Improvement of the Evaluation of Seismic Risk in Fault Areas by Lidar-Derived Geophysical Data

Author:

Molua Collins O.

Abstract

This study aimed to improve the methods of assessing seismic risk in fault zones based on lidar data in geophysics. The research highlighted this by comparing the newly developed fault maps with the usual methods of fault mapping and how lidar technology developed high-resolution 3D mapping. We conducted mobile and terrestrial LIDAR surveys to produce DEMs and study the attributes of the fault zones. The technique involved mobile lidar systems with different specifications of emitted transmission rate: 45 000 m/s to 52, 100m/s pulse repetition: 190, 000 Hz–220, 000 Hz; and point density: 10223 points/m2 to 14567 points/m2. Terrestrial lidar surveys used scanner heights of 1. 500-1. 700m and obtained the horizontal and vertical sampling density, ranging from 240,456 to 315,678 points per square meter. We used LAStools, Arc GIS, and QISIS software to filter, classify, and visualize the data processing. e applied interpolation techniques such as IDW, Kriging, Spline, and Natural Neighbors to generate DEMs. Research outcomes identified 15 different fault segments with lengths varying from 10. 000-20. 000 km, along with maximum displacements of 0. 987-4. 567 m, and average slip rates of 3. 456-7. 890 mm/year. The most extended fault segment altogether was FS05, which was 20. 000 km with a maximum bidding distance of 4. 567 m and a 7. 890 mm/year slip rate. We discovered that the proposed method successfully filtered out noise points from lidar data, with the noise points varying between 0.111-0.266 million. We created DEMs with vertical rms errors ranging from 0.045-0.050 m. The study revealed that lidar technology offers accurate and dense geospatial data, essential for discriminating between fault zones. This approach dramatically improves seismic hazard analysis and the identification of the best ways to minimize risks. These are increasing lidar surveys in other seismically active regions, using multiple data sources for analysis, and deploying constant surveys in high-risk fault line regions to increase consistency in detecting surface changes and tectonic activity.

Publisher

HM Publishers

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3