Journal of Geology & Geophysics
Open Access

Geological Mapping with Convolution Neural Network Using Statistical Data: A Powerful Tool for Unraveling the Earth's Surface

Mohamed El Tokhi^{* *}Correspondence: Mohamed El Tokhi, Department of Petrology and Geochemistry, UAE University, College of Science, Abu Dhabi, UAE, Email:

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Description

Geological mapping is an important tool for understanding the complex geological history of the Earth's surface. It involves the creation of detailed maps that identify and describe the different types of rocks, sediments, and other geologic features present in an area. However, geological mapping is a time-consuming and labor-intensive process that requires extensive field observations and data analysis. Convolutional Neural Networks (CNN) offer a promising solution to these challenges by enabling the automatic extraction of geologic features from satellite images using statistical data. In this article, we explore the use of CNNs in geological mapping and how they can be used to unlock the secrets of the Earth's surface.

A Convolutional Neural Network (CNN) is a type of artificial neural network used in image processing and pattern recognition. CNNs consist of multiple layers of interconnected nodes, each of which performs a specific mathematical operation on the input data. The output of each layer is then fed into the next layer, where further processing occurs.

CNNs are trained using a large dataset of labeled images, allowing them to learn patterns and features within the images. This learning process enables the CNN to accurately classify and recognize new images that it has not encountered before.

CNNs have the potential to revolutionize geological mapping by enabling the automatic extraction of geologic features from satellite images. By training a CNN on a dataset of labeled satellite images, the network can learn to identify and classify different types of rocks, sediments, and other geologic features.

The use of statistical data is crucial in this process, as it enables the network to differentiate between different types of features based on their physical properties. For example, the network can be trained to recognize the spectral characteristics of certain minerals, allowing it to differentiate between different types of rock formations.

Applications of geological mapping with CNNs

The use of CNNs in geological mapping has many potential applications. For example, it can be used to identify and locate potential mineral deposits, enabling more efficient and targeted mineral exploration. CNNs can also be used to identify areas of high geological risk, such as those prone to landslides or earthquakes, allowing for more effective hazard mitigation strategies.

Furthermore, the use of CNNs in geological mapping can enable the creation of more accurate and detailed geological maps, improving our understanding of the Earth's surface and its history.

Limitations of CNNs in geological mapping

While CNNs offer a promising solution to the challenges of geological mapping, they are not without limitations. CNNs are highly dependent on the quality and quantity of the training data, and their accuracy can be affected by factors such as variations in lighting and weather conditions.

In addition, the interpretation of the results generated by the network requires significant expertise and experience in geology. While CNNs can assist in the identification and classification of geologic features, they cannot replace the expertise of geologists in the interpretation of geological data.

Geological mapping is an important tool for understanding the complex history of the Earth's surface. The use of Convolutional Neural Networks (CNNs) in geological mapping offers a promising solution to the challenges of this time-consuming and labor-intensive process. By leveraging statistical data and machine learning algorithms, CNNs can enable the automatic extraction of geologic features from satellite images, enabling more efficient and accurate geological mapping. While there are limitations to the use of CNNs in geological mapping, continued research and development in this area will undoubtedly lead to further advancements and discoveries in the field of geology.