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DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

DOE OSTI2021

Testing of Instrumentation and Control Sensors and Cables for Small Modular Reactors

As advanced reactor concepts like small modular reactors (SMRs) progress towards design maturity and commercial deployment, it is important to assess their instrumentation and control (I&C) system sensors and cables to assure their safe, reliable, and efficient operation throughout their service lifetimes. The I&C system of any nuclear power plant (NPP) is the central nervous system of the plant and is made up of field devices such as sensors which are connected via cables to analog and/or digital systems. These systems are responsible for control and/or protection of the plant. The performance of I&C sensors and cables is dependent on several factors, including plant operating conditions. In particular, the plant operation can affect the static (accuracy) and dynamic (response) performance of the sensor, whereas the surrounding ambient environment can impact the remaining useful life of the cables, connectors, and other associated components within I&C sensor circuits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Evaluation of Low Cost Infrasound Sensor Packages

Sandia National Laboratories has tested and evaluated the performance of the following five models of low-cost infrasound sensors and sensor packages: Camas microphone, Gem Infrasound Logger, InfraBSU sensor, Raspberry Boom, and the Samsung S10 smartphone utilizing the Redvox app. The purpose of this infrasound sensor evaluation is to measure the performance characteristics in such areas as power consumption, sensitivity, self-noise, dynamic range, response, passband, linearity, sensitivity variation due to changes in static pressure and temperature, and sensitivity to vertical acceleration. The infrasound monitoring community has leveraged such sensors and integrated packages in novel ways; better understanding the performance of these units serves the geophysical monitoring community.

42 ENGINEERING↗

DOE OSTI2021

Retro-Commissioning Sensor Suitcase for Energy Efficiency

This project will enable Pacific Northwest National Laboratory (PNNL) and Lawrence Berkeley National Laboratory (LBNL) to work with industry partner, GreenPath Energy Solutions, joint developers of the Suitcase, to enhance the capabilities and usability of the Retro-Commissioning Sensor Suitcase (hereinafter the Sensor Suitcase or Suitcase), assisting GreenPath to take the Suitcase to market and rapidly expand their market share. The proposed project will specifically focus on 1) adding sensors types to extend the data collection capability and support more building and equipment performance metrics and identification of even more energy saving opportunities, 2) developing algorithms to identify recommendations for the new energy savings opportunities from item 1 and to prototype software modifications implementing them, 3) validating the use of vibration sensors to detect the operating state of a broader set of packaged HVAC equipment (additional capacities, different numbers of stages, etc.) than tested in initial development and modifying the state algorithm and software code, as needed, 4) improving cost-effectiveness in manufacturing the Sensor Suitcase, and 5) additional field testing of the technology in real buildings to more comprehensively validate Suitcase performance and to guide refinement of its capabilities. Project results will position GreenPath, and potential future licensees, to implement the new capabilities developed in this project in GreenPath’s RCx Building Suitcase, increasing its functionality and the savings resulting from its use. These enhancements will increase the value of the Suitcase to users and increase the potential market for its use and impacts in that impact.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Novel Harsh Environment Materials and Fabrication Techniques for Wireless Sensor Applications (Final Report)

The overarching goal of this project is to establish a center of excellence program at the University of Maine that is focused on Harsh Environmental Materials and Fabrication Techniques for Wireless Sensor Applications. UMaine is well-positioned to build on previous successes in the areas of materials science research and sensor engineering. Since 1980, the Laboratory for Surface Science & Technology (LASST) has been a very successful interdisciplinary UMaine research center with a well-established infrastructure to investigate surfaces, interfaces, thin films, and micro/nano-fabrication. This infrastructure established through NSF, DOE, DOD, NASA, the State of Maine, and industry includes (i) a 3,500 ft2 clean room with nano/microfabrication and photolithography instrumentation, (ii) thin film synthesis, processing and characterization, (iii) surface and thin film analytical chemistry tools, and (iv) device packaging, electronic testing, wireless devices, system fabrication, and sensor test facilities. This strong foundation is the basis for a planned significant growth in R&D capacity currently underway. To that end, since the start of this project in the Fall of 2019, LASST has undergone a transition to become a new interdisciplinary center at UMaine, named the Frontier Institute for Research in Sensor Technologies (FIRST). This new focus represents a commitment and investment by UMaine that aligns with the proposed DOE-EPSCoR theme, capitalizing on the state-of-the-art instrumentation to form an energized group of faculty and students pursuing advances in sensor materials, devices, and applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Journey Over Destination: Differentiable Sensor Placement Enhances Generalization [Poster]

