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| 利用支持向量分类(SVC)估算断层深度和特征选择(英文) | |
| 作者姓名： | Mohammad Ehsan Hekmatian Vahid E. Ardestani Mohammad Ali Riahi Ayyub Memar Koucheh Bagh Jalal Amini |
| 作者单位： | Faculty of Basic Sciences of Science and Research Branch,Islamic Azad University;Nuclear Fuel Cycle Research School of Nuclear Science & Technology Research Institute (NSTRI);Institute of Geophysics,University of Tehran;Faculty of Engineering,University of Tehran |
| 摘 要： | 地下断层深度的估算是重力解释难题之一,我们试利用支持向量分类(SVC)法进行计算。使用正演和非线性反演技术,通过相关误错使检测地下断层深度成为可能。但必要有一个深度初始猜测值,而且这猜测值通常不是由重力资料得。本文我们介绍以SVC作为利用重力数据估算断层深度的一种手段。在这项研究中,我们假设一种地下断层深度可归为一种类型,SVC作为一个分类算法。为了有效地利用此SVC算法,我们基于一个正确的特征选择算法去选择正确的深度特征。本次研究中我们建立了一套基于不同深度地下断层的合成重力剖面训练集,用以训练用于计算实际的地下断层深度的SVC代码。然后用其它合成重力剖面训练集测试我们训练的SVC代码,同时也用实际资料验证了我们的训练SVC代码。 |
| 关 键 词： | 深度计算 地下断裂 支持向量分类(SVC) 特征 特征选择 |
Fault depth estimation using support vector classifier and features selection |
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| Mohammad Ehsan Hekmatian,Vahid E. Ardestani,Mohammad Ali Riahi,Ayyub Memar Koucheh Bagh,Jalal Amini.Fault depth estimation using support vector classifier and features selection[J].Applied Geophysics,2013,10(1):88-96. | |
| Authors: | , Mohammad Ehsan Hekmatian , Vahid EArdestani , Mohammad Ali Riahi , Ayyub Memar Koucheh Bagh , Jalal Amini |
| Institution: | 1. Faculty of Basic Sciences of Science and Research Branch, Islamic Azad University, Tehran, Iran;Nuclear Fuel Cycle Research School of Nuclear Science & Technology Research Institute(NSTRI), Tehran, Iran 2. Institute of Geophysics, University of Tehran, Tehran, Iran 3. Nuclear Fuel Cycle Research School of Nuclear Science & Technology Research Institute(NSTRI), Tehran, Iran 4. Faculty of Engineering, University of Tehran, Tehran, Iran |
| 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. |
| Keywords: | depth estimation subsurface fault support vector classifier feature features selection |
| 本文献已被 CNKI 万方数据 SpringerLink 等数据库收录！ | |