Hybrid Empirical Ground-Motion Models for the Island of Hawaii Based on an Updated Strong Ground-Motion Database

地震动 运动(物理) 地质学 大地测量学 数据库 地震学 计算机科学 人工智能
作者
Shahram Pezeshk,Mehran Davatgari-Tafreshi,Alireza Haji‐Soltani
出处
期刊:Bulletin of the Seismological Society of America [Seismological Society of America]
卷期号:114 (4): 2186-2201 被引量:1
标识
DOI:10.1785/0120230225
摘要

ABSTRACT In this study, we develop ground-motion models (GMMs) for the Island of Hawaii. This area has been the site of several significant earthquake events with a growing database of strong ground-motion observations. Ground-motion modeling on the Island of Hawaii is challenging due to different anelastic attenuation characteristics, the volcanic origin of some of the events, and event depth distribution. Only a few GMMs have been developed for the Island of Hawaii. In this study, we apply a hybrid empirical method (HEM) to develop two separate GMMs for shallow (hypocentral depth ≤ 20 km) and deep (hypocentral depth > 20 km) earthquakes on the Island of Hawaii. We utilize the ratio of the stochastic point-source model in the target and host regions as an appropriate adjustment factor. We apply these adjustment factors to convert the GMMs from the host (western North America) to the target (Island of Hawaii) region. We considered five GMMs proposed in the Next Generation Attenuation Phase 2 project by the Pacific Earthquake Engineering Research Center to model ground motions in the host region. We developed GMMs to predict peak ground acceleration and 5%-damped pseudospectral acceleration at periods T = 0.01–10 s, for moment magnitudes (M) in the range of 3–7.5, and for Joyner–Boore distances in the RJB≤200 km range. The applicability of HEM to develop GMMs for the Island of Hawaii and the growing strong ground-motion data result in further improvements in the capability of GMMs.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
奋斗冷风关注了科研通微信公众号
2秒前
晨曦发布了新的文献求助10
3秒前
舒适的冰凡完成签到,获得积分10
4秒前
4秒前
科研通AI5应助狂野的子轩采纳,获得10
4秒前
4秒前
Lu完成签到 ,获得积分10
5秒前
qqqqgc发布了新的文献求助10
5秒前
称心曼安应助炙热芷蕊采纳,获得20
5秒前
5秒前
IRONY发布了新的文献求助10
6秒前
ooseabiscuit完成签到,获得积分10
6秒前
7秒前
7秒前
zero完成签到 ,获得积分10
8秒前
零零落落完成签到,获得积分20
9秒前
qqqqgc完成签到,获得积分10
9秒前
幸福顺意完成签到,获得积分10
10秒前
内向不二发布了新的文献求助10
10秒前
西瓜发布了新的文献求助10
11秒前
零零落落发布了新的文献求助10
12秒前
13秒前
杰2580完成签到,获得积分10
14秒前
wadaxiwa完成签到,获得积分10
14秒前
墨墨墨墨墨墨完成签到,获得积分10
14秒前
Hathaway完成签到,获得积分10
15秒前
15秒前
科研通AI5应助atad2采纳,获得10
15秒前
Qq完成签到,获得积分10
15秒前
WeiPaiHWuFXZ完成签到 ,获得积分10
15秒前
谨慎师完成签到,获得积分10
15秒前
mengzhao完成签到,获得积分10
16秒前
章竟完成签到,获得积分10
16秒前
Lucas应助晒太阳的乌龟采纳,获得10
18秒前
18秒前
HMMXC完成签到,获得积分20
18秒前
18秒前
19秒前
高分求助中
Technologies supporting mass customization of apparel: A pilot project 600
Разработка метода ускоренного контроля качества электрохромных устройств 500
Chinesen in Europa – Europäer in China: Journalisten, Spione, Studenten 500
Arthur Ewert: A Life for the Comintern 500
China's Relations With Japan 1945-83: The Role of Liao Chengzhi // Kurt Werner Radtke 500
Two Years in Peking 1965-1966: Book 1: Living and Teaching in Mao's China // Reginald Hunt 500
Epigenetic Drug Discovery 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3817577
求助须知:如何正确求助?哪些是违规求助? 3360882
关于积分的说明 10410010
捐赠科研通 3078935
什么是DOI,文献DOI怎么找? 1690894
邀请新用户注册赠送积分活动 814197
科研通“疑难数据库(出版商)”最低求助积分说明 768065