Author(s)

H. Kojima, Y. Taguchi, K. Nishimura & S. Obayashi

Abstract

This paper presents an inverse- and decompositional-analysis of unobserved trigger factors according to slope failure types. Due to the difficulties of pixelbased observation on the trigger factors, we had proposed the inverse analysis algorithm based on the structural equation modeling (SEM). Through the \“measurement equation” defined between the causal factors (i.e., observed variables) and the trigger factor (i.e., unobserved latent variable), the trigger factor can be inversely estimated. As the subsequent subjects for the previous studies, in this contribution, we have tried to decompose trigger factors into the \“1st trigger factor” and the \“2nd trigger factor” with respect to slope failure types, such as surface slope failure, deep-seated slope failure, and landslide, which had been induced by Niigata Chuetsu Earthquake (Oct. 23, 2004). The 1st and the 2nd trigger factor influence map have been also produced according to the slope failure types. As a final outcome, the differences in these TFI maps are delineated on a \“difference (DIF) map” that enables us to analyze the difference of trigger factor influence with respect to slope failure types simultaneously. Keywords: slope failure types, inverse and decompositional analysis of trigger factors, geographical information, satellite remotely sensed data, structural equation modeling.

Keywords

slope failure types, inverse and decompositional analysis of trigger factors, geographical information, satellite remotely sensed data, structural equation modeling