An enhanced strategy for geo-exploratory data analysis to facilitate the discovery of new mineral deposits

Wang, Wenlei; Liu, Zhibo; Tang, Jie; Yuan, Changjiang

doi:10.1016/j.gexplo.2024.107411

Cited by 3 publications

(1 citation statement)

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“…Geological mapping is of crucial importance in geological research, constituting an essential pillar for guiding mineral exploration activities 1 – 13 . Remote sensing is considered to be the most effective and useful technique for identifying lithological units.…”

Section: Introductionmentioning

confidence: 99%

An integrated remote sensing, petrology, and field geology analyses for Neoproterozoic basement rocks in some parts of the southern Egyptian-Nubian Shield

El-Desoky,

Bachri,

El Mezayen

et al. 2024

Sci Rep

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The main objective of this study was to use deep learning, and convolutional neural networks (CNN), integrated with field geology to identify distinct lithological units. The Samadia-Tunduba region of the South Eastern Desert of Egypt was mapped geologically for the first time thanks to the use of processed developed CNN algorithms using Landsat 9 OLI-2, which were further enhanced by geological fieldwork, spectral measurements of field samples, and petrographic examination. According to previously published papers, a significant difference was observed in the distribution of rocks and their boundaries, as well as the previously published geological maps that were not accurately compatible with the nature of the area. The many lithologic units in the region are refined using principal component analysis, color ratio composites, and false-color composites. These techniques demonstrated the ability to distinguish between various igneous and metamorphic rock types, especially metavolcanics, metasediments, granodiorite, and biotite monzogranite. The Key structural trends, lithological units, and wadis affecting the area under study are improved by the principal component analysis approach (PC 3, 2, 1), (PC 2, 3, 4), (PC 4, 3, 2), (PC 5, 4, 3), and (PC 6, 5, 4) in RGB, respectively. The best band ratios recorded in the area are recorded the good discrimination (6/5, 4/3, and 2/1), (4/2, 6/7, and 5/6), and (3/2, 5/6, and 4/6) for RGB. The classification map achieved an overall accuracy of 95.27%, and these results from Landsat-9 data were validated by field geology and petrographical studies. The results of this survey can make a significant difference to detailed geological studies. A detailed map of the new district has been prepared through a combination of deep learning and fieldwork.

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Section: Introductionmentioning

confidence: 99%