Chanthujan Chandrakumar, Marion Lara Tan, Caroline Holden, Max Stephens, Amal Punchihewa, Raj Prasanna
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Each relationship’s performance is evaluated through correlation coefficient (R), coefficient of determination (R²), root mean square error (RMSE), and 5-fold Cross-validation RMSE, aiming to identify the most predictive empirical model for the Canterbury context. Results using a weighted scoring approach indicate that the relationship involving P-wave Peak Ground Velocity (Pv) within a 3-second window strongly correlates with S-wave Peak Ground Acceleration (PGA), highlighting its potential for EEWS. The selected empirical relationship is subsequently applied to establish a P-wave amplitude (Pv) threshold for the Canterbury region as a case study from which an EEWS could benefit. 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引用次数: 0
摘要
新西兰奥特亚罗瓦(Aotearoa New Zealand,简称 NZ)是一个地震高发地区,本研究旨在解决预测该地区地震时 S 波振幅的关键问题,以实施地震预警系统(EEWS)。这项研究使用了来自新西兰坎特伯雷地区历史地震综合数据集的地动参数。该研究探索了在破坏性 S 波振幅到来之前对其进行估计的可能性,主要侧重于最初的 P 波信号。研究利用三个参数在 P 波和 S 波振幅之间建立了九种线性回归关系:峰值地面加速度、峰值地面速度和峰值地面位移。通过相关系数 (R)、判定系数 (R²)、均方根误差 (RMSE) 和 5 倍交叉验证均方根误差,对每种关系的性能进行了评估,旨在找出对坎特伯雷环境最具预测性的经验模型。使用加权评分法得出的结果表明,涉及 3 秒窗口内 P 波峰值地面速度 (Pv) 的关系与 S 波峰值地面加速度 (PGA) 高度相关,突出了其在 EEWS 方面的潜力。选定的经验关系随后被用于确定坎特伯雷地区的 P 波振幅(Pv)阈值,作为 EEWS 可从中受益的案例研究。该研究还建议今后开展研究,探索预测 S 波振幅的复杂机器学习模型,并利用新西兰不同地区的更多数据集扩大分析范围。
Estimating S-wave amplitude for earthquake early warning in New Zealand: Leveraging the first 3 seconds of P-Wave
This study addresses the critical question of predicting the amplitude of S-waves during earthquakes in Aotearoa New Zealand (NZ), a highly earthquake-prone region, for implementing an Earthquake Early Warning System (EEWS). This research uses ground motion parameters from a comprehensive dataset comprising historical earthquakes in the Canterbury region of NZ. It explores the potential to estimate the damaging S-wave amplitude before it arrives, primarily focusing on the initial P-wave signals. The study establishes nine linear regression relationships between P-wave and S-wave amplitudes, employing three parameters: peak ground acceleration, peak ground velocity, and peak ground displacement. Each relationship’s performance is evaluated through correlation coefficient (R), coefficient of determination (R²), root mean square error (RMSE), and 5-fold Cross-validation RMSE, aiming to identify the most predictive empirical model for the Canterbury context. Results using a weighted scoring approach indicate that the relationship involving P-wave Peak Ground Velocity (Pv) within a 3-second window strongly correlates with S-wave Peak Ground Acceleration (PGA), highlighting its potential for EEWS. The selected empirical relationship is subsequently applied to establish a P-wave amplitude (Pv) threshold for the Canterbury region as a case study from which an EEWS could benefit. The study also suggests future research exploring complex machine learning models for predicting S-wave amplitude and expanding the analysis with more datasets from different regions of NZ.
期刊介绍:
The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.