Investigation of Damages in Immediate Vicinity of Co-Seismic Faults During the 2016 Kumamoto Earthquake

In order to understand the damage situation immediately after the occurrence of a disaster and to support disaster response, we developed a method to classify the degree of building damage in three stages with machine-learning using road-running survey images obtained immediately after the Kumamoto earthquake. Machine-learning involves a learning phase and a discrimination phase. As training data, we used images from a camera installed in the travel direction of an automobile, in which the degree of damage was visually categorized. In the learning phase, class separation is carried out by support vector machine (SVM) on a basis of a feature calculated from training patch images for each extracted damage category. In the discrimination phase, input images are provided with raster scan so that the class separation is carried out in units of the patch image. In this manner, learning, discrimination, and parameter tuning are repeated. By doing so, we developed a damage-discrimination model for each patch image and validated the discrimination accuracy using a cross-validation method. Furthermore, we developed a method using an optical flow for preventing double counting of damaged areas in cases where an identical building is captured in multiple photos.

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Benefits of Defining Geological Sensitive Zones in the Mitigation of Disasters Along Earthquake Fault Zones in Taiwan – The Case of Milun Fault

Chu¹,

Huang²,

Lin³

et al. 2021

JDR

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In Taiwan, the main purpose of earthquake fault zone legislation is to prevent earthquake-related disasters around the surface traces of active faults, particularly in urban areas. Here, the Geologically Sensitive Area (GSA) of the Milun Fault (Milun Earthquake Fault Zone) is used as an example to reveal the importance of such legislation. Field data collected along the Milun Fault before and after the 2018 Hualien Earthquake were used to reveal the reappearance of damages within the GSA. The 2018 Hualien Earthquake represents one of the shortest recurrence intervals (67 years) among all major faults in Taiwan. Most of the surface ruptures and damaged buildings in Hualien City were within the Milun Fault GSA and concentrated on the hanging wall of the fault. Moreover, 61% (91/148) of the damaged buildings and 83% (692/835) of the surface ruptures occurred within 100 m of the fault line. The results of this study demonstrate the importance of defining GSAs of active faults for mitigating earthquake hazards.

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Investigation of Damages in Immediate Vicinity of Co-Seismic Faults During the 2016 Kumamoto Earthquake

Cited by 7 publications

References 11 publications

Seismological and Geodetic Observations of the 2016 Kumamoto Earthquake Sequence

Seismological and Geodetic Observations of the 2016 Kumamoto Earthquake Sequence

Damage Detection Method for Buildings with Machine-Learning Techniques Utilizing Images of Automobile Running Surveys Aftermath of the 2016 Kumamoto Earthquake

Benefits of Defining Geological Sensitive Zones in the Mitigation of Disasters Along Earthquake Fault Zones in Taiwan – The Case of Milun Fault

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