Masters Theses

Date of Award

12-2004

Degree Type

Thesis

Degree Name

Master of Science

Major

Physics

Major Professor

William E. Blass

Committee Members

Robert N. Compton, Chia C. Shih, Stephen J. Daunt

Abstract

Geophysical disturbances resulting from human activities often have significant
consequences for plants and animals, and even for entire ecosystems.Disturbances resulting from petroleum exploration and production activities can have long term impacts on soils, watersheds, rivers and lakes, vegetation, wildlife, and humans. These anthropogenic disturbances are frequently the result of hydrocarbon (oil) or produced water (brine) spills. Brine is usually produced simultaneously with oil or gas. The ability to detect brine spills with remote sensing techniques would be valuable to petroleum companies and industry regulators. The objectives of this research were to 1) determine if brine spills could be detected spectroscopically, 2) determine if spectral analysis could be performed using a statistical method to identify surface features quickly and easily from large imaging spectroscopy data sets without modeling and removing atmospheric effects or performing detailed spectral unmixing, 3) develop a spectral signature for brine spills which could be applied at other locations, and 4) determine if brine spills could be detected using substantially fewer spectral bands so that a smaller and cheaper instrument could be applied to detect these disturbances. Using hyperspectral image cubes acquired by NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over Osage County, Oklahoma, a multivariate statistical clustering technique successfully discerned well-documented brine disturbances on the Tallgrass Prairie Preserve, and the resulting brine spectral signature was applied to locate similar brine disturbances in surrounding image scenes. While validating the prediction results by visiting the site was outside the scope of this project, high resolution aerial photographs were used to assess the success of the predictions and attribute at least 40 of the 87 prediction regions to petroleum activities. While a number of false positives resulted from the analysis, many of these are easily discounted based on objects in the aerial photographs or explained by mineral/salt accumulation. In addition, four bands from the 224-band hyperspectral imagery were used to predict brine disturbances in one of the image cubes. Approximately 90% of the prediction regions detected in the original analysis—which used 187 of the 224 bands—were again detected using only four spectral bands.

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