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Learning 3D Mineral Prospectivity from 3D Geological Models Using Convolutional Neural Networks: Application to a Structure-controlled Hydrothermal Gold Deposit. (arXiv:2109.00756v2 [physics.geo-ph] UPDATED)
cs.CV updates on arXiv.org arxiv.org
The three-dimensional (3D) geological models are the typical and key data
source in the 3D mineral prospecitivity modeling. Identifying
prospectivity-informative predictor variables from the 3D geological models is
a challenging and tedious task. Motivated by the ability of convolutional
neural networks (CNNs) to learn the intrinsic features, in this paper, we
present a novel method that leverages CNNs to learn 3D mineral prospectivity
from the 3D geological models. By exploiting the learning ability of CNNs, the
presented method allows for …
application arxiv convolutional neural networks learning networks neural networks physics