3D Geophysical Predictive Modeling by Spectral Feature Subset Selection in Mineral Exploration

Abbassi, Bahman ORCID: https://orcid.org/0000-0002-5768-009X, Cheng, Li-Zhen, Jebrak, Michel ORCID: https://orcid.org/0000-0002-0473-9924 et Lemire, Daniel ORCID: https://orcid.org/0000-0003-3306-6922 (2022). 3D Geophysical Predictive Modeling by Spectral Feature Subset Selection in Mineral Exploration. Minerals , 12 (10). p. 1296. doi:10.3390/min12101296 Repéré dans Depositum à https://depositum.uqat.ca/id/eprint/1376

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Résumé

"Several technical challenges are related to data collection, inverse modeling, model fusion, and integrated interpretations in the exploration of geophysics. A fundamental problem in integrated geophysical interpretation is the proper geological understanding of multiple inverted physical property images. Tackling this problem requires high-dimensional techniques for extracting geological information from modeled physical property images. In this study, we developed a 3D statistical tool to extract geological features from inverted physical property models based on a synergy between independent component analysis and continuous wavelet transform. An automated interpretation of multiple 3D geophysical images is also presented through a hybrid spectral feature subset selection (SFSS) algorithm based on a generalized supervised neural network algorithm to rebuild limited geological targets from 3D geophysical images. Our self-proposed algorithm is tested on an Au/Ag epithermal system in British Columbia (Canada), where layered volcano-sedimentary sequences, particularly felsic volcanic rocks, are associated with mineralization. Geophysical images of the epithermal system were obtained from 3D cooperative inversion of aeromagnetic, direct current resistivity, and induced polarization data sets. The recovered cooperative susceptibilities allowed locating a magnetite destructive zone associated with porphyritic intrusions and felsic volcanoes (Au host rocks). The practical implementation of the SFSS algorithm in the study area shows that the proposed spectral learning scheme can efficiently learn the lithotypes and Au grade patterns and makes predictions based on 3D physical property inputs. The SFSS also minimizes the number of extracted spectral features and tries to pick the best representative features for each target learning case. This approach allows interpreters to understand the relevant and irrelevant spectral features in addition to the 3D predictive models. Compared to conventional 3D interpolation methods, the 3D lithology and Au grade models recovered with SFSS add predictive value to the geological understanding of the deposit in places without access to prior geological and borehole information."

Type de document:	Article
Informations complémentaires:	La version officielle de cette publication a été publiée dans la revue Minerals en 2022 : https://doi.org/10.3390/min12101296 // La bibliothèque du Cégep de l’Abitibi-Témiscamingue et de l’Université du Québec en Abitibi-Témiscamingue (UQAT) a obtenu l’autorisation de l’auteur de ce document afin de diffuser, dans un but non lucratif, une copie de son oeuvre dans Depositum, site d’archives numériques, gratuit et accessible à tous. L’auteur conserve néanmoins ses droits de propriété intellectuelle, dont son droit d’auteur, sur cette oeuvre. // The library of the Cégep de l’Abitibi-Témiscamingue and the Université du Québec en Abitibi-Témiscamingue (UQAT) obtained the permission of the author to use a copy of this document for nonprofit purposes in order to put it in the open archives Depositum, which is free and accessible to all. The author retains ownership of the copyright on this document.
Mots-clés libres:	independent component analysis; 3D modeling; spectral; feature subset selection; Genetic algorithm; Neural networks; Fuzzy-logic; Nsga-II; Dimensionality; Deposit; Curse
Divisions:	Mines et eaux souterraines
Date de dépôt:	25 nov. 2022 16:32
Dernière modification:	25 nov. 2022 16:32
URI:	https://depositum.uqat.ca/id/eprint/1376

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