Publication Name: AEGC 2021
Authors: Minsu Kwon
Date Published: September 2021
Number of Pages: 3
Abstract:
Thanks to the brilliant progress in machine learning, many research works have conducted data-driven mineral prospectivity mapping. However, it is challenging to integrate highly multidisciplinary geoscientific data with machine learning algorithms. Especially, geological data are heterogeneous and non-numerical even though they are crucial for mineral exploration. In this work, we introduce how to preprocess the geoscientific data and design a machine learning model based on knowledge to make the best use of both geoscientific information and the advantages of machine learning.