Satellite-based remote sensing analysis for the exploration of MVT Pb-Zn mineralization using an integrated approach of minimum distance classification, deep autoencoder and fuzzy logic modeling

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-04-01 DOI:10.1016/j.rsase.2025.101561

Soran Qaderi , Abbas Maghsoudi , Amin Beiranvand Pour , Mahyar Yousefi

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引用次数: 0

Abstract

Mississippi Valley-type (MVT) Pb-Zn mineralization is a key economic resource, yet its exploration is challenging due to complex alteration patterns and high costs. This study integrates ASTER satellite imagery with deep learning to enhance prospectivity mapping. We applied image processing techniques, including Principal Component Analysis (PCA), Band Ratios (BR), Band Math (BM), and Spectral Angle Mapper (SAM), to identify alteration zones. The Minimum Distance Classification (MDC) method classified these zones, extracting key evidence layers. These layers—dolomitization (MDC-PCA, SAM) and carbonate-iron oxide (MDC-BR, MDC-BM)—were integrated using Deep Autoencoder (DAE) and Fuzzy Logic Modeling (GFO) to generate prospectivity maps. Prediction-area (P-A) plots showed the DAE model outperformed GFO, achieving a normalized density (N_d) of 4.1 compared to 3.61 for GFO, indicating a more precise delineation of high-potential mineralization zones. Field validation confirmed strong alignment with known Pb-Zn occurrences. This study highlights the effectiveness of remote sensing and deep learning in cost-effective mineral exploration and provides a scalable framework for similar metallogenic provinces.

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基于最小距离分类、深度自编码器和模糊逻辑建模综合方法的MVT铅锌矿化遥感勘探分析

密西西比河谷型铅锌矿化是一种重要的经济资源，但其蚀变模式复杂，成本高，勘探难度大。该研究将ASTER卫星图像与深度学习相结合，以增强勘探区测绘。应用主成分分析（PCA）、频带比（BR）、频带数学（BM）和光谱角映射器（SAM）等图像处理技术识别蚀变带。最小距离分类（MDC）方法对这些区域进行分类，提取关键证据层。这些层-白云化（MDC-PCA， SAM）和碳酸盐岩-氧化铁(MDC-BR, MDC-BM) -使用深度自编码器（DAE）和模糊逻辑建模（GFO）进行集成，以生成远景图。预测面积（P-A）图显示，DAE模型优于GFO模型，其归一化密度（Nd）为4.1，而GFO模型的归一化密度（Nd）为3.61，表明可以更精确地圈定高潜力矿化带。现场验证证实了与已知铅锌矿床的强对准。该研究突出了遥感和深度学习在具有成本效益的矿产勘探中的有效性，并为类似的成矿省份提供了可扩展的框架。

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems