MANUSCRIPT Mapping Rare Earth Element Prospectivity in the Ruri Carbonatite Complex Using Explainable Ensemble Learning
説明可能なアンサンブル学習を用いたルリ炭酸塩岩コンプレックスにおける希土類元素有望性マッピング (AI 翻訳)
Inyangala¹ A, Waswa¹ AK, Angeyo² HK
🤖 gxceed AI 要約
日本語
再生可能エネルギー技術やEVに不可欠な希土類元素（REE）の探査を目的に、ケニアのルリ炭酸塩岩コンプレックスで説明可能なスタック型アンサンブル機械学習を開発。地質・地球化学・放射能データを統合し、SVMとRFをベース学習器、XGBoostをメタ学習器とするモデルで95.6%の精度とROC-AUC 0.97を達成。SHAP分析によりTREOやLREE/HREE比などが重要予測因子と特定され、有望地域をマッピングした。
English
This study develops an explainable stacked ensemble machine learning framework for rare earth element (REE) prospectivity mapping in the Ruri Carbonatite Complex, Kenya. Combining geological, geochemical, and radiometric data, the model achieves 95.6% accuracy and ROC-AUC 0.97. SHAP analysis identifies key predictors such as TREO and LREE/HREE fractionation, producing interpretable prospectivity maps for REE exploration.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本はREEの安定供給確保が重要課題であり、本手法は海外鉱物探査におけるAI活用の好事例。国内の資源戦略やJOGMEC等の探査活動に応用可能で、GX関連鉱物のサプライチェーン強化に寄与する。
In the global GX context
This paper contributes to global GX by addressing the critical mineral supply chain for renewable energy and EVs. The explainable ML approach offers a replicable method for REE exploration, supporting the transition to clean energy technologies worldwide.
👥 読者別の含意
🔬研究者:Provides a robust explainable ML framework for mineral prospectivity mapping, applicable to other critical minerals.
🏢実務担当者:Offers a data-driven tool for identifying REE targets, potentially reducing exploration costs and risks.
🏛政策担当者:Highlights the importance of AI in securing critical mineral supplies, informing resource security policies.
📄 Abstract（原文）
<title>Abstract</title> <p>The growing demand for renewable energy technologies, Electric Vehicles (Evs), and advanced electronics underscores the vital importance of rare earth elements (REEs) as critical minerals and its associated demand for effective, data-informed exploration strategies. This study aimed at developing an explainable stacked ensemble machine learning framework for the REE prospectivity mapping in Ruri Carbonatite Complex, southwest Kenya, which is underexplored. Geological, geochemical, radiometric and spatial datasets were combined to train a stacked ensemble model with Support Vector Machine (SVM) and Random Forest (RF) as base learners and Extreme Gradient Boosting (XGBoost) as a meta-learner. The performance of the model was tested by 5-fold stratified cross-validation and various classification measures such as accuracy, precision, recall, F1-score, Matthews Correlation Coefficient, Cohen's Kappa, ROC-AUC and PR-AUC. The highest performance was obtained by the ensemble model which had an accuracy of 95.6% and a ROC-AUC of 0.97. The most influential predictors identified by SHapley Additive exPlanations (SHAP) were: Total Rare Earth Oxides (TREO), LREE/HREE fractionation, thorium, uranium, radioactive enrichment indices and structural density. The map of prospectivity, produced using the explainable ensemble learning, identified several REE targets related to carbonatite intrusions, fenitized host rocks, and main structural corridors, highlighting the importance of explainable ensemble learning for REE exploration with reliable and interpretable outputs.</p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10351873/v1first seen 2026-08-14 04:41:17 · last seen 2026-08-19 04:20:08
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