Richard L. Bedell

Bio: Richard L. Bedell is an academic researcher from University of Nevada, Reno. The author has contributed to research in topics: Hyperspectral imaging & Spectral signature. The author has an hindex of 1, co-authored 1 publications receiving 67 citations.

Mapping alteration minerals at prospect, outcrop and drill core scales using imaging spectrometry

Abstract: Imaging spectrometer data (also known as ‘hyperspectral imagery’ or HSI) are well established for detailed mineral mapping from airborne and satellite systems. Overhead data, however, have substantial additional potential when used together with ground-based measurements. An imaging spectrometer system was used to acquire airborne measurements and to image in-place outcrops (mine walls) and boxed drill core and rock chips using modified sensor-mounting configurations. Data were acquired at 5 nm nominal spectral resolution in 360 channels from 0.4 to 2.45 μm. Analysis results using standardized hyperspectral methodologies demonstrate rapid extraction of representative mineral spectra and mapping of mineral distributions and abundances in map-plan, with core depth, and on the mine walls. The examples shown highlight the capabilities of these data for mineral mapping. Integration of these approaches promotes improved understanding of relations between geology, alteration and spectral signatures in three dime...

Mapping Advanced Argillic Alteration at Cuprite, Nevada, Using Imaging Spectroscopy

Abstract: Mineral maps based on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were used to study late Miocene advanced argillic alteration at Cuprite, Nevada. Distributions of Fe-bearing minerals, clays, micas, sulfates, and carbonates were mapped using the Tetracorder spectral-shape matching system. The Al content of white micas increases toward altered areas and near intrusive rocks. Alunite composition varies from pure K to intimate mixtures of Na-K endmembers with subpixel occurrences of huangite, the Ca analogue of alunite. Intimately mixed Na-K alunite marks areas of relatively lower alteration temperature, whereas co-occurring Na-alunite and dickite may delineate relict hydrothermal conduits. The presence of dickite, halloysite, and well-ordered kaolinite, but absence of disordered kaolinite, is consistent with acidic conditions during hydrothermal alteration. Partial lichen cover on opal spectrally mimics chalcedony, limiting its detection to lichen-free areas. Pods of buddingtonite are remnants of initial quartz-adularia-smectite alteration. Thus, spectral maps provide a synoptic view of the surface mineralogy, and define a previously unrecognized early steam-heated hydrothermal event. Faulting and episodes of hydrothermal alteration at Cuprite were intimately linked to upper plate movements above the Silver Peak-Lone Mountain detachment and growth, collapse, and resurgence of the nearby Stonewall Mountain volcanic complex between 8 and 5 Ma. Isotopic dating indicates that hydrothermal activity started at least by 7.61 Ma and ended by about 6.2 Ma. Spectral and stable isotope data suggest that Cuprite is a late Miocene low-sulfidation adularia-sericite type hot spring deposit overprinted by late-stage, steam-heated advanced argillic alteration formed along the margin of the Stonewall Mountain caldera.

A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion

Abstract: Mining companies heavily rely on drill-core samples during exploration campaigns as they provide valuable geological information to target important ore accumulations. Traditional core logging techniques are time-consuming and subjective. Hyperspectral (HS) imaging, an emerging technique in the mining industry, is used to complement the analysis by rapidly characterizing large amounts of drill-cores in a nondestructive and noninvasive manner. As the accurate analysis of drill-core HS data is becoming more and more important, we explore the use of machine learning techniques to improve speed and accuracy, and help to discover underlying relations within large datasets. The use of supervised techniques for drill-core HS data represents a challenge since quantitative reference data is frequently not available. Hence, we propose an innovative procedure to fuse high-resolution mineralogical analysis and HS data. We use an automatic high-resolution mineralogical imaging system (i.e., scanning electron microscopy-mineral liberation analysis) for generating training labels. We then resample the MLA image to the resolution of the HS data and adopt a soft labeling strategy for mineral mapping. We define the labels for the classes as mixtures of geological interest and use the classifiers (random forest and support vector machines) to map the entire drill-core. We validate our framework qualitatively and quantitatively. Thus, we demonstrate the ability of the proposed technique to fuse and up-scale high-resolution mineralogical analysis with drill-core HS data.

Integration of Terrestrial and Drone-Borne Hyperspectral and Photogrammetric Sensing Methods for Exploration Mapping and Mining Monitoring

Abstract: Mapping lithology and geological structures accurately remains a challenge in difficult terrain or in active mining areas We demonstrate that the integration of terrestrial and drone-borne multi-sensor remote sensing techniques significantly improves the reliability, safety, and efficiency of geological activities during exploration and mining monitoring We describe an integrated workflow to produce a geometrically and spectrally accurate combination of a Structure-from-Motion Multi-View Stereo point cloud and hyperspectral data cubes in the visible to near-infrared (VNIR) and short-wave infrared (SWIR), as well as long-wave infrared (LWIR) ranges acquired by terrestrial and drone-borne imaging sensors Vertical outcrops in a quarry in the Freiberg mining district, Saxony (Germany), featuring sulfide-rich hydrothermal zones in a granitoid host, are used to showcase the versatility of our approach The image data are processed using spectroscopic and machine learning algorithms to generate meaningful 25D (ie, surface) maps that are available to geologists on the ground just shortly after data acquisition We validate the remote sensing data with thin section analysis and laboratory X-ray diffraction, as well as point spectroscopic data The combination of ground- and drone-based photogrammetric and hyperspectral VNIR, SWIR, and LWIR imaging allows for safer and more efficient ground surveys, as well as a better, statistically sound sampling strategy for further structural, geochemical, and petrological investigations

Imaging spectroscopy of geological samples and outcrops: Novel insights from microns to meters

Abstract: Imaging spectroscopy is a powerful, non-destructive mineralogic tool that provides insights into a variety of geological processes. This remote measurement technique has been used for decades from orbital or aerial platforms to characterize surface compositions of Earth and other solar system bodies. These instruments have now been miniaturized for use in the laboratory and field, thereby enabling petrologic analyses of samples and outcrops. Here, we review the technique and present four examples showing the exciting science potential and new insights into geological processes.