ラドン追跡と人工ニューラルネットワークに基づく岩石破裂強度の予測のための統合アプローチ
Wenhao Wang1, Shengyang Feng1, Xiaodong Wang2
1School of Resource Environment and Safety Engineering, University of South China, Hengyang, 421001, Hunan, China.
Journal of environmental radioactivity
|August 21, 2025
まとめ
岩石の断層の強さを予測することは 資源の採掘に不可欠です この研究では,人工ニューラルネットワーク (ANN) とラドン追跡法が導入され,地質学的アプリケーションの断裂強度の推定で90.7%の精度を達成しました.
科学分野:
- 地質学
- 地理学
- 人工知能
背景:
- 資源 (石油,ガス,地熱) の探査には,岩石破裂の強度の正確な決定が不可欠です.
- 岩石の断層を直接測定することは技術的に困難であり,正確な強度評価を妨げています.
研究 の 目的:
- 岩石破裂の強度を予測するための統合的アプローチを開発する.
- 人工ニューラルネットワーク (ANN) とラドン追跡を組み合わせて,予測の精度を向上させる.
主な方法:
- 断裂した岩層におけるラドン排出速度をシミュレートするためのラドン移動モデルを確立した.
- ディスクリート・フラクチャー・ネットワーク (DFN) モデルを用いて生成された岩石の断層.
- 数字でシミュレートされた900のデータセットで ANNを訓練した.
主要な成果:
- 判定係数0.907で高い予測精度を達成した.
- ANNモデルの精度にとって重要な要因として隠された層とニューロンの数を特定しました.
- アウトスクロップの断裂強度を予測してモデルを検証し,測定値と密接な一致 (7.5%の差) を示した.
結論:
- 統合されたANNとラドン追跡アプローチは,岩石破裂の強度を予測するための信頼できる方法を提供します.
- この技術は,地質学と工学の実践における直接的な断層測定の限界を克服するための実行可能な解決策を提供します.
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