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Cu-bearing signatures from multi-element geochemical data, a correct strategy to implement a Convolutional Autoencoder Algorithm | ||
| International Journal of Mining and Geo-Engineering | ||
| مقاله 1، دوره 59، شماره 1، خرداد 2025، صفحه 1-10 اصل مقاله (1.08 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijmge.2024.381037.595190 | ||
| نویسندگان | ||
| Mobin Saremi1؛ Seyyed Ataollah Agha Seyyed Mirzabozorg2؛ Abbas Maghsoudi3؛ Maysam Abedi* 2؛ Ramin DehghanNiri2 | ||
| 1Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran. | ||
| 2School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran. | ||
| 3Department of Mining and Metallurgy, Amirkabir University of Technology, Tehran, Iran. | ||
| چکیده | ||
| Recent advancements in autoencoders and their variants have notably enhanced the detection of multi-element geochemical signatures linked to ore occurences. This research employed a convolutional autoencoder algorithm (CAE) to identify geochemical anomalies, leveraging the algorithm’s ability into account the spatial correlation within the geochemical dataset. In this framework, two stream sediment datasets were generated in the Feizabad district using a conceptual modelling approach alongside a big data analysis strategy. These datasets were individually fed into the CAE model to identify multi-element geochemical anomalies based on the reconstruction error in an unsupervised manner. A comparative analysis of two geochemical prospectivity models and the simplified geological map of Feizabad demonstrates a strong spatial correlation between the identified anomaly regions and known mineral occurrences, which are distributed across andesite, tuff, and Eocene-Oligocene intrusive rocks. However, a quantitative assessment using prediction-area plots indicates that the multi-element geochemical map derived from the conceptual model exhibits a higher prediction rate (72%) compared to the geochemical prospectivity map generated through the big data approach (63%). | ||
| کلیدواژهها | ||
| Stream sediment geochemistry؛ Deep learning؛ Big data analytics؛ Unsupervised anomaly detection؛ Feizabad district | ||
| مراجع | ||
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