Applicability of artificial neural networks for obtaining velocity models from synthetic seismic data

被引:7
作者
Baronian, C. [1 ]
Riahi, M. A. [1 ]
Lucas, C. [2 ]
机构
[1] Univ Tehran, Inst Geophys, Tehran, Iran
[2] Univ Tehran, Dept Elect & Comp Engn, Tehran, Iran
关键词
Artificial neural networks (ANN); Seismic velocity analysis; Synthetic seismograms; Dipping structures; DECONVOLUTION; PICKING;
D O I
10.1007/s00531-008-0314-3
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models. The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers, namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the travel time hyperbolas of seismic data.
引用
收藏
页码:1173 / 1184
页数:12
相关论文
共 20 条
[1]   VELOCITY ANALYSIS BY ITERATIVE PROFILE MIGRATION [J].
ALYAHYA, K .
GEOPHYSICS, 1989, 54 (06) :718-729
[2]  
BRADLEY ME, 2003, PRACTICAL SEISMIC IN
[3]   Artificial neural networks for parameter estimation in geophysics [J].
Calderón-Macías, C ;
Sen, MK ;
Stoffa, PL .
GEOPHYSICAL PROSPECTING, 2000, 48 (01) :21-47
[4]   Automatic NMO correction and velocity estimation by a feedforward neural network [J].
Calderon-Macias, C ;
Sen, MK ;
Stoffa, PL .
GEOPHYSICS, 1998, 63 (05) :1696-1707
[5]   Hopfield neural networks, and mean field annealing for seismic deconvolution and multiple attenuation [J].
CalderonMacias, C ;
Sen, MK ;
Stoffa, PL .
GEOPHYSICS, 1997, 62 (03) :992-1002
[6]   The application of back-propagation neural network to automatic picking seismic arrivals from single-component recordings [J].
Dai, HC ;
MacBeth, C .
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 1997, 102 (B7) :15105-15113
[7]   Sub-basalt imaging problems and the application of Artificial Neural Networks [J].
Fitzgerald, EM ;
Bean, CJ .
JOURNAL OF APPLIED GEOPHYSICS, 2001, 48 (04) :183-197
[8]  
Haykin S., 1999, NEURAL NETWORKS COMP, DOI DOI 10.1017/S0269888998214044
[9]  
LAMPINEN J, 1997, P TOOLMET 97 TOOL EN, P28
[10]   Estimation of seismic waveform governing parameters with neural networks [J].
Langer, H ;
Nunnari, G ;
Occhipinti, L .
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 1996, 101 (B9) :20109-20118