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Fault depth estimation using support vector classifier and features selection

Content Provider	Springer Nature Link
Author	Hekmatian, Mohammad Ehsan Ardestani, Vahid E. Riahi, Mohammad Ali Bagh, Ayyub Memar Koucheh Amini, Jalal
Copyright Year	2013
Abstract	Depth estimation of subsurface faults is one of the problems in gravity interpretation. We tried using the support vector classifier (SVC) method in the estimation. Using forward and nonlinear inverse techniques, detecting the depth of subsurface faults with related error is possible but it is necessary to have an initial guess for the depth and this initial guess usually comes from non-gravity data. We introduce SVC in this paper as one of the tools for estimating the depth of subsurface faults using gravity data. We can suppose that each subsurface fault depth is a class and that SVC is a classification algorithm. To better use the SVC algorithm, we select proper depth estimation features using a proper features selection (FS) algorithm. In this research, we produce a training set consisting of synthetic gravity profiles created by subsurface faults at different depths to train the SVC code to estimate the depth of real subsurface faults. Then we test our trained SVC code by a testing set consisting of other synthetic gravity profiles created by subsurface faults at different depths. We also tested our trained SVC code using real data.
Starting Page	88
Ending Page	96
Page Count	9
File Format	PDF
ISSN	16727975
Journal	Applied Geophysics
Volume Number	10
Issue Number	1
e-ISSN	19930658
Language	English
Publisher	Chinese Geophysical Society
Publisher Date	2013-04-09
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	depth estimation subsurface fault support vector classifier feature features selection Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Geophysics

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