Abbassi, Bahman, and Li-Zhen Cheng. 2021. "3D Geophysical Post-Inversion Feature Extraction for Mineral Exploration through Fast-ICA" Minerals 11, no. 9: 959. https://doi.org/10.3390/min11090959
Abbassi, Bahman, and Li-Zhen Cheng. 2021. "3D Geophysical Post-Inversion Feature Extraction for Mineral Exploration through Fast-ICA" Minerals 11, no. 9: 959. https://doi.org/10.3390/min11090959
Abbassi, Bahman, and Li-Zhen Cheng. 2021. "3D Geophysical Post-Inversion Feature Extraction for Mineral Exploration through Fast-ICA" Minerals 11, no. 9: 959. https://doi.org/10.3390/min11090959
Abbassi, Bahman, and Li-Zhen Cheng. 2021. "3D Geophysical Post-Inversion Feature Extraction for Mineral Exploration through Fast-ICA" Minerals 11, no. 9: 959. https://doi.org/10.3390/min11090959
Abstract
A major problem in the post-inversion geophysical interpretation is the extraction of geological information from inverted physical property models, which do not necessarily represent all underlying geological features. No matter how accurate the inversions are, each inverted physical property model is sensitive to limited aspects of subsurface geology and is insensitive to other geological features that are otherwise detectable with complementary physical property models. Therefore, specific parts of the geological model can be reconstructed from different physical property models. To show how this reconstruction works, we simulated a complex geological system that comprises an original layered earth model that has passed several geological deformations and alteration overprints. Linear combination of complex geological features comprised three physical property distributions: Electrical resistivity, induced polarization chargeability, and magnetic susceptibility models. This study proposes a multivariate feature extraction approach to extract information about the underlying geological features comprising the bulk physical properties. We evaluated our method in numerical simulations and compared three feature extraction algorithms to see the tolerance of each method to the geological artifacts and noises. We show that the fast-independent component analysis (fast-ICA) algorithm by negentropy maximization is a robust method in the geological feature extraction that can handle the added unknown geological noises. The post-inversion physical properties are also used to reconstruct the underlying geological sources. We show that the sharpness of the inverted images is an important constraint on the feature extraction process. Our method successfully separates geological features in multiple 3D physical property models. This methodology is reproducible for any number of lithologies and physical property combinations and can recover the latent geological features, including the background geological patterns from overprints of chemical alteration.
Keywords
Feature extraction; independent component analysis; 3D inversion; physical properties
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