The challenge of reconstructing spatial fields that change over time from limited sensor data has been a focal point for many research studies. Various machine learning methods have been used in attempts to address this complex issue, including convolutional neural networks. All of the proposed methods share a common requirement that the user needs to manually determine the sensor positions. This requirement remains a limiting factor in the ongoing quest for efficient learning and accurate field reconstruction. This study aims to present a method that enables a model to optimize sensor positions via backpropagation, thereby facilitating the model’s exploration of the spatial domain and enhancing sensor positioning effectively. Indexing naturally incorporates discrete decisions. This operation is nondifferentiable which is a requirement for the application of gradient-based optimization methods. We showcased its effectiveness by training an attention-based neural network, which achieved top-tier performance on two separate datasets. To our knowledge, this represents the first fully end-to-end differentiable workflow for enhancing sensor placement within a neural network model.

58 GEOSCIENCES↗

DOE OSTI2023

Development of Optical Fiber-Based Sensors for Nuclear Microreactor Structural Health Monitoring

This report provides an experimental assessment of two different optical fiber–based acoustic sensors that are being investigated for application in nuclear microreactors to enhance structural health monitoring capabilities. Optical fibers are resilient in high-temperature and high-radiation environments, have a small sensor footprint, are immune to electromagnetic interference, and are capable of spatially distributed sensing. The two sensors investigated here are (1) Fabry–Pérot Cavities (FPCs) between two copper-coated fibers, embedded in nickel capillary tubes and (2) type-I fiber Bragg grating (FBG) arrays contained within metal capillary tubes. These sensors can be interrogated using low-coherence interferometry or swept wavelength interferometry, respectively, to measure the resonant frequencies of the components or systems to which these sensors are bonded. The FPC developed herein has been subjected to temperatures up to nearly 800°C while tack-welded to a tubular test specimen. Even at the highest temperatures, the measured resonant frequencies compared well with those obtained using an accelerometer that was bonded to an unheated portion of the specimen. The FBG array was tested in multiple bonding configurations to a tubular test specimen, all at room temperature, with the understanding that high temperature (i.e., type II) FBGs could be used to obtain similar data at high temperatures; the FBG array data were validated with noncontact laser vibrometry, which is being used at Los Alamos National Laboratory to relate acoustic signatures to component stresses and/or structural defects. The goal of this work is to identify the most promising techniques that are also compatible with operation in a microreactor environment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Blockchain Empowered Provenance Framework for Sensor Identity Management and Data Flow Security in Fossil-based Power Plants

The overall goals of this project are divided into 3 important phases. First, a peer-to-peer SCADA network is to be established where identity profile of sensors from fossil-power plants will be stored in an immutable manner in order to verify data flow integrity in real time. Second, it is aimed to develop a Blockchain-based provenance platform to audit equipment operations along with data and process integrity violations. In this phase, various operational rules will be incorporated in the ledger on behalf of the operating sensor so its abnormal use can be easily detected. In addition, it is aimed to achieve provenance when the rate of transactions is exceedingly high, thus enabling to design a scalable provenance scheme that can work in real time when number of data collecting points or sensor units increase in fossil-based power plants. Finally, a working testbed will be developed to integrate the Blockchain platform with sensor-based fossil power plant comprising of Remote Terminal Units (RTU) and SCADA devices. Using multiple Raspberry-PIs mimicking the role of RTUs that are interfaced with multiple sensors devices will be deployed to serve as a critical component of fossil power plant and the performance metrics such as throughput, latency, bootstrapping time, etc., will be evaluated on the testbed.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Robustness and printed sensor qualification

A limiting factor of additive manufactured (AM) sensors for in-pile applications is the development of appropriate interconnection and packaging strategies that can maintain reliable performance in extreme environments. Failure mechanisms in these harsh conditions could include, but not limited to, materials interaction (i.e., intermetallic formation) and coefficient of thermal expansion (CTE) mismatch between the individual components of the sensors. To mitigate premature failure and enable the successful sustained operation of AM sensors, non-destructive qualification tests are used as an intermediate process control step used for verifying robustness and reliability of the sensor before they are deployed. A materials system of interest is the use of barium strontium titanium oxide (BST) films on stainless steel 316L (SS316L) substrate. Due to BST’s high dielectric constant and tunable dielectric properties, there is an interest for its application as an insulation/encapsulation layer for capacitive strain gauges when printed on structural materials. Previous work on the BST/SS316L material system, however, showed that the BST cracks when exposed to temperatures up to 600 °C due to CTE mismatch between the printed BST film and the metallic substrate. When comparing different fabrication techniques, less surface cracking was observed in the samples that deposited thinner (i.e., 2-20 μm) than samples that were thicker (i.e., 135 µm – 300 µm). The objective of this report is to establish a qualification process that will determine and address any challenges (i.e., mechanical failure of thick prints) of the AM sensor prior to its application in experimental tests. To demonstrate the qualification process in this report, laser spallation and uniaxial tensile tests using dynamic and quasi-static loading, respectively, will be discussed.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

