DOI Number : 10.5614/itbj.eng.sci.2011.43.3.1
Hits : 4

Comparing Models GRM, Refraction Tomography and Neural Network to Analyze Shallow Landslide

Armstrong F. Sompotan1, Linus A. Pasasa2 & Rachmat Sule3

1Department of Physics, Manado State University
Jl. Kampus FMIPA UNIMA di Tondano, 95618, Indonesia
2Physics of Complex System, Institute of Technology Bandung
3Department of Geophysical Engineering, Institute of Technology Bandung
Jl. Ganesha 10 Bandung, 40132, Indonesia
E-mail: armstrong.itb@gmail.com


Abstract. Detailed investigations of landslides are essential to understand fundamental landslide mechanisms. Seismic refraction method has been proven as a useful geophysical tool for investigating shallow landslides. The objective of this study is to introduce a new workflow using neural network in analyzing seismic refraction data and to compare the result with some methods; that are general reciprocal method (GRM) and refraction tomography. The GRM is effective when the velocity structure is relatively simple and refractors are gently dipping. Refraction tomography is capable of modeling the complex velocity structures of landslides. Neural network is found to be more potential in application especially in time consuming and complicated numerical methods. Neural network seem to have the ability to establish a relationship between an input and output space for mapping seismic velocity. Therefore, we made a preliminary attempt to evaluate the applicability of neural network to determine velocity and elevation of subsurface synthetic models corresponding to arrival times. The training and testing process of the neural network is successfully accomplished using the synthetic data. Furthermore, we evaluated the neural network using observed data. The result of the evaluation indicates that the neural network can compute velocity and elevation corresponding to arrival times. The similarity of those models shows the success of neural network as a new alternative in seismic refraction data interpretation.

Keywords: GRM; neural network; refraction tomography; shallow landslide.

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