A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data

A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data

Abstract This study presents a comprehensive approach for constructing a 3D Apparent Geological Model (AGM) by integrating multi-resistivity data using statistical methods, supervised machine learning (SML), and Python-based modeling techniques. Demonstrated through a case study in the Choushui Rive...

Full description

Saved in:
Bibliographic Details
Main Authors: Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie
Format: Article
Language:English
Published: SpringerOpen 2024-11-01
Series:Geoscience Letters
Subjects:
3D apparent geological model
3D resistivity model
Supervised machine learning
Python-based modeling
Choushui river alluvial fan
Online Access:https://doi.org/10.1186/s40562-024-00368-0
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1186/s40562-024-00368-0

Similar Items