Evaluation of Howard A. Hanson Dam Juvenile Fish Passage and Survival Study Live Fish Injury Assessment, Sensor Fish, and BioPA Modeling Tasks

The live fish injury assessment, Sensor Fish, and BioPA modeling study tasks were conducted by researchers from Pacific Northwest National Laboratory (PNNL). The four tasks were part of the larger Evaluation of Howard A. Hanson Dam (HAHD) Juvenile Fish Passage and Survival study, which had six total tasks. To achieve study objectives for each of the four tasks, field work occurred at Green Peter Dam (GPR) to evaluate the highest elevation steep slope bypass pipe, at HAHD to evaluate baseline conditions of the horseshoe tunnel, and at PNNL’s Aquatic Research Laboratory (ARL) to evaluate simulated dam passage conditions (i.e., shear forces and collision). Each of these evaluations utilized live fish injury assessment, Sensor Fish, and BioPA modeling. Live fish injury assessment and survival (tagged with and without balloon or passive integrated transponder [PIT] tags) was correlated with Sensor Fish to determine thresholds. The CFD analyses were then performed, and the computed values were compared to the corresponding measured values of Sensor Fish data. The results of the overall injury and survival of fish was also used in the validation of the CFD modeling method. Collectively, the results will aid in future modeling of fish passage at HAHD. Results from these tasks can be used by biologists, engineers, resource managers, and regional decision-makers to inform baseline conditions under current operations and the engineering design of the new FPF at HAHD. This draft report contains initial data and results from the four tasks. Table 8 1, Table 8 2, and Table 8 3, and Figure 8 1, Figure 8 2, and Figure 8 3 depict the CFD modeling findings for the GPR steep slope bypass, HAHD horseshoe tunnel, and laboratory testing. Table 8 4, Table 8 5, and Table 8 6 depict the Sensor Fish findings for the GPR steep slope bypass and HAHD horseshoe tunnel testing. The Mv values observed in the HAHD were significantly lower compared to the laboratory experiments conducted at PNNL. Currently, investigations are underway to understand the reasons for this disparity and to establish an appropriate threshold value for Mv. Survival predictions presented in the tables below should be considered preliminary and should not be used until further analyses and adjustments are completed. The next steps for modeling will include the flow regime, (i.e., density of flow regimes due to water and air mixing ) to continue to improve on the threshold value for Mv.

13 HYDRO ENERGY↗

DOE OSTI2026

Quality Control of Silicon Sensor Modules for Particle Detectors

The High-Luminosity Large Hadron Collider (HL-LHC) will produce a higher rate of particle collisions than the current Large Hadron Collider (LHC), requiring important upgrades to the Compact Muon Solenoid (CMS) to handle an increased amount of data. An important upgrade is the Phase-2 Outer Tracker Upgrade, which consists of 13,000 silicon sensor modules made of two parallel silicon sensors and readout electronics. These modules undergo careful quality control checks both during and after module assembly to ensure precise and reliable detector performance. This project focuses on precision testing for quality control of silicon sensor modules at Fermilab. Hands-on work includes visual inspection, current-voltage testing, module testing, and ultraviolet (UV) light exposure of modules showing abnormal current-voltage behavior. The ultraviolet exposure process improves the abnormal sensor readout data by placing the selected sensor side of the module directly under the UV light inside a controlled box. In addition to laboratory testing and ultraviolet experiments, I developed a Python-based data tool that connects to a module database and allows selected testing conditions and module information to be retrieved and displayed efficiently. These different testing procedures, experimental processes, and computational tools support the broader goal of identifying module issues and improving modules that will be used in the CMS Outer Tracker Phase-2 Upgrade.

