## Descriptions, Spectral Plots,and Digital Reflectance Spectra of Samples Applied to Spectral Analysis ofImaging Spectroscopy Data:

## Utah (East Tintic Mountains, Oquirrh Mountains, WasatchMountains, and Tushar Mountains), Nevada (Goldfield Hills), and New Mexico(Jemez Mountains), USA, 1999-2002

## by Barnaby W. Rockwell1

**Open-File Report 02-407**

**Version 1.2**

**2002**  

This report is preliminary and has not beenreviewed for conformity with U.S. Geological Survey editorial standards orwith the North American Stratigraphic Code. Any use of trade, firm, or productnames is for descriptive purposes only and does not imply endorsement bythe U.S. Government.

**U.S. DEPARTMENT OF THE INTERIOR****U.S. GEOLOGICAL SURVEY**

**1****barnabyr@usgs.gov, Denver, Colorado**

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This report documents rock samples that were analyzed in the laboratory as a part of several projects, including the USGS/USEPA Utah Abandoned Mine Lands Imaging Spectroscopy Project. These samples are intimate mixtures of various minerals associated with hydrothermal alteration and supergene weathering.  Reflectance spectra of these samples were used as standard reference spectra for the spectral analysis of AVIRIS data covering parts of Utah, Nevada (Goldfield Hills, Esmeralda County), and New Mexico (Jemez Mountains near Los Alamos). Sample compositions were determined using bulk X-ray Diffraction (XRD) analysis, the results of which are listed in thedescription pages for each sample. 

The samples where spectrally characterized in the laboratory using an ASD FieldSpec Pro FR spectrometer over the 0.35 to 2.50 µm range. Forty spectral measurements were integrated to generate each saved spectrum, and from 10 to 15 saved spectrawere averaged to generate the spectra published here. The ASD FR spectrometerhas three separate detector arrays that acquire data from different wavelengthregions of the electromagnetic spectrum (array 1: VNIR, 0.35-1.05 µm;array 2: SWIR1, 1.00-1.80 µm; and array 3: SWIR2, 1.80-2.50 µm).Offsets in reflectance level between data acquired by each of the three detectorarrays were computed and removed by extrapolating the linear trends fromseveral channels at each end of the more stable SWIR1 array, calculatingthe offsets between the extrapolated trends and the first channels of theadjacent arrays, and adding the offsets to adjust all data from the adjacentarrays. The resultant spectra were converted to absolute reflectance (ABSREF) by multiplying the reflectance spectrum of the Spectralon white referencestandard by each measured average sample spectrum.  The ABS REF spectra(717 channels) were convolved to 2151 channels to increase the trapezoidalintegration for the final convolutions to the wavelength and bandpass ofthe AVIRIS instrument. All spectral convolutions were performed using cubicspline algorithms (Carnahan and Wilkes, 1973).

The spectra published herein were incorporated into the Tetracorder expert system (Clark and others, 2002), which was used for materials identification from laboratory and imaging spectroscopy data. One or more diagnostic spectral absorption features were identified in each spectrum for analysis by the Tetracorder system. The wavelength intervals of these absorption features are indicated on the description page of each sample, and are defined by twoendpoints, each of which is represented by a short wavelength interval (usually0.03 microns) over which spectral data are averaged to reduce effects ofnoise. The wavelength intervals defining an absorption feature represent thespectral regions over which a modified least-squares curve-fitting algorithm compared the reference spectra to AVIRIS image spectra. After the absorption features are isolated from broader, more general curve shapes using continuum removal (Clark, 1999), each reference spectrum was scaled to match the feature depth of the AVIRIS spectra. Curve-fitting was then performed between reference and AVIRIS spectra, generating a value of “fit” for each absorption feature. The degree of curve "fit" for each diagnostic feature was weighted based onthe relative area of that feature beneath the continuum, with weights ofall diagnostic features summing to unity. Therefore, the “fit” values ofdeep and broad features with large areas had more influence on material identificationthan the “fits” of features with smaller areas. The weighted curve "fits"for each diagnostic feature were then summed to derive a single composite"fit" value for each reference spectrum which was compared to the  composite"fit" values of other materials characterized by vibrational absorptions inthe 1.4 – 2.5 µm spectral region. The material with the largest composite"fit" for this group of materials was selected as the identified material.

Some diagnostic absorption features are shallow in depth, narrow in width, and/or occur in wavelength regions subject to low signal-to-noise in data from a particular sensor, and are, thus, not ideal for curve fitting. These features were identified as "weak" and were utilized by the Tetracorder expert system in a manner different from that used for the principal diagnosticfeatures. In the analysis of AVIRIS spectra, the “weak” features had to existabove a globally-defined “fit” threshold for the composite “fit” of a materialto be included in the selective competition between materials leading toan identification, but the “fit” value for the “weak” feature was not includedin the composite “fit” for that material. Features designated as "weak" areindicated with a "W" on the description pages. An example of a "weak" featureis the jarosite feature at 0.43 µm evident in sample BRCM-1.

**NOTE: All latitude/longitude coordinates for sample collection locations    are relative to the NAD 27 horizontal datum.**

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