Addressing Geological Challenges in Mineral Resource Estimation: A Comparative Study of Deep Learning and Traditional Techniques (original) (raw)

Geological Analysis and Machine Learning for deposits prediction

Andrey Chitalin

View PDFchevron_right

Comparing the Performance of Different Neural Networks Architectures for the Prediction of Mineral Prospectivity

Piyasak Jeatrakul

2005 International Conference on Machine Learning and Cybernetics, 2005

View PDFchevron_right

Machine Learning Algorithms and Their Application to Ore Reserve Estimation of Sparse and Imprecise Data

Rajive Ganguli

Journal of Intelligent Learning Systems and Applications, 2010

View PDFchevron_right

Lithology prediction in the subsurface by artificial neural networks on well and 3D seismic data in clastic sediments: a stochastic approach to a deterministic method

Iva Kolenković Močilac

GEM - International Journal on Geomathematics, 2020

View PDFchevron_right

Geological modeling using a recursive convolutional neural networks approach

sebastian avalos

arXiv: Image and Video Processing, 2019

View PDFchevron_right

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

Simon Michaux

Minerals

View PDFchevron_right

Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation: A case study

Abu Bakarr Jalloh

International Journal of Mining Science and Technology, 2016

View PDFchevron_right

Novel machine learning workflow for rock property prediction in the geologically complex presalt Santos basin, Brazil

Jaydip Guha

First International Meeting for Applied Geoscience & Energy Expanded Abstracts, 2021

View PDFchevron_right

Refining our understanding of the subsurface geology using deep learning techniques

salma alsinan

Second International Meeting for Applied Geoscience & Energy

View PDFchevron_right

A review of machine learning in processing remote sensing data for mineral exploration

hodjat shirmard

2021

View PDFchevron_right

Neural networks for geophysicists and their application to seismic data interpretation

Bas Peters

The Leading Edge, 2019

View PDFchevron_right

Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information

Matthew Cracknell

Computers & Geosciences, 2014

View PDFchevron_right

Neural networks in petroleum geology as interpretation tools

Josipa Velić

Central European Geology, 2010

View PDFchevron_right

Application of artificial neural networks for lithofacies determination based on limited well data

Bruno Saftic

Central European Geology

View PDFchevron_right

Integration of a neural ore grade estimation tool in a 3D resource modeling package

Bryan Denby, I. Kapageridis

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999

View PDFchevron_right

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Anbazhagan Panjamani

Geotechnical and Geological Engineering, 2008

View PDFchevron_right

Direct Estimation of Porosity from Seismic Data using Rock and Wave Physics Informed Neural Networks (RW-PINN)

Divakar Vashisth

Cornell University - arXiv, 2022

View PDFchevron_right

Comparative Evaluation of Neural Network Learning Algorithms for Ore Grade Estimation

Biswajit Samanta

Mathematical Geology, 2006

View PDFchevron_right

Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India

Alok Porwal

Nonrenewable Resources, 2003

View PDFchevron_right

Integrating deep learning insights for complex geological features: A case study from the Santos Basin, offshore Brazil

Ana Krueger

First International Meeting for Applied Geoscience & Energy Expanded Abstracts, 2021

View PDFchevron_right

Application of Machine learning and Artificial Intelligence in Environmental and Engineering Geology

adeola Akinrinmade

Application of Machine learning and Artificial Intelligence in Environmental and Engineering Geology, 2023

View PDFchevron_right

Recent Application of Machine Learning Algorithms in Petroleum Geology: A brief review

Blessing Elosionu

View PDFchevron_right

Lithological facies classification using deep convolutional neural network

Yadigar Imamverdiyev

Journal of Petroleum Science and Engineering, 2018

View PDFchevron_right

Generalized regression and feed-forward back propagation neural networks in modelling porosity from geophysical well logs

Nasir Khan

View PDFchevron_right

Machine Learning Based Systems Application to Mineral Resource Estimation and Compliance with Reporting Codes for Mineral Resources

I. Kapageridis

International Conference on Raw Materials and Circular Economy – RawMat2021, 2021

View PDFchevron_right

Comparison of backpropagation neural networks and statistical techniques for analysis of geological features in Landsat imagery

Petros Podaras

1991

View PDFchevron_right

Mineral Rock Classification Using Convolutional Neural Network

jahnavi behara

Recent Trends in Intensive Computing

View PDFchevron_right

Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information

Marco Ceia

Journal of Applied Geophysics, 2015

View PDFchevron_right