Deep learning and fractal-wavelet techniques for magnetite-apatite exploration in Tarom Iran.

Mohammad Mahdi Pourgholam, Ahmad Adib, Peyman Afzal, Kambiz Rahbar, Mehran Gholinejad

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Mohammad Mahdi PourgholamDepartment of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Ahmad AdibDepartment of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran. adib@azad.ac.ir.

Peyman AfzalDepartment of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Kambiz RahbarDepartment of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Mehran GholinejadDepartment of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

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No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study proposes an approach to detect iron-apatite mineralization and paragenesis related to Iron deposits by applying an image fusion based on deep learning (FDL) on exploration datasets. The exploration dataset of the study area includes remote-sensing, airborne geophysics, stream sediment geochemistry, and the 1:100,000 geological map of the Tarom area (Iran). The dataset used as a layer or sublayer in the Geographic Information System (GIS) program and exploration model includes features based on the presented Prospectivity Model (PM) of Iron-Apatite. The Prospectivity model was based on logical relations between each layer or sublayer extracted features and classes. The thresholds were performed on the layers and sublayers based on fractal-wavelet algorithms (DWT, W-N, SA) using the fractal wavelet classifier tool (FWC). To indicate the potential regions as Mineral Prospectivity Map (MPM), the results of each layer are integrated based on exploration model relations in a raster format by pixel values in application of fusion-based deep Learning (FDL) algorithm using pre-trained networks.

Indexed as

Exploration featuresFractal wavelet classifierFusion based on deep learningIron-Apatite anomaliesMineral prospectivity mapsProspectivity model

Identifiers

PMID40883398

DOI10.1038/s41598-025-16040-2

PMCPMC12397438

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