Observation of short-term variations in the clay minerals ratio after the 2015 Chile great earthquake (8.3$M_{\rm{w}}$) using Landsat 8 OLI data

Journal of earth system science

Niu Z^1,2¹  Mitra A K^4³  Nath B^1,2,3⁴ 

[1]College of Resource and Environment, University of Chinese Academy of Sciences (UCAS), Yuquan Road 19, Shijingshan, Beijing 100 049, People’s Republic of China.^2

[2]Department of Earth Sciences, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711 103, West Bengal, India.^4

[3]Department of Geography and Environmental Studies, University of Chittagong, Chittagong 4331, Bangladesh.^3

[4]The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS) and Beijing Normal University, P.O. Box 9718, Datun Road, Chaoyang District, Beijing 100 101, People’s Republic of China.^1

关键词: Clay minerals ratio;    earthquake;    Chile;    Landsat 8 OLI;    multispectral;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

In this paper, we explore the potential use of available free Landsat sensor data to investigate the short-term variations (STV) in the clay mineral ratio (CMR) following the 2015 Chile great moment magnitude earthquake (8.3$M_{\rm{w}}$). The present investigation was carried out in the absence of ground observation data. Landsat 8 Operational Land Imager (OLI)-based multi-temporal imageries of before, after and non-earthquakes were used to derive the above parameter by applying the band ratio approach of bands 6 and 7, where the before and after imageries were compared with non-seismic event images as well as for validation. For the temporal automatic lineament data extraction and final lineament mapping, band 8 (panchromatic) was used by applying the LINE algorithm technique of PCI Geomatica, and ArcGIS 10.5 software, respectively. All these derived products finally interact with the regional geology, fault line and lineament systems. The results reveal that CMR can easily identify the STV at temporal scales before and after the earthquake, while both are normal during non-earthquake time. However, this variation was observed in all three buffer zones (i.e., 50, 100 and 150 km buffer) and highly pronounced especially in the fault adjoining areas. Therefore, we found this research to be effective and could be used as an alternative method for future earthquake studies.

【 授权许可】

CC BY