开发基于机器学习的地动模型,以预测土耳其的峰值地速

IF 1.6 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Journal of Seismology Pub Date : 2024-09-05 DOI:10.1007/s10950-024-10239-y
Fahrettin Kuran, Gülüm Tanırcan, Elham Pashaei
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引用次数: 0

摘要

本文介绍了基于机器学习的图尔基耶峰值地动速度(PGV)几何平均水平分量地动模型。PGV 是衡量和诊断结构潜在地震破坏的重要烈度指标。PGV 的可靠预测对于精确计算地震灾害至关重要。本文对随机森林、支持向量机、线性回归、人工神经网络、梯度提升和贝叶斯脊回归等各种机器学习算法的效率、可靠性和能力进行了评估和比较。最新编制的土耳其强震数据库包含了从 1983 年到 2023 年发生的 950 多次地震,用于塑造模型的学习能力和做出准确预测的能力。应用三种特征选择方法--最小绝对收缩和选择操作器、递归特征消除和皮尔逊相关性--分别代表嵌入、包装和过滤方法,以确定最适合预测 PGV 的估计器参数。残差分析和统计评估指标用于衡量机器学习模型的性能和有效性。在所应用的算法中,梯度提升法在预测 PGV 方面取得了巨大成功,尤其是在综合利用所有估计参数(特征)时。与现有的地动模型相比,梯度提升模型表现出更出色的预测能力。该模型适用于矩级在 3.5 到 7.8 之间、Joyner 和 Boore 距离达 200 千米的浅地壳走向滑动和正断层地震。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Developing machine learning-based ground motion models to predict peak ground velocity in Turkiye

This paper introduces machine learning-based Turkiye-specific ground motion models for the geometric mean horizontal component of peak ground velocity (PGV). PGV is a significant intensity metric to measure and diagnose potential earthquake damage in structures. Reliable prediction of PGV is of essential importance in precise calculations of seismic hazard. The efficiencies, reliabilities, and capabilities of various machine learning algorithms, including Random Forest, Support Vector Machine, Linear Regression, Artificial Neural Network, Gradient Boosting, and Bayesian Ridge Regression, are evaluated and compared. The most recently compiled Turkish strong motion database, which consists of over 950 earthquakes occurring from 1983 to 2023, is used for shaping the models' ability to learn and make accurate predictions. Three feature selection methods- Least Absolute Shrinkage and Selection Operator, Recursive Feature Elimination, and Pearson’s Correlation- representing embedded, wrapper, and filter approaches, respectively, are applied to determine the most suitable estimator parameters to predict PGV. Residual analyses and statistical evaluation metrics are employed to measure the performance and effectiveness of the machine learning models. Among the algorithms applied, Gradient Boosting demonstrates exceptional success in predicting PGV, particularly when utilizing all estimator parameters (features) collectively. The Gradient Boosting model exhibits superior predictive capabilities compared to existing ground motion models. It is applicable to shallow crustal strike-slip and normal faulting earthquakes with moment magnitude ranging from 3.5 to 7.8 and Joyner and Boore distance up to 200 km.

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来源期刊
Journal of Seismology
Journal of Seismology 地学-地球化学与地球物理
CiteScore
3.30
自引率
6.20%
发文量
67
审稿时长
3 months
期刊介绍: Journal of Seismology is an international journal specialising in all observational and theoretical aspects related to earthquake occurrence. Research topics may cover: seismotectonics, seismicity, historical seismicity, seismic source physics, strong ground motion studies, seismic hazard or risk, engineering seismology, physics of fault systems, triggered and induced seismicity, mining seismology, volcano seismology, earthquake prediction, structural investigations ranging from local to regional and global studies with a particular focus on passive experiments.
期刊最新文献
Source parameters of the May 28, 2016, Mihoub earthquake (Mw 5.4, Algeria) deduced from Bayesian modelling of Sentinel-1 SAR data Fault imaging using earthquake sequences: a revised seismotectonic model for the Albstadt Shear Zone, Southwest Germany A logic-tree based probabilistic seismic hazard assessment for the central ionian islands of cephalonia and ithaca (Western Greece) Developing machine learning-based ground motion models to predict peak ground velocity in Turkiye Fault structures of the Haichenghe fault zone in Liaoning, China from high-precision location based on dense array observation
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