使用机器学习算法预测地震地面运动的显著持续时间
1College of Civil Engineering, Dalian Minzu University, Dalian, 116600, Liaoning, China.
PloS one
|February 28, 2024
概括
这项研究通过机器学习增强了地震运动预测,引入了新的参数和融合模型,以提高显著持续时间预测的准确性.
科学领域:
- 地质物理学和地震学.
- 计算地震学是一种计算机地震学.
- 机器学习在地球科学中的应用.
背景情况:
- 预测地震运动持续时间对于地震工程和危险评估至关重要.
- 现有的模型通常在捕捉地震参数之间的复杂关系时缺乏准确性.
- 显然,需要先进的计算方法来改善地震持续时间的预测.
研究的目的:
- 使用机器学习算法预测地震运动的显著持续时间 (D5-75,D5-95).
- 引入和优化特征参数,以提高预测准确度.
- 开发和验证新型的融合模型,用于优越的地震持续时间预测.
主要方法:
- 使用XGBoost进行特征参数优化,识别最佳的四个参数组合.
- 随机森林,XGBoost,BP神经网络和SVM算法的比较预测性能.
- 开发和评估了两个聚变模型:堆积和加权平均.
主要成果:
- 与单个算法相比,融合模型 (堆叠和加权平均) 显著提高了预测准确度和概括能力.
- 优化的参数组合增强了机器学习模型的预测能力.
- 剩余分析证实了融合模型的性能和可靠性的提高.
结论:
- 机器学习,特别是聚变模型,提供了一种可靠的方法来预测地震运动的持续时间.
- 整合了额外的参数,如断层顶部深度和震中机制参数,完善了预测能力.
- 开发的核聚变模型为地震持续时间的预测提供了一种经过验证,准确和合理的方法,其性能优于现有的方法.
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