Automated Multi-Scale and Multivariate Geological Logging from Drill-Core Hyperspectral Data

Abstract

Hyperspectral drill-core scanning adds value to exploration campaigns by providing continuous, high-resolution mineralogical data over the length of entire boreholes. However, multivariate mineralogical data must be transformed into lithological domains such that it is compatible with interpolation techniques and be usable for geomodeling. Manual interpretation of multivariate drill-core data is a challenging, time-consuming and subjective task, and automated or semi-automated approaches are needed. However, naive machine-learning techniques that ignore the distinct spatial structure and multi-scale nature of geological systems tend to produce geologically unreasonable results. Automated geological logging and multi-scale hierarchical domaining of drill-cores has been previously addressed in several studies by means of scalograms from a wavelet transform and tessellation, albeit exploiting only univariate information. The methodology involves the extraction of the local first principal component at a neighborhood of each observation, and the segmentation of the resulting series of scores with a continuous wavelet transform for boundary detection. In this way, the correlation pattern between the variables is incorporated into the segmentation. The scalogram accurately locates the geological boundaries at depth and yields hierarchical geological domains with mineralogical composition characteristics. The performance of this approach is demonstrated on a synthetic as well as a real multivariate dataset. The real dataset consists of mineral abundances derived from drill-core hyperspectral imaging data acquired in Elvira, a shale-hosted volcanogenic massive sulfide deposit located in the Iberian Pyrite Belt, where 7000 m of drill-core were acquired along 80 boreholes. The extracted domains are sensible from a geological point of view and spatially coherent across the boreholes in cross-sections. The results at relevant scales were qualitatively validated by comparing against the lithological log. This method is fast, is appropriate for multivariate geological data along boreholes, and provides a choice of scales for hierarchical geological domains along boreholes with mineralogical composition characteristics that can be modeled in 3D. Our approach provides an automatic way to transform hyperspectral image-derived mineral maps into vertically coherent geological units that are appropriate inputs for 3D geological modeling workflows. Moreover, the method improves the boundary detection and geological domaining by making use of multivariate information.

Air Pollution Detection Using a Novel Snap-Shot Hyperspectral Imaging Technique

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Predictive Prospecting Using Remote Sensing in a Mountainous Terrestrial Volcanic Area, in Western Bangongco–Nujiang Mineralization Belt, Tibet

“…Moreover, multispectral data can make up for the shortage of hyperspectral data to a certain extent, which can improve the spatial resolution by merging with panchromatic bands so as to enhance iron-stained alteration mineral anomalies and oxhydryl alteration anomalies more effectively, such as data from Landsat 8 OLI (OLI: multispectral sensor) and ASTER, among others. Combining multispectral data with hyperspectral data can provide more accurate alteration mineral identification [33][34][35][36]. Previous studies have shown that the extraction of iron-stained information usually base on the selection of only four bands for principal component analysis (PCA), which cannot fully reflect the variable features in the wave spectrum of iron-stained alteration minerals in detail [37][38][39].…”

Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging

“…In HSI, the data from the image are not only processed in the three visible colors but in the whole electromagnetic spectrum. HSI has been previously employed in agriculture [ 18 ], astronomy [ 19 ], military [ 20 ], biosensors [ 21 , 22 , 23 ], air pollution detection [ 24 , 25 ], remote sensing [ 26 ], dental imaging [ 27 ], environment monitoring [ 28 ], satellite photography [ 29 ], cancer detection [ 30 , 31 , 32 ], forestry monitoring [ 33 ], security [ 34 ], food security [ 35 ], natural resources surveying [ 36 ], vegetation observation [ 37 ], and geological mapping [ 38 ]. DR (DR) is one of the major complications of microvascular injury [ 39 ].…”