Nagpal, Gaurav, Nagpal, Ankita, Jasti, Naga Vamsi Krishna and Inani, Sarveshwar Kumar (2024) Use of neural networks for Ore and Waste classification of mining deposits based on geophysical data. In: ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence, 23 November 2023, Jaipur, India.
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Abstract
In mining industry, the ore is mined & treated for extraction of metal from ore. So, ore and waste separation is very crucial for mining and to ensure a grade of ore that could be converted into metal. Prior to mining, exploration is required to demarcate the ore body for mining and characterize the waste for better planning. This paper tends to classify the ore and waste (Zinc, Lead type of deposit) that is very complex in nature from point of ore body configuration and continuity for mining. Since the existing literature has not used the neural networks to determine the prospects of an area for these ores, this study has made an attempt at classifying these deposits as ore or waste using neural networks.
Item Type: | Conference or Workshop Item (Paper) |
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Keywords: | Ore deposits | Geophysical data | Neural-networks | Ore classification | Orebodies | Waste classification | Waste separation |
Subjects: | Physical, Life and Health Sciences > Engineering and Technology Physical, Life and Health Sciences > Environmental Science, Policy and Law Social Sciences and humanities > Social Sciences > Social Sciences (General) |
JGU School/Centre: | Jindal Global Business School |
Depositing User: | Subhajit Bhattacharjee |
Date Deposited: | 01 Jun 2024 16:25 |
Last Modified: | 08 Sep 2024 17:13 |
Official URL: | https://doi.org/10.1145/3647444.3647920 |
URI: | https://pure.jgu.edu.in/id/eprint/7867 |
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