{"title":"Intensity Prediction Equations for Himalaya and its sub-regions based on data from traditional sources and USGS’s Did You Feel It? (DYFI)","authors":"P. Anbazhagan, Harish Thakur","doi":"10.1007/s10950-024-10214-7","DOIUrl":null,"url":null,"abstract":"<div><p>This study has developed Intensity Prediction Equations (IPEs) for the Himalayas and its sub-regions (divided into North-West Himalaya, Central Himalaya, and North-East Himalaya). For this purpose, intensity data reported in previous studies using traditional methods (like field surveys, media reports, and newspapers) and internet-based questionnaires (such as USGS’s Did You Feel It? or DYFI) were used to catalogue two separate intensity datasets. Intensities of traditional datasets were also reassessed for some earthquake events by different studies in the different scales of assignment, which was homogenized for the same intensity scale. IPEs are derived for both datasets separately using a two-stage and one-stage regression technique. These IPEs are developed for a first- and second-order relation with respect to earthquake magnitude. A “maximum intensity vs. magnitude approximation of the IPE” approach relying on an optimal hypocentral depth has also been proposed to select the best-suited IPEs. The information-theoretic approach-based Log-likelihood method (Scherbaum et al. 2009) has been used to check and compare developed IPE performance for events not used for IPE development. These newly developed equations can be used to assess the damage potential of future earthquakes.</p></div>","PeriodicalId":16994,"journal":{"name":"Journal of Seismology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Seismology","FirstCategoryId":"89","ListUrlMain":"https://link.springer.com/article/10.1007/s10950-024-10214-7","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
This study has developed Intensity Prediction Equations (IPEs) for the Himalayas and its sub-regions (divided into North-West Himalaya, Central Himalaya, and North-East Himalaya). For this purpose, intensity data reported in previous studies using traditional methods (like field surveys, media reports, and newspapers) and internet-based questionnaires (such as USGS’s Did You Feel It? or DYFI) were used to catalogue two separate intensity datasets. Intensities of traditional datasets were also reassessed for some earthquake events by different studies in the different scales of assignment, which was homogenized for the same intensity scale. IPEs are derived for both datasets separately using a two-stage and one-stage regression technique. These IPEs are developed for a first- and second-order relation with respect to earthquake magnitude. A “maximum intensity vs. magnitude approximation of the IPE” approach relying on an optimal hypocentral depth has also been proposed to select the best-suited IPEs. The information-theoretic approach-based Log-likelihood method (Scherbaum et al. 2009) has been used to check and compare developed IPE performance for events not used for IPE development. These newly developed equations can be used to assess the damage potential of future earthquakes.
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.