Application of LANDSAT-2 data to the implementation and enforcement of the Pennsylvania Surface Mining Conservation and Reclamation Act
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A group of knowledgeable scientists and engineers in New Mexico has recognized the need for such a testing capability and has proposed a project to evelop an extraterrestrial surface simulation facility. A group of universities, national laboratories, and private industrial firms is proposing to establish a Center for Extraterrestrial Engineering and Construction (CETEC) and to develop large extraterrestrial surface simulation facilities in which this needed testing can be realistically performed. The CETEC is envisioned to be both a center of knowledge and data regarding engineering, construction, mining, and material process operations on extraterrestrial bodies and a set of extraterrestrial surface simulation facilities. The primary CETEC facility is proposed to be a large domed building made of steel reinforced concrete with more than one acre of test floor area covered with several feet of simulated lunar soil and dust. Various aspects of the project are presented in viewgraph form.
The Airborne Visible Infrared Imaging Spectrometer (AVIRIS) acquires spectral imaging data covering the 0.4 - 2.5 micron wavelength range in 224 10-nm-wide channels from a NASA ER-2 aircraft at 20 km. More than half of the spectral region is affected by atmospheric gaseous absorption. Over the past decade, several techniques have been used to remove atmospheric effects from AVIRIS data for the derivation of surface reflectance spectra. An operational atmosphere removal algorithm (ATREM), which is based on theoretical modeling of atmospheric absorption and scattering effects, has been developed and updated for deriving surface reflectance spectra from AVIRIS data. Due to small errors in assumed wavelengths and errors in line parameters compiled on the HITRAN database, small spikes (particularly near the centers of the 0.94- and 1.14-micron water vapor bands) are present in this spectrum. Similar small spikes are systematically present in entire ATREM output cubes. These spikes have distracted geologists who are interested in studying surface mineral features. A method based on the "global" fitting of spectra with low order polynomials or other functions for removing these weak spikes has recently been developed by Boardman (this volume). In this paper, we describe another technique, which fits spectra "locally" based on cubic spline smoothing, for quick post processing of ATREM apparent reflectance spectra derived from AVIRIS data. Results from our analysis of AVIRIS data acquired over Cuprite mining district in Nevada in June of 1995 are given. Comparisons between our smoothed spectra and those derived with the empirical line method are presented.
A former Ames employee, Monte Zweben, founded a new company, Blue Martini Software, that provides software to companies seeking to personalize their products to individual customers. This customer targeting approach is accomplished through the use of artificial intelligence concepts Zweben worked on while at Ames. The Ames AI research has found applications in clickstream mining and purchasing behavior data collection.
Over the past year, the Webnucleo Group at Clemson University has been developing a web site with a number of interactive online tools for astronomy and cosmochemistry applications. The site uses SHP (Simplified Hypertext Preprocessor), which, because of its flexibility, allows us to embed almost any computer language into our web pages. For a description of SHP, please see http://www.joeldenny.com/ At our web site, an internet user may mine large and complex data sets, such as our stellar evolution models, and make graphs or tables of the results. The user may also run some of our detailed nuclear physics and astrophysics codes, such as our nuclear statistical equilibrium code, which is written in fortran and C. Again, the user may make graphs and tables and download the results.