Siddiqui, Hooriya [DuPage Coll.] (ORCID:0009000151↗

DOE OSTI2021

Direct Mercury Detection in Landfill Leachate Using a Novel AuNP-Biopolymer Carbon Screen-Printed Electrode Sensor

A novel Au nanoparticle (AuNP)-biopolymer coated carbon screen-printed electrode (SPE) sensor was developed through the co-electrodeposition of Au and chitosan for mercury (Hg) ion detection. This new sensor showed successful Hg2+ detection in landfill leachate using square wave anodic stripping voltammetry (SWASV) with an optimized condition: a deposition potential of −0.6 V, deposition time of 200 s, amplitude of 25 mV, frequency of 60 Hz, and square wave step voltage of 4 mV. A noticeable peak was observed at +0.58 V associated with the stripping current of the Hg ion. The sensor exhibited a good sensitivity of ~0.09 μA/μg (~0.02 μA/nM) and a linear response over the concentration range of 10 to 100 ppb (50–500 nM). The limit of detection (LOD) was 1.69 ppb, which is significantly lower than the safety limit defined by the United States Environmental Protection Agency (USEPA). The sensor had an excellent selective response to Hg2+ in landfill leachate against other interfering cations (e.g., Zn2+, Pb2+, Cd2+, and Cu2+). Fifteen successive measurements with a stable peak current and a lower relative standard deviation (RSD = 5.1%) were recorded continuously using the AuNP-biopolymer-coated carbon SPE sensor, which showed excellent stability, sensitivity and reproducibility and consistent performance in detecting the Hg2+ ion. It also exhibited a good reliability and performance in measuring heavy metals in landfill leachate.

Hwang, Jae-Hoon (ORCID:0000000220100864)↗

DOE OSTI2024

A Hermetic Package Technique for Multi-Functional Fiber Sensors through Pressure Boundary of Energy Systems Based on Glass Sealants

This paper presents a hermitic fiber sensor packaging technique that enables fiber sensors to be embedded in energy systems for performing multi-parameter measurements in high-temperature and strong radiation environments. A high-temperature stable Intrinsic Fabry–Perot interferometer (IFPI) array, inscribed by a femtosecond laser direct writing scheme, is used to measure both temperature and pressure induced strain changes. To address the large disparity in thermo-expansion coefficients (TECs) between silica fibers and metal parts, glass sealants with TEC between silica optical fibers and metals were used to hermetically seal optical fiber sensors inside stainless steel metal tubes. The hermetically sealed package is validated for helium leakages between 1 MPa and 10 MPa using a helium leak detector. An IFPI sensor embedded in glass sealant was used to measure pressure. The paper demonstrates an effective technique to deploy fiber sensors to perform multi-parameter measurements in a wide range of energy systems that utilize high temperatures and strong radiation environments to achieve efficient energy production.

Optics↗

DOE OSTI2019

Design and Evaluation of Sensor Housing for Boundary Layer Profiling Using Multirotors

Traditional configurations for mounting Temperature–Humidity (TH) sensors on multirotor Unmanned Aerial Systems (UASs) often suffer from insufficient radiation shielding, exposure to mixed and turbulent air from propellers, and inconsistent aspiration while situated in the wake of the UAS. Descent profiles using traditional methods are unreliable (when compared to an ascent profile) due to the turbulent mixing of air by the UAS while descending into that flow field. Consequently, atmospheric boundary layer profiles that rely on such configurations are bias-prone and unreliable in certain flight patterns (such as descent). This article describes and evaluates a novel sensor housing designed to shield airborne sensors from artificial heat sources and artificial wet-bulbing while pulling air from outside the rotor wash influence. The housing is mounted above the propellers to exploit the rotor-induced pressure deficits that passively induce a high-speed laminar airflow to aspirate the sensor consistently. Our design is modular, accommodates a variety of other sensors, and would be compatible with a wide range of commercially available multirotors. Extensive flight tests conducted at altitudes up to 500 m Above Ground Level (AGL) show that the housing facilitates reliable measurements of the boundary layer phenomena and is invariant in orientation to the ambient wind, even at high vertical/horizontal speeds (up to 5 m/s) for the UAS. A low standard deviation of errors shows a good agreement between the ascent and descent profiles and proves our unique design is reliable for various UAS missions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Printed Potentiometric Nitrate Sensors for Use in Soil

