Application of Convolutional Neural Networks (CNNs) for Enhanced Detection of Hydrothermal Alteration in Remote Sensing (RS) Imagery

Detecting hydrothermal alteration zones is crucial in geological exploration, as these zones often signal the presence of valuable mineral resources. Traditionally, identifying hydrothermal alterations has relied on field studies and remote sensing methods, which depend heavily on manual interpretation and statistical analysis. However, recent progress in deep learning, particularly with Convolutional Neural Networks (CNNs), has shown significant potential for increasing both the accuracy and efficiency of alteration detection in remote sensing imagery. CNNs, a deep learning model class designed for image analysis and spatial pattern recognition, have an architecture that enables automatic feature learning and hierarchical extraction from complex data. This capability makes CNNs highly effective for processing remote sensing images, allowing researchers to identify subtle spectral and spatial patterns linked to hydrothermal alterations-patterns that traditional methods often struggle to detect. By integrating CNNs into remote sensing workflows, challenges in conventional exploration techniques, such as high costs, extensive fieldwork, and the limitations of manual interpretation, are addressed. CNNs reduce exploration costs and risks by automating the detection process, leading to faster analysis and greater reliability in identifying prospective mineral zones. Studies have demonstrated CNNs' effectiveness in geological applications, with research on hyperspectral imagery achieving classification accuracies above ۹۰% in detecting hydrothermal alterations. These advances underscore CNNs' ability to extract deeper spectral and spatial features, strengthening the accuracy and consistency of geological analysis across expansive regions. Despite these advantages, CNN applications face challenges, particularly regarding the need for large, labeled datasets, which can be a barrier in fields with limited labeled data. Moreover, the 'black box' nature of CNNs raises concerns about interpretability, a crucial factor in building trust in automated geological assessment systems. Ensuring model predictions are interpretable is essential for sound decision-making in resource exploration. In summary, CNN application in remote sensing marks a transformative shift in geological exploration. By automating hydrothermal alteration detection and enhancing mineral assessment accuracy, CNNs are poised to revolutionize the field. Continued exploration into hyperparameter optimization and interpretable models promises to enhance CNN effectiveness, paving the way for advanced solutions in resource exploration and environmental monitoring.

کلیدواژه ها:

Hydrothermal Alteration ، Convolutional Neural Networks (CNNs) ، Remote Sensing (RS) ، Mineral Exploration ، Deep Learning (DL)