Nagpal, Gaurav, Ramesh, Singh Shrikant, Krishna Jasti, Naga Vamsi, Nagpal, Ankita and Sharma, Gunjan Mohan (2023) Ore Grade Estimation in Mining Industry from petro-physical data using neural networks. In: ICIMMI '22: Proceedings of the 4th International Conference on Information Management & Machine Intelligence, 23 - 24 December 2022, Jaipur,Rajasthan.
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Abstract
The grade of the ore in mining industry plays a very important role. From the petro-physical data, the grade of the ore can be predicted with reasonable accuracy. However, the existing literature is silent on the techniques of data analytics that can be used for ore-grade estimation with the help of data. The study uses multi-layer neural network perceptron model and neural network regression models for predicting the grade on the basis of Petro-physical data that was collected by doing borehole geophysical survey capturing twenty-one properties of the ore. The research study is able to estimate the grade of the ore with reasonable accuracy using the data.
Item Type: | Conference or Workshop Item (Paper) |
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Keywords: | Classification | Confusion Matrix | Mining Industry | Multi-Layer Perceptron Model | Neural Regression | Petro-Physical Data |
Subjects: | Social Sciences and humanities > Social Sciences > Social Sciences (General) |
JGU School/Centre: | Jindal Global Business School |
Depositing User: | Amees Mohammad |
Date Deposited: | 28 Jun 2023 03:03 |
Last Modified: | 28 Jun 2023 03:03 |
Official URL: | https://doi.org/10.1145/3590837.3590954 |
URI: | https://pure.jgu.edu.in/id/eprint/6250 |
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