流离失所(心理学)
衰减
计算机科学
噪音(视频)
基本事实
选择(遗传算法)
多项式的
算法
人工智能
数学
光学
物理
心理学
图像(数学)
数学分析
心理治疗师
作者
María Elisa Ramos-Sepúlveda,Grace A. Parker,Eric M. Thompson,Scott J. Brandenberg,Mingzhou Li,Okan Ilhan,Youssef M. A. Hashash,Ellen M. Rathje,Jonathan P. Stewart
出处
期刊:Geo-Congress 2019
日期:2023-03-23
卷期号:: 327-335
被引量:4
标识
DOI:10.1061/9780784484692.034
摘要
Earthquake ground motion processing for next-generation attenuation (NGA) projects required human inspection to select high-pass corner frequencies (fcHP), which is time-intensive and subjective. With growth in the number of recordings per event and interest in enhancing repeatability, we sought to develop automated procedures for fcHP selection. These procedures consider signal-to-noise ratio (SNR) and non-physical features in the displacement time series that indicate high- and/or low-frequency noise effects. The procedures are implemented in a US Geological Survey software package (gmprocess). We extend previous procedures for SNR-based corner frequency selection to also check for low-frequency artifacts in the displacement record using a polynomial fit to improve fcHP selection. We evaluate the performance of the SNR and polynomial fit criteria using recordings from the 2020 M5.1 North Carolina and the 2013 M4.7 Southern Ontario earthquakes. Data processed with the SNR-only criteria can have low fcHP and displacement drift; the displacement check increases fcHP and reduces displacement drift.
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