GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data

Setyadi, Harman; Widodo, Lilik Eko; Notosiswoyo, Sudarto; Saptawati, Putri; Ismanto, Arief; Hardjana, Iip

doi:10.1063/1.4941643

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Geo Science information which commonly consist of geological, geographical, geophysical and geochemical information, are gathered routinely from satellite and airborne sensors in a digital form. Large sets of ground survey data are also stored in digital form as argued by [13,14]. Accordingly, integration of field survey data, maps, and other information for the purpose of mineral exploration and resource estimation is a very time consuming task.…”

Section: Importance Of Gis In Mining Explorationmentioning

confidence: 99%

GIS and Database Management for Mining Exploration

Wambo¹,

Quinter²

2023

GIS and Spatial Analysis

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The mining sector has several tools which permits mining exploration works to be done in the most efficient and time conscious way as possible. This chapter is focus on Geographic Information Systems (GIS) and Database and aims to show how this combined approach can help in mineral exploration. It is all about building a database and GIS system capable first of all to assess if a geological/geographical data taken from the field is valid or wrong and to effectively manage mining data in a GIS designed to positively influence any mining exploration project from its earliest stages to the end of the project. We focus on Meiganga area where we have been involved.

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Section: Importance Of Gis In Mining Explorationmentioning

confidence: 99%

GIS and Database Management for Mining Exploration

Wambo¹,

Quinter²

2023

GIS and Spatial Analysis

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“…Fuzzy logic or ANN based models for mineral resource estimation using geological, geophysical, geochemical, mineral occurrence, magnetic, Geo-informatics and satellite data was studied by [76][77][78][79][80][81][82][83][84][85][86].…”

Section: Mineral Explorationmentioning

confidence: 99%

Applications, Promises and Challenges of Artificial Intelligence in Mining Industry: A Review

Ghosh¹

2022

Preprint

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<p>To keep up with the new technology modernization and the profit in shake of investors and stakeholders and importantly for the nation, and to ensure health and safety mining industry needs to approve new-age autonomous technologies and intelligent system in their field. Integration of Artificial Intelligence, Machine Learning, Internet of Things (IoT) and Automation are the keys to the 4th revolution in mining industry. This paper presents the overview of recent research upon artificial intelligence enhanced techniques and possibilities in mining operations and mining related domains. There is also a brief about the recent autonomous techniques and equipment in mining industry. Implementations and possibilities of artificial intelligence in safety and accident analysis of mining operations are sincerely detailed. Computer vision and spatial image analysis is also discussed as the recent advancement of deep learning and pattern recognition. Other mining related implementations of intelligent systems includes fragment analysis of ores, intelligent ventilation, on-site mineral processing simplification, digital twinning, mineral exploration, mineral price forecasting, mining equipment selection, post-mining land reclamation and scheduling. This paper also notes the detailed obstacles for implementing intelligent systems in mining industry.</p>

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“…Mineral exploration remains an integral part of geological investigations as it attempts to unravel the most favourable zones for possible occurrence of economic deposits. In many cases, several methods consisting of geochemistry, mineralogy, petrology, geophysics, and remote sensing/Geographic Information System (GIS) are employed for feasibility assessment and target delineation of mineral resources across a wide range of geological environments [1,2,3,4,5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

A spatial reconnaissance survey for gold exploration in a schist belt

Tende

Aminu

Amuda

et al. 2021

Heliyon

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Geological data integration and spatial analysis for structural elucidation are more assertive approaches for reconnaissance scale mineral exploration. In this study, several methods involving Fry analysis, distance correlation analysis, prediction area plots as well as knowledge driven predictive models including TOPSIS, ARAS and MOORA were systematically employed for unravelling the spatial geological attributes related to gold mineralisation. Additionally, statistical validation of knowledge driven predictive models were implemented using the Receiver Operating Characteristic/Area Under Curve analysis (ROC/AUC). The evidence from Fry and distance correlation analysis suggests that gold occurrence within parts of the Malumfashi schist belt of Nigeria is defined by a strong spatial association with the ENE-WSW as well as the NNE-SSW trending structures. The prediction area plot also revealed a robust spatial correlation between mineral occurrence and spatial data related to geological structures. The application of knowledge driven predictive models suggest a high favourability for gold occurrence within the southern, central, and north-eastern parts of the study location, while statistical validation using the ROC/AUC curves suggest a high prediction accuracy greater than 70% for all models. The geospatial analysis for mineral exploration within the Malumfashi area has unveiled an invaluable geological criterion for gold targeting with a considerable level of certainty.

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GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data

Cited by 5 publications

References 4 publications

GIS and Database Management for Mining Exploration

GIS and Database Management for Mining Exploration

Applications, Promises and Challenges of Artificial Intelligence in Mining Industry: A Review

A spatial reconnaissance survey for gold exploration in a schist belt

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