Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

Abstract

Abstract:The mineral ore potential of many mountainous regions of the world, like the Kurdistan region of Iraq, remains unexplored. For logistical and sometimes political reasons, these areas are difficult to map using traditional methods. We highlight the improvement in remote sensing geological mapping that arises from the integration of geomorphic features in classifications. The Mawat Ophiolite Complex (MOC) is located in the NE of Iraq and is known for its mineral deposits. The aims of this study are: (I) to refine the existing lithological map of the MOC; (II) to identify the best discriminatory datasets for lithological classification, including geomorphic features and textures; and (III) to identify potential locations with high concentrations of chromite. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The updated map allowed the identification of a new mafic body and a substantial improvement of the geometry of the known lithological units. The use of geomorphic features allowed for the increase of the overall accuracy from 73% to 79.3%. In addition, we detected chromite occurrences within the ophiolite by applying Spectral Angle Mapping (SAM) technique. We identified two new locations having high concentrations of chromite and verified one of these promising areas and has coarsely crystalline chromite within dunite host rock. The chromium (Cr 2 O 3 ) concentration is ~8.46%. The SAM and SVM methods applied on ASTER satellite data show that these can be used as a powerful tool to explore ore deposits and to further improve lithological mapping in mountainous semi-arid regions.

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

“…For the classification obtained from the original dataset, the overall accuracies range between 76% and 95%, and half of these were extracted from hyperspectral imagery. The overall accuracy obtained from S2A_AST_DEM is 77.83%, lower than those from [66] and [51]. Nevertheless, the number of lithological classes in this study is fifteen, which is more than the classes from [66] (seven classes) and [51] (nine classes).…”

“…In this study, the one against one support vector machine (OAO-SVM) classifier accomplished in C++ was employed for lithological mapping. The radial basis function was selected as the kernel type, the penalty parameter was set to 100, and the gamma in kernel function was the inverse of the band number of the S2A_DEM dataset, namely, 0.091 [51].…”

Remote Sensing Exploration of Nb-Ta-LREE-Enriched Carbonatite (Epembe/Namibia)

“…The recognition of small-scale features, however, is very important for REE exploration, as enrichment often occurs within small dykes or distinct enrichment zones [20]. To tackle these problems, we use an approach that combines these spectral data with morphometric indices, which allow one to differentiate lithologies based on their distinct topographic signature (e.g., Othman and Gloaguen [21]). …”

Multisensoral Topsoil Mapping in the Semiarid Lake Manyara Region, Northern Tanzania

“…Remote sensing studies make use of SVM properties, like high computation performance and high classification accuracies with small numbers of training samples [89]. Recent studies used SVM approaches to identify lithological units with remote sensing data [90,91].…”