Mapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430-970 nm)

摘要

Hyperspectral imagery is used to map the distribution of iron and separate iron ore from shale (a waste product) on a vertical mine face in an open-pit mine in the Pilbara, Western Australia. Vertical mine faces have complex surface geometries which cause large spatial variations in the amount of incident and reflected light. Methods used to analyse imagery must minimise these effects whilst preserving any spectral variations between rock types and minerals. Derivative analysis of spectra to the 1st-, 2nd- and 4th-order is used to do this. To quantify the relative amounts and distribution of iron, the derivative spectrum is integrated across the visible and near infrared spectral range (430-970 nm) and over those wavelength regions containing individual peaks and troughs associated with specific iron absorption features. As a test of this methodology, results from laboratory spectra acquired from representative rock samples were compared with total amounts of iron minerals from X-ray diffraction (XRD) analysis. Relationships between derivatives integrated over the visible near-infrared range and total amounts (% weight) of iron minerals were strongest for the 4th- and 2nd-derivative (R~2 = 0.77 and 0.74, respectively) and weakest for the lst-derivative (R~2 = 0.56). Integrated values of individual peaks and troughs showed moderate to strong relationships in 2nd- (R~2 = 0.68-0.78) and 4th-derivative (R~2 - 0.49-0.78) spectra. The weakest relationships were found for peaks or troughs towards longer wavelengths. The same derivative methods were then applied to imagery to quantify relative amounts of iron minerals on a mine face. Before analyses, predictions were made about the relative abundances of iron in the different geological zones on the mine face, as mapped from field surveys. Integration of the whole spectral curve (430-970 nm) from the 2nd- and 4th-derivative gave results which were entirely consistent with predictions. Conversely, integration of the lst-derivative gave results that did not fit with predictions nor distinguish between zones with very large and small amounts of iron oxide. Classified maps of ore and shale were created using a simple level-slice of the 1st-derivative reflectance at 702, 765 and 809 nm. Pixels classified as shale showed a similar distribution to kaolinite (an indicator of shales in the region), as mapped by the depth of the diagnostic kaolinite absorption feature at 2196 nm. Standard statistical measures of classification performance (accuracy, precision, recall and the Kappa coefficient of agreement) indicated that nearly all of the pixels were classified correctly using 1st-derivative reflectance at 765 and 809 nm. These results indicate that data from the VNIR (430-970 nm) can be used to quantify, without a priori knowledge, the total amount of iron minerals and to distinguish ore from shale on vertical mine faces.