Elaine K. Young, Michael E. Oskin, Alba M. Rodriguez Padilla
{"title":"Reproducibility of Remote Mapping of the 2019 Ridgecrest Earthquake Surface Ruptures","authors":"Elaine K. Young, Michael E. Oskin, Alba M. Rodriguez Padilla","doi":"10.1785/0220230095","DOIUrl":null,"url":null,"abstract":"Abstract We use multiple, independently produced surface-rupture maps of the 2019 Ridgecrest earthquake sequence to test the reproducibility of surface-rupture map interpretation and completeness. The 4 July Mw 6.4 and 5 July Mw 7.1 earthquakes produced surface-rupture zones approximately 20 and 50 km in length, respectively. Three independent mappers with various backgrounds in active tectonics mapped the surface rupture from the postearthquake lidar data without knowledge from postearthquake field or geodetic observations. Visual comparisons of the three remote rupture maps show good agreement for scarps >50 cm in height. For features with less topographic expression, interpretations of the data vary more widely between mappers. Quantitative map comparisons range from 18% to 54% consistency between mapped lines with 1 m buffers. The percent overlap increases with buffer width, reflecting variance in line placement as well as differences in fault-zone interpretation. Overall, map similarity is higher in areas where the surface rupture was simpler and had more vertical offset than in areas with complex rupture patterns or little vertical offset. Fault-zone interpretation accounts for the most difference between maps, while line placement accounts for differences at the meter scale. In comparison to field observations, our remotely produced maps capture the principal rupture well but miss small features and geometric complexity. In general, lidar excels for the detection and measurement of vertical offsets in the landscape, and it is deficient for detecting lateral offset with little or no vertical motion.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"20 1","pages":"0"},"PeriodicalIF":2.6000,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seismological Research Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1785/0220230095","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract We use multiple, independently produced surface-rupture maps of the 2019 Ridgecrest earthquake sequence to test the reproducibility of surface-rupture map interpretation and completeness. The 4 July Mw 6.4 and 5 July Mw 7.1 earthquakes produced surface-rupture zones approximately 20 and 50 km in length, respectively. Three independent mappers with various backgrounds in active tectonics mapped the surface rupture from the postearthquake lidar data without knowledge from postearthquake field or geodetic observations. Visual comparisons of the three remote rupture maps show good agreement for scarps >50 cm in height. For features with less topographic expression, interpretations of the data vary more widely between mappers. Quantitative map comparisons range from 18% to 54% consistency between mapped lines with 1 m buffers. The percent overlap increases with buffer width, reflecting variance in line placement as well as differences in fault-zone interpretation. Overall, map similarity is higher in areas where the surface rupture was simpler and had more vertical offset than in areas with complex rupture patterns or little vertical offset. Fault-zone interpretation accounts for the most difference between maps, while line placement accounts for differences at the meter scale. In comparison to field observations, our remotely produced maps capture the principal rupture well but miss small features and geometric complexity. In general, lidar excels for the detection and measurement of vertical offsets in the landscape, and it is deficient for detecting lateral offset with little or no vertical motion.