A Critical Insight into the 2018 Sulawesi Earthquake-Induced Landslide in Palu Valley Region, Indonesia: Integrating Remote Sensing and GIS Techniques with FE-Based Numerical Modeling

An earthquake of 7.5 moment magnitude struck Sulawesi Island of Indonesia on September 28, 2018, causing landslides, mudflows, and a tsunami. Despite having an inclination of <2%, the slopes in several areas were impacted by landslides with lateral spreading ranging from 1 to 2 km. Various research groups have conducted numerous studies using geotechnical investigation, geophysical techniques, remote sensing, and geographical information system (GIS) techniques. The primary cause of landslides has been linked to excessive irrigation practices across the region, leading to liquefaction following the earthquake. This study provides critical insight into the various approaches employed and geotechnical experiments carried out to investigate Palu Valley earthquake-induced landslides. The current study focuses on the evaluation of landslide-affected areas utilizing remote sensing and GIS techniques as well as geotechnical modeling. The landslide-affected zones are identified and demarcated using multispectral satellite remote sensing imagery. The remote sensing and GIS-derived input are utilized for further geotechnical modeling. Furthermore, with the aid of finite element (FE) based modeling, the most critical section of a landslide is analyzed. The FE modeling techniques are widely used to evaluate forces, strain/stress field, and displacement induced in the soil mass. Different GeoStudio software modules (SEEP/W, SIGMA/W, and QUAKE/W) are utilized to simulate the real field scenario of the landslide progression. Seepage and stress analysis of the landslide section is performed to gain an understanding of the pore water pressure and stress conditions, and seismic analysis is performed to analyze dynamic loading due to earthquake shaking. Finally, the landslide section is evaluated for liquefaction potential and lateral spreading, which are correlated to in situ moisture content and seismic strong motion characteristics.