Historically, spacecraft landing systems have been tested with human volunteers, because analytical methods for estimating injury risk were insufficient. These tests were conducted with flight-like suits and seats to verify the safety of the landing systems. Currently, NASA uses the Brinkley Dynamic Response Index to estimate injury risk, although applying it to the NASA environment has drawbacks: (1) Does not indicate severity or anatomical location of injury (2) Unclear if model applies to NASA applications. Because of these limitations, a new validated, analytical approach was desired. Leveraging off of the current state of the art in automotive safety and racing, a new approach was developed. The approach has several aspects: (1) Define the acceptable level of injury risk by injury severity (2) Determine the appropriate human surrogate for testing and modeling (3) Mine existing human injury data to determine appropriate Injury Assessment Reference Values (IARV). (4) Rigorously Validate the IARVs with sub-injurious human testing (5) Use validated IARVs to update standards and vehicle requirement
Process mining for conformance analysis focuses on comparing a reference process model against a data-driven process model that is generated via log files from information technology systems. While this approach is helpful when there is an existing process model in an organization, it leaves the question of what to do in the absence of a complete reference process model unanswered. In this paper, we present a comparative assessment approach that combines process mining, process mapping for dimensionality reduction, and statistical analysis. Our goal is to find similarities and dissimilarities in data-driven process models among U.S. Veterans Health Administration (VHA) facilities to assess process conformance among different healthcare facilities, which can help assess the standardization of care. We illustrate our approach by applying it to two clinical radiology order process models generated by two similar facilities. Our results demonstrate statistical similarities in the standardization of care among those two facilities.
For the past five years, the Biological and Physical Sciences Division has pioneered Open Science in Space Biology by funding the NASA GeneLab project. Along with the Ames Life Sciences Data Archive, GeneLab has quickly become the world leader in archiving and scientifically curating spaceflight and spaceflight relevant multi-omics data. Specifically, the GeneLab Data System has become a full enterprise solution providing advanced mining capabilities, several application programming interfaces for data federation and machine learning approaches, and delivering to the world an analytical and visualization platform which has enabled collaboration within the scientific community. Over the past three years, large meta-analysis and modeling studies have been published by the GeneLab Analysis Working Groups (AWGs), which are comprised of ~200 volunteer scientists. One natural extension of GeneLab data reuse has recently turned towards linking animal data with human data, which is the next necessary step to further validate animal models for inferring biological risks to humans conducting LEO, lunar or Martian missions. As such, data from the Human Research Program are an essential component of GeneLab and ALSDA. At the moment, simulated space radiation experiments conducted at Brookhaven National Laboratory make the most of HRP GeneLab data, and the scientific community has been eager to also link their animal spaceflight results to actual Astronaut data and human analog data. We will discuss further the current status of knowledge and future approaches to accelerate our basics understanding of the impact of space stressors on humans using latest omics technology.
The United States demand for Neodymium-Iron-Boron (NdFeB) magnets, produced from rare earth elements (REEs) such as (Nd) and Dysprosium (Dy), far exceeds its nascent domestic production capacity, rendering it reliant on vulnerable global supply chains dominated by China. To guide research and development investments in securing U.S. REE supply, defensible benchmark metrics across environmental, economic, and social dimensions are needed. In this study, we built globally-representative, process-based cradle-to-cradle life cycle inventories for Nd and Dy in NdFeB magnets lifecycles, encompassing primary material acquisition, beneficiation, smelting and refining, metal processing, specialty alloy and chemical transformation, subcomponent manufacturing, consumer application (use phase) and end-of-life management. We carried out detailed literature review, and applied process engineering principles to build industry-representative upscaled life cycle inventories for both metals. We used these models to conduct bottom-up literature review and gap analysis on existing literature, compilation of data sources for each life cycle stage (and transformations where necessary), and a preliminary technoeconomic analysis (TEA)/life cycle costing analysis (LCCA). Findings from this work emphasize the need for metal specific, representative REE LCIs to establish robust benchmarks for advancing sustainable REE technologies and guiding R&D in REE supply chains.
The application of remote sensing to the inventory of coal surface mines and to the monitoring of mine reclamation in the eastern United States was investigated. Data were acquired during spring and autumn by several sensor systems over study areas located within the bituminous coal fields of Pennsylvania. Data sources were the Landsat MSS, an airborne multispectral scanner (Daedalus DS-1260), the airborne Thematic Mapper Simulator, and high-altitude color and color infrared aerial photography. A comparison of the data sets was conducted by a quantitative assessment of area measurement accuracy. Landsat data were found most suitable for a synoptic inventory of mines on a regional basis. High-altitude aerial photography was considered the best source of the detailed information required for reclamation monitoring. Nine channels of data from the airborne scanner were evaluated to select the most useful spectral bands for discriminating among the land cover types associated with surface mines. Four bands were selected in the following order by a stepwise linear discriminant procedure: 0.60-0.65 micron, 0.92-1.10 microns, 0.80-0.89 micron, and 8-14 microns. The data corresponding to these four bands were used for the thematic mapping of land cover.
