基于机器学习的地动模型用于预测日本钻孔运动的 PSA

IF 1.6 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Sinhang Kang, Eunbi Mun, Dung Tran Thi Phuong, Byungmin Kim
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引用次数: 0

摘要

以前曾基于回归分析(RA)开发了许多预测地表地动强度的地动模型(GMM)。本研究基于机器学习 (ML) 方法(即两种集合方法(随机森林 (RF) 和梯度提升 (GB))以及人工神经网络 (ANN))开发了 GMM,用于估算 30 个周期(0.01-7.0 s)内岩内地动的 5%阻尼伪谱加速度 (PSA)。考虑到四种地震类型(活动地壳区域事件的主震和余震以及俯冲带界面事件的主震和余震)的特点不同,我们分别开发了四种地震类型的 GMM。我们利用了日本 575 个钻孔站在 602 次矩震级大于 5.0 且破裂距离小于 300 公里的地震中记录的 20,041 次地面运动。通过总残差、事件间残差和事件内残差的标准偏差,评估了基于 RF、GB、ANN 和 RA 的 GMM 的预测性能。基于三种 ML 方法(RF、GB 和 ANN)的 GMM 比基于 RA 的模型表现更好。基于射频的 GMMs 对岩内地面运动的 PSA 预测最为准确,偏差和方差较小,可提高地下结构的抗震设计和地震危险性评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning-based ground motion models for predicting PSAs of borehole motions in Japan

Machine learning-based ground motion models for predicting PSAs of borehole motions in Japan

Machine learning-based ground motion models for predicting PSAs of borehole motions in Japan

Numerous ground-motion models (GMMs) that predict the intensities of surface ground motions have been previously developed based on regression analysis (RA). This study develops GMMs to estimate 5% damped pseudo-spectral accelerations (PSAs) for 30 periods (0.01–7.0 s) for within-rock ground motions, based on machine learning (ML) methods (i.e., two ensemble methods (random forest (RF) and gradient boosting (GB)) and an artificial neural network (ANN)). GMMs are developed separately for four earthquake types (main and aftershocks of active crustal region events and those of subduction zone interface events), considering the differences in the characteristics of each earthquake type. We utilize 20,041 ground motions recorded at 575 borehole stations in Japan during 602 earthquakes with moment magnitudes greater than 5.0 and rupture distances shorter than 300 km. The prediction performances of GMMs based on RF, GB, ANN, and RA are evaluated by the standard deviations of the total, between-event, and within-event residuals. The GMMs based on the three ML methods (RF, GB, and ANN) perform better than the RA-based models. The RF-based GMMs resulted in the most accurate prediction of the PSAs of within-rock ground motions with a small bias and variance, which can enhance the seismic designs and seismic hazard assessments for underground structures.

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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.
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