Journal of Geology & Geophysics
Open Access

Use of Artificial Neural Networks for Airborne Geophysics and Sparse Drillings in Mineral Exploration

Wael Abdel Kawy^{* *}Correspondence: Wael Abdel Kawy, Department of Soil Science, Cairo University, Giza, Egypt, Email:

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Description

Exploration for mineral resources is a critical aspect of the mining industry. Traditionally, exploration has relied on drilling, but the high costs and time-consuming process have led to the development of alternative methods such as airborne geophysics. Airborne geophysics is a method of surveying the earth's subsurface using aircraft to measure variations in the earth's magnetic and electromagnetic fields. This method provides a cost-effective and time-efficient way of mapping mineral resources, but it is limited by the quality of data acquired.

Sparse drilling is another exploration method that involves drilling at select locations to obtain geological information. This method is essential in validating and refining the results obtained from airborne geophysics. However, sparse drilling can be costly, and it is often challenging to determine the optimal locations for drilling.

Artificial Neural Networks (ANNs) have emerged as a valuable tool in mineral exploration, particularly in integrating data from various sources to make informed decisions. ANNs are a type of machine learning algorithm designed to model complex relationships between inputs and outputs. ANNs have been used in various aspects of mineral exploration, including resource estimation, geological mapping, and mineral prospectivity analysis.

The integration of ANNs into airborne geophysics and sparse drillings has the potential to improve mineral exploration by providing accurate and cost-effective ways of mapping mineral resources. By using ANNs, geologists can identify the optimal locations for sparse drilling and reduce the number of drill holes required to obtain reliable geological data. Additionally, ANNs can help to integrate data from various sources, such as airborne geophysics, drilling results, and geological maps, to provide a comprehensive understanding of the subsurface.

The use of ANNs in airborne geophysics involves training a neural network to predict the geological structure and mineralization of the subsurface based on the airborne geophysical data. The trained network can then be used to generate 3D models of the subsurface, which can aid in mineral resource exploration and resource estimation. The accuracy of the ANN model is dependent on the quality of the input data and the training process.

Sparse drillings can be optimized by using ANNs to identify the optimal locations for drilling. ANNs can analyze the data obtained from airborne geophysics, drilling results, and geological maps to determine the geological structure and potential mineralization of the subsurface. This information can then be used to identify areas where sparse drilling is most likely to be successful. By reducing the number of drill holes required, ANNs can significantly reduce exploration costs while still providing accurate geological data.

The integration of ANNs into mineral exploration also provides an opportunity for real-time decision-making. ANNs can be used to analyze data in real-time, allowing geologists to make informed decisions about exploration activities. For example, if an airborne survey identifies a potential mineral deposit, the ANN model can be used to determine the optimal locations for drilling, and the drilling can be conducted immediately. This real-time approach can significantly reduce exploration time and costs while still providing accurate geological data.

The integration of ANNs into airborne geophysics and sparse drillings has the potential to improve mineral exploration by providing accurate and cost-effective ways of mapping mineral resources. ANNs can help to optimize sparse drillings, reduce exploration costs, and provide real-time decision-making. However, the accuracy of the ANN model is dependent on the quality of input data and the training process. It is essential to ensure that the input data is accurate and representative of the subsurface, and the network is appropriately trained to provide accurate predictions. As the mining industry continues to face increasing exploration costs and environmental concerns, the integration of ANNs into mineral exploration is becoming more critical.