Plant-available nitrogen, often in the form of nitrate, is an essential nutrient for plant growth. However, excessive nitrate in the environment and watershed has harmful impacts on natural ecosystems and consequently human health. A distributed network of nitrate sensors could help to quantify and monitor nitrogen in agriculture and the environment. Here, we have developed fully printed potentiometric nitrate sensors and characterized their sensitivity and selectivity to nitrate. Each sensor comprises an ion-selective electrode and a reference electrode that are functionalized with polymeric membranes. The sensitivity of the printed ion-selective electrodes was characterized by measuring their potential with respect to a commercial silver/silver chloride reference electrode in varying concentrations of nitrate solutions. The sensitivity of the printed reference electrodes to nitrate was minimized with a membrane containing polyvinyl butyral (PVB), sodium chloride, and sodium nitrate. Selectivity studies with sulphate, chloride, phosphate, nitrite, ammonium, calcium, potassium, and magnesium showed that high concentrations of calcium can influence sensor behavior. The printed ion-selective and reference electrodes were combined to form a fully printed sensor with sensitivity of −48.0 ± 3.3 mV/dec between 0.62 and 6200 ppm nitrate in solution and −47 ± 4.1 mV/dec in peat soil.

Baumbauer, Carol L. (ORCID:0000000246747551)↗

DOE OSTI2024

Experimental Investigation of Low-Frequency Distributed Acoustic Sensor Responses to Two Parallel Propagating Fractures

Low-frequency distributed acoustic sensing (LF-DAS) is a diagnostic tool for hydraulic fracture propagation with far-field monitoring using fiber optic sensors. LF-DAS senses strain rate variation caused by stress field change due to fracture propagation. Fiber optic sensors are installed in the monitoring wells in the vicinity of a fractured well. From the strain responses, fracture propagation can be evaluated. To understand subsurface conditions with multiple propagating fractures, a laboratory-scale hydraulic fracture experiment was performed simulating the LF-DAS response to fracture propagation with embedded distributed optical fiber strain sensors under these conditions. The experiment was performed using a transparent cube of epoxy with two parallel radial initial flaws centered in the cube. Fluid was injected into the sample to generate fractures along the initial flaws. The experiment used distributed high-definition fiber optic strain sensors with tight spatial resolutions. The sensors were embedded at two different locations on opposite sides of the initial flaws, serving as observation/monitoring locations. We also employed finite element modeling to numerically solve the linear elastic equations of equilibrium continuity and stress–strain relationships. The measured strains from the experiment were compared to simulation results from the finite element model. The experimentally derived strain and strain-rate waterfall plots from this study show the responses to both fractures propagating, while the fracture at the lower position took most of the fluid during the experiment. Interestingly, a fracture first began propagating from the upper flaw of the two flaws, but once the lower fracture was initiated, it grew much faster than the upper fracture. Both fibers were intercepted by the lower fracture, further verifying the strain signature as a fracture is approaching and intersecting an offset fiber.

Chemistry↗

NASA NTRS1972

Determination of the inflight OTF of orbital earth resources sensors

There are no author-identified significant results in this report. A technique has been investigated that is particularly applicable to evaluation of earth-orbiting multispectral sensors. In all programs employing such sensors, simultaneous underflight photography from aircraft is made during passes of the spacecraft. To evaluate the spacecraft imagery, the two sets of photographs are scanned and digitized with a microdensitometer. The data are then Fourier analyzed, and the spatial frequency spectra is calculated. The spatial frequencies in the underflight trace are scaled to those in the spacecraft trace, and the ratio of the spectra gives the in-flight optical transfer function (OTF) for the orbiting sensor. The method has been applied to an Apollo 9 frame and the sensor OTF has been calculated. The results are good enough to encourage use of the technique and to indicate the accuracies required of the various measurements involved in determining in-flight sensor OTF by this method. A technique for determining correct scan registration and scaling between the two sets of imagery and a promising approach to noise reduction, in the form of weighted averaging of OTF's, are discussed.

Slater, P. N.↗

NASA NTRS1972

Application of remote sensor data to geologic analysis of the Bonanza test site, Colorado

A variety of remote sensor data has aided geologic mapping in central Colorado. This report summarizes the application of sensor data to both regional and local geologic mapping and presents some conclusions on the practical use of remote sensing for solving geologic mapping problems. It is emphasized that this study was not conducted primarily to test or evaluate remote sensing systems or data, but, rather, to apply sensor data as an accessory tool for geologic mapping. The remote sensor data used were acquired by the NASA Earth Observations Aircraft Program. Conclusions reached on the utility of the various sensor data and interpretation techniques for geologic mapping were by-products of attempts to use them.

Lee, K.↗