Scholarlink

<p>The Doruneh Fault System, Iran’s second-largest fault zone, generates infrequent but large-magnitude earthquakes with long recurrence intervals. Limited historical data from this sparsely populated desert region challenge reliable seismic hazard assessments. We address this gap by developing a 50,000-year synthetic seismicity model for the Doruneh Fault System using Virtual Quake, independent of observed catalogs. The model produces seismicity rates consistent with available data and enables the calculation of time-dependent conditional probabilities and expected waiting times (EWTs) for earthquakes of M6 + and above. Given the fault’s extensive length and proximity to urban and industrial centers, these findings enhance seismic hazard evaluations for the region. Our study demonstrates the value of synthetic seismicity models in data-scarce regions, providing critical insights for earthquake risk mitigation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Recurrence Time Distributions of Large Earthquakes along Doruneh Fault System (NE Iran): Assessment Using Virtual Quake Simulator

  • 期刊论文

  • 发表日期：

    2025年9月4日

查看全文
  • Mohammad Hossein Tahriri,
  • Hamid Saffari

摘要

The Doruneh Fault System, Iran’s second-largest fault zone, generates infrequent but large-magnitude earthquakes with long recurrence intervals. Limited historical data from this sparsely populated desert region challenge reliable seismic hazard assessments. We address this gap by developing a 50,000-year synthetic seismicity model for the Doruneh Fault System using Virtual Quake, independent of observed catalogs. The model produces seismicity rates consistent with available data and enables the calculation of time-dependent conditional probabilities and expected waiting times (EWTs) for earthquakes of M6 + and above. Given the fault’s extensive length and proximity to urban and industrial centers, these findings enhance seismic hazard evaluations for the region. Our study demonstrates the value of synthetic seismicity models in data-scarce regions, providing critical insights for earthquake risk mitigation.