--> Abstract: Automated Classification of Sedimentary Units in Drill Cores and Outcrops using high-resolution Hyperspectral Imaging and Neural Networks; #90063 (2007)

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Automated Classification of Sedimentary Units in Drill Cores and Outcrops using high-resolution Hyperspectral Imaging and Neural Networks

 

Ragona, Daniel Eduardo1, Bernard Minster2, Tom Rockwell3 (1) University of Califorinia, San Diego, La Jolla, CA (2) University of California, San Diego, La Jolla, CA (3) San Diego State University, San Diego, CA

 

We present a new methodology for automatic mapping of sedimentary stratigraphy of drill cores or outcrops using short wave infrared (SWIR) hyperspectral images and multilayer perceptron (MLP). Ground-based hyperspectral imaging provides an effective method to study and store stratigraphic and structural data from cores or field exposures. Neural networks supply a variety of well-established techniques towards pattern recognition, especially for data examples with high-dimensionality input-outputs. High-spatial/spectral resolution data from drill cores were collected using a portable hyperspectral scanner with 245 continuous channels measured across the 960 to 2404 nm spectral range. The data were corrected and pre-processed to obtain the reflectance spectra. For the supervised classification we built a set using hundreds of reflectance spectra of the sediments of the cores. The examples were grouped into eight classes corresponding to the most common materials found in the samples. A MLP was trained to construct the classification models. The best model achieved 98.4 % classification accuracy. Using this model we generated classification images of the cores that show an excellent description of the stratigraphic units of the, even in areas where the human eye was not capable to identify layers. In conclusion, the results of this work show that reflectance spectra combined with neural networks can be used to properly discern and classify sediments of very similar composition and grain size. Quantitative identification of geological materials can be used as a fast and objective method to describe, automatically classify and assist in the correlation of samples, drill cores and outcrops.

 

AAPG Search and Discover Article #90063©2007 AAPG Annual Convention, Long Beach, California