Landslide investigation by using geoelectrical resistivity in Taipingshan, Yilan County, Taiwan

• Main Author: Akhmad Zamroni
• Other Authors: Wen-Yen Chang
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/78jc3x

碩士 === 國立東華大學 === 自然資源與環境學系 === 106 === Rainfall is a key factor of landslides on steep slopes. Geoelectrical resistivity can produce a two-dimension (2D) resistivity image supported by drilling data and previous geological profile to analyze profile in the subsurface. Reconstruction of subsurface profile showed a depth of potential mass wasting in the landslide area. Kinds of material derived are colluvium at a depth of 0 – 10 m, weathered rock (slate) at a depth of 10 – 29 m, and bedrock (slate) at a depth of 29 – 100 m. The position of sliding surface was located between the weathered rock (slate) and bedrock (slate) at a depth of around 29 m. The sliding surface was obtained from two materials with contrasting resistivity values. So, the thickness of potential mass wasting is around 29 m. A landslide in Taipingshan was caused by Typhoon Saola in 2012, when rainfall was >1800 mm for three days. It is very important to monitor the area because Taipingshan is a National Forest Recreation Area. Geoelectrical resistivity with dipole-dipole and Schlumberger configurations permanently monitor this landslide prone area. Rainfall data for 42 days were used to determine geoelectrical resistivity response towards rainfall. The 2D resistivity image showed significant dynamic changes between dry and wet conditions. Resistivity value in the dry condition or there is low rainfall was around 70 Ωm. Resistivity value in wet condition was around 24 – 70 Ωm. Changes in resistivity occurred in 0 – 40 m depth where there were weathered rock and many fracture zones that showed discolorations at that depth. Resistivity value declines in heavy rainfall, especially in those positions. This is because water filled the fracture zone or percolate into the weathered rock. Resistivity value increases when conditions are dry or there is low rainfall because water evaporates. This 2D resistivity image response towards the rainfall could help with early warning of landslides.