy (DOE) Workshop on “Data and Communications in Basic Energy Sciences: Creating a Pathway for Scientific Discovery” that was held at the Bethesda Marriott in Maryland on October 24-25, 2011. The workshop brought together leading researchers from the Basic Energy Sciences (BES) facilities and Advanced Scientific Computing Research (ASCR). The workshop was co-sponsored by these two Offices to identify opportunities and needs for data analysis, ownership, storage, mining, provenance and data transfer at light sources, neutron sources, microscopy centers and other facilities. Their charge was to identify current and anticipated issues in the acquisition, analysis, communication and storage of experimental data that could impact the progress of scientific discovery, ascertain what knowledge, methods and tools are needed to mitigate present and projected shortcomings and to create the foundation for information exchanges and collaboration between ASCR and BES supported researchers and facilities. The workshop was organized in the context of the impending data tsunami that will be produced by DOE’s BES facilities. Current facilities, like SLAC National Accelerator Laboratory’s Linac Coherent Light Source, can produce up to 18 terabytes (TB) per day, while upgraded detectors at Lawrence Berkeley National Laboratory’s Advanced Light Source will generate ~10TB per hour. The expectation is that these rates will increase by over an order of magnitude in the coming decade. The urgency to develop new strategies and methods in order to stay ahead of this deluge and extract the most science from these facilities was recognized by all. The four focus areas addressed in this workshop were: Workflow Management - Experiment to Science: Identifying and managing the data path from experiment to publication. Theory and Algorithms: Recognizing the need for new tools for computation at scale, supporting large data sets and realistic theoretical models. Visualization and Analysis: Supporting near-real-time feedback for experiment optimization and new ways to extract and communicate critical information from large data sets. Data Processing and Management: Outlining needs in computational and communication approaches and infrastructure needed to handle unprecedented data volume and information content. It should be noted that almost all participants recognized that there were unlikely to be any turn-key solutions available due to the unique, diverse nature of the BES community, where research at adjacent beamlines at a given light source facility often span everything from biology to materials science to chemistry using scattering, imaging and/or spectroscopy. However, it was also noted that advances supported by other programs in data research, methodologies, and tool development could be implemented on reasonable time scales with modest effort. Adapting available standard file formats, robust workflows, and in-situ analysis tools for user facility needs could pay long-term dividends. Workshop participants assessed current requirements as well as future challenges and made the following recommendations in order to achieve the ultimate goal of enabling transformative science in current and future BES facilities: Theory and analysis components should be integrated seamlessly within experimental workflow. Develop new algorithms for data analysis based on common data formats and toolsets. Move analysis closer to experiment. Move the analysis closer to the experiment to enable real-time (in-situ) streaming capabilities, live visualization of the experiment and an increase of the overall experimental efficiency. Match data management access and capabilities with advancements in detectors and sources. Remove bottlenecks, provide interoperability across different facilities/beamlines and apply forefront mathematical techniques to more efficiently extract science from the experiments. This workshop report examines and reviews the status of several BES facilities and highlights the successes and shortcomings of the current data and communication pathways for scientific discovery. It then ascertains what methods and tools are needed to mitigate present and projected data bottlenecks to science over the next 10 years. The goal of this report is to create the foundation for information exchanges and collaborations among ASCR and BES supported researchers, the BES scientific user facilities, and ASCR computing and networking facilities. To jumpstart these activities, there was a strong desire to see a joint effort between ASCR and BES along the lines of the highly successful Scientific Discovery through Advanced Computing (SciDAC) program in which integrated teams of engineers, scientists and computer scientists were engaged to tackle a complete end-to-end workflow solution at one or more beamlines, to ascertain what challenges will need to be addressed in order to handle future increases in data

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Directly imaging steeply-dipping fault zones in geothermal fields with multicomponent seismic data

For characterizing geothermal systems, it is important to have clear images of steeply-dipping fault zones because they may confine the boundaries of geothermal reservoirs and influence hydrothermal flow. Elastic reverse-time migration (ERTM) is the most promising tool for subsurface imaging with multicomponent seismic data. However, conventional ERTM usually generates significant artifacts caused by the cross correlation of undesired wavefields and the polarity reversal of shear waves. In addition, it is difficult for conventional ERTM to directly image steeply-dipping fault zones. We develop a new ERTM imaging method in this paper to reduce these artifacts and directly image steeply-dipping fault zones. In our new ERTM method, forward-propagated source wavefields and backward-propagated receiver wavefields are decomposed into compressional (P) and shear (S) components. Furthermore, each component of these wavefields is separated into left- and right-going, or downgoing and upgoing waves. The cross correlation imaging condition is applied to the separated wavefields along opposite propagation directions. For converted waves (P-to-S or S-to-P), the polarity correction is applied to the separated wavefields based on the analysis of Poynting vectors. Numerical imaging examples of synthetic seismic data demonstrate that our new ERTM method produces high-resolution images of steeply-dipping fault zones.

58 GEOSCIENCES↗