Interferometric synthetic aperture radar (InSAR) data over the area of the Hector Mine earthquake (Mw 7.1, 16 October 1999) reveal postseismic deformation of several centimeters over a spatial scale of 0.5 to 50 km. We analyzed seven SAR acquisitions to form interferograms over four time periods after the event. The main deformations seen in the line-of-sight (LOS) displacement maps are a region of subsidence (60 mm LOS increase) on the northern end of the fault, a region of uplift (45 mm LOS decrease) located to the northeast of the primary fault bend, and a linear trough running along the main rupture having a depth of up to 15 mm and a width of about 2 km. We correlate these features with a double left-bending, rightlateral, strike-slip fault that exhibits contraction on the restraining side and extension along the releasing side of the fault bends. The temporal variations in the near-fault postseismic deformation are consistent with a characteristic time scale of 135 + 42 or - 25 days, which is similar to the relaxation times following the 1992 Landers earthquake. High gradients in the LOS displacements occur on the fault trace, consistent with afterslip on the earthquake rupture. We derive an afterslip model by inverting the LOS data from both the ascending and descending orbits. Our model indicates that much of the afterslip occurs at depths of less than 3 to 4 km.
SUMMARY We describe array methods to search for low signal-to-noise ratio (SNR) signals in long-period seismic data using Fourier analysis. This is motivated by published results that find evidence of solar free oscillations in the Earth's seismic hum. Previous work used data from only one station. In this paper, we describe methods for computing spectra from array data. Arrays reduce noise level through averaging and provide redundancy that we use to distinguish coherent signal from a random background. We describe two algorithms for calculating a robust spectrum from seismic arrays, an algorithm that automatically removes impulsive transient signals from data, a jackknife method for estimating the variance of the spectrum, and a method for assessing the significance of an entire spectral band. We show examples of their application to data recorded by the Homestake Mine 3-D array in Lead, SD and the Piñon Flats PY array. These are two of the quietest small aperture arrays ever deployed in North America. The underground Homestake data has exceptionally low noise, and the borehole sensors of the PY array also have very low noise, making these arrays well suited to finding very weak signals. We find that our methods remove transient signals effectively from the data so that even low-SNR signals in the seismic background can be found and tested. Additionally, we find that the jackknife variance estimate is comparable to the noise floor, and we present initial evidence for solar g-modes in our data through the T2 test, a multivariate generalization of Student's t-test.
Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems.
The functions, operational procedures, and major items of equipment that comprise the terrestrial mining process are characterized. These data are used to synthesize a similar activity on the lunar surface. Functions, operations, and types of equipment that can be suitably transferred to lunar operation are identified. Shortfalls, enhancements, and technology development needs are described. The lunar mining process and what is required to adapt terrestrial equipment are highlighted. It is concluded that translation of terrestrial mining equipment and operational processes to perform similar functions on the lunar surface is practical. Adequate attention must be given to the harsh environment and logistical constraints of the lunar setting. By using earth-based equipment as a forcing function, near- and long-term benefits are derived (i.e., improved terrestrial mining in the near term vis-a-vis commercial production of helium-3 in the long term.
