市民の地震学は,2021年のハイチ地震の解読に役立ちます
E Calais1,2,3,4, S Symithe3,5, T Monfret2,3,6
1Département de Géosciences, École Normale Supérieure, CNRS UMR 8538, PSL Université, Paris, France.
Science (New York, N.Y.)
|March 10, 2022
まとめ
市民の地震計は,従来のネットワークが故障した2021年のハイチ地震の重要な近地データを提供しました. この市民科学のデータは 余震の正確な予測を可能にし 大規模な地震の理解を深めました
科学分野:
- 地球科学
- 地震学
- 地理学
背景:
- ハイチでの2021年M(w) 7.2の地震は,2010年のM(w) 7.0の地震の近く,地震的に活発な断層地帯で発生した.
- フィールドへのアクセスは不安全のため制限され,2021年の地震の間,全国的な地震計ネットワークは機能しませんでした.
- 2019年に設置された既存の市民地震計のネットワークは,重要な代替データ源を提供しました.
研究 の 目的:
- 地震メカニズムと余震の配列を理解するための市民の地震計データの有用性を評価する.
- 危機時の地震モニタリングにおける市民科学の効果を評価する.
- 市民科学から得られたデータを用いて 余震予測の機械学習モデルをテストする.
主な方法:
- リアルタイムのデータ収集のための市民の地震計ネットワークの導入と利用
- 余震群を特定するために近地地震データを分析する.
- 市民の地震計データと従来の地震学と地質学データの逆転を比較する.
- 市民の地震計データを基に余震を予測する機械学習アルゴリズムの適用
主要な成果:
- 市民の地震計のデータは 2つの異なる余震群を成功裏に定義しました
- これらのクラスターは,従来の方法によって特定されたコセイズミックスリップの領域と空間的に相関しています.
- 最寄りのシズモメーターのデータを用いた機械学習モデルは,ネットワークによるカタログに匹敵する予測精度を達成しました.
結論:
- 市民科学ネットワークは 大規模な地震の際に 重要な地震データをリアルタイムで提供できます 特に従来のインフラが損なわれた場合です
- 市民の地震計のデータは地震のメカニズムと余震モニタリングの迅速な分析に役立ちます.
- 市民科学と機械学習の統合は 地震への備えと対応を改善するための強力なアプローチを提供します
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