灰色の狼の最適化による改良された変数モード分解による地震ランダムノイズ分離と抑制
Zhenjing Yao1,2,3, Wenzhe Li1,2,3, Jingyi Zhu4
1College of Information and Control Engineering, Institute of Disaster Prevention, Sanhe, Hebei, China.
PloS one
|September 5, 2025
まとめ
この研究は,効果的な地震騒音抑制のためにグレー・ウルフ・アルゴリズム (GWO) で最適化された強化された変数モード分解 (VMD) 方法を導入します. GWO-VMDのアプローチは,重要な信号情報を保存しながらランダムなノイズを分離することにより,地震データの質を大幅に改善します.
科学分野:
- 地質学と地震信号処理
- 地球科学におけるコンピューティングインテリジェンス
背景:
- 地震騒音は記録された地震データの質を著しく低下させる.
- 効果的な地震騒音分離と抑制は,正確な地質学的な解釈に不可欠です.
- 既存の消音方法では,騒音削減と信号保存のバランスをとることがしばしば困難です.
研究 の 目的:
- 地震によるランダムな騒音抑制のための新しい方法を開発する.
- 信号と騒音の比率 (SNR) を改善することによって地震データ品質を向上させる.
- 有効な地震信号を保持しながら ランダムなノイズを効果的に分離する.
主な方法:
- 強化されたバリエーションモード分解 (VMD) による地震騒音抑制法を提案した.
- VMDパラメータ (Kとα) を最適化するための統合グレー・ウルフ・最適化 (GWO) アルゴリズム.
- GWOの適性評価のために使用されたエンベロープエントロピーとIMFの選択のためのKurtosis比較.
主要な成果:
- GWO-VMD法では,他の方法と比較してSNRが27.78%増加しました.
- 構造的類似性 (SSIM) を維持しながら,RMSEの78.82%の改善を示した.
- ランダムなノイズが分離され,合成データと実際のデータで有効な地震信号成分が保存されました.
結論:
- 提案されたGWO-VMD方法は地震のランダムなノイズ分離と抑制に有効です.
- この技術は地震データの品質を向上させるのに優れた性能を提供します.
- この方法は,騒音の分離と重要な地震信号情報の保存の両方に有効であることが証明されています.
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