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چکیده
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Supervised machine learning algorithms have shown enormous potential to predict mineral prospectivities and to identify mineral exploration targets within study areas. However, accurately selecting non-deposit points remains a critical challenge, as improper selection can mitigate the prediction rate and introduce systematic bias. This study describes the idea of leveraging and comparing deep autoencoder (DAE) network (as a first experiment) with expert knowledge (as a second experiment) to tackle the problem of non-deposit selection in predictive modeling of mineral prospectivity. For this, according to the conceptual model of porphyry copper deposits evidence layers of fault density, multi-element geochemical signatures, proximity to phyllic and argillic alterations, and proximity to intrusive rocks, were first generated to represent ore-forming subsystems. Within the first experiment, a DAE technique was used to integrate multiple exploration criteria whereby non-deposit locations within the recognized non-prospective regions were determined. Within the second experiment, expert opinions were set as criteria to define non-deposit locations. Both sets of non-deposit points were fed into a random forest (RF) algorithm, generating two prospectivity models. The effectiveness of these models was evaluated using the prediction-area (P-A) plot and the normalized density index (Nd). The Nd values for all models exceed one, indicating their effectiveness in integrating exploration evidence to delineate potential targets. However, the DAE-based experiment improved the prediction rate of RF and reduced systematic uncertainties. The proposed methodology was shown to be a robust approach to enhance the relevance of mineral prospectivity mapping, and it may possess the potential to predict new porphyry copper exploration targets in analogous mineral systems.
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