Interpolating scattered data points is a problem of wide ranging interest. A number of approaches for interpolation have been proposed both from theoretical domains such as computational geometry and in applications' fields such as geostatistics. Our motivation arises from geological and mining applications. In many instances data can be costly to compute and are available only at nonuniformly scattered positions. Because of the high cost of collecting measurements, high accuracy is required in the interpolants. One of the most popular interpolation methods in this field is called ordinary kriging. It is popular because it is a best linear unbiased estimator. The price for its statistical optimality is that the estimator is computationally very expensive. This is because the value of each interpolant is given by the solution of a large dense linear system. In practice, kriging problems have been solved approximately by restricting the domain to a small local neighborhood of points that lie near the query point. Determining the proper size for this neighborhood is a solved by ad hoc methods, and it has been shown that this approach leads to undesirable discontinuities in the interpolant. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. This process achieves its efficiency by replacing the large dense kriging system with a much sparser linear system. This technique has been applied to a restriction of our problem, called simple kriging, which is not unbiased for general data sets. In this paper we generalize these results by showing how to apply covariance tapering to the more general problem of ordinary kriging. Through experimentation we demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined with iterative methods for solving large sparse systems. We demonstrate our approach on large data sizes arising both from synthetic sources and from real applications.
We collect vehicle trajectory data from major transportation network companies (TNCs) in New York City (NYC) in 2017 and 2019, and we use the trajectory data to understand how the growth of TNCs has impacted traffic congestion and emission in urban areas. By mining the large-scale trajectory data and conduct the case study in NYC, we confirm that the rise of TNC is the major contributing factor that makes urban traffic congestion worse. From 2017 to 2019, the number of for-hire vehicles (FHV) has increased by over 48% and served 90% more daily trips. These resulted in an average citywide speed reduction of 22.5% on weekdays, and the average speed in Manhattan decreased from 11.76 km/h in April 2017 to 9.56 km/h in March 2019. The heavier traffic congestion may have led to 136% more NOx, 152% more CO and 157% more HC emission per kilometer traveled by the FHV sector. Our results show that the traffic condition is consistently worse across different times of the day and at different locations in NYC. And we build the connection between the number of available FHVs and the reduction in travel speed between the two years of data and explain how the rise of TNC may impact traffic congestion in terms of moving speed and congestion time. Our findings provide valuable insights for different stakeholders and decision-makers in framing regulation and operation policies towards more effective and sustainable urban mobility.
Ion mobility (IM) is often combined with LC-MS experiments to provide an additional dimension of separation for complex sample analysis. While highly complex samples are better characterized by the full dimensionality of LC-IM-MS experiments to uncover new information, downstream data analysis workflows are often not equipped to properly mine the additional IM dimension. For many samples the data acquisition benefits of including IM separations are all that is necessary to uncover sample information and the full dimensionality of the data is not required for data analysis. Post-acquisition reduction and adaptation of the dimensions of LC-IM-MS and IM-MS experiments into an LC-MS format opens the possibility to use a plethora of existing software tools. In this work, we developed data file conversion tools to reduce the complexity of IM data analysis. Three data file transformations are introduced in the PNNL PreProcessor software: 1) mapping the IM axis to the LC axis for IM-MS data, 2) converting the drift time vs. m/z space to CCS/z vs m/z space, and 3) transforming All Ions IM/MS mobility aligned fragmentation data to a standard LC-MS DDA data file format. Finally, these new data file conversions are demonstrated with corresponding lipidomics and proteomics workflows that leverage existing LC-MS data analysis software to highlight the benefits of the data transformations.
The U.S. Bureau of Mines is developing a computer model to analyze the human factors aspect of mining machine operator compartments. The model will be used as a research tool and as a design aid. It will have the capability to perform the following: simulated anthropometric or reach assessment, visibility analysis, illumination analysis, structural analysis of the protective canopy, operator fatigue analysis, and computation of an ingress-egress rating. The model will make extensive use of graphics to simplify data input and output. Two dimensional orthographic projections of the machine and its operator compartment are digitized and the data rebuilt into a three dimensional representation of the mining machine. Anthropometric data from either an individual or any size population may be used. The model is intended for use by equipment manufacturers and mining companies during initial design work on new machines. In addition to its use in machine design, the model should prove helpful as an accident investigation tool and for determining the effects of machine modifications made in the field on the critical areas of visibility and control reach ability.