An Application of Genetic Algorithms in Seismic Refraction Interpretation

Authors

  • Chaiwat LERSVIRIYANANTAKUL Innovation in Physics and Intellectual Properties Research Unit, Prince of Songkla University, Hat Yai, Songkhla 90112
  • Pattara AIYARAK Innovation in Physics and Intellectual Properties Research Unit, Prince of Songkla University, Hat Yai, Songkhla 90112
  • Warawutti LOHAWIJARN 2Geophysics Research Unit, Department of Physics, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112

Keywords:

Genetic algorithms, seismic refraction, velocity inversion, travel time inversion

Abstract

Seismic refraction is a conventional geophysical method for determining geological structures by making use of velocity differences of propagating waves in various rock types. Data interpretation to determine the structures are based on travel time and distance from the source to each receiver (geophone) presented in the form of a t-x graph. In addition to the many conventional methods, our paper evaluates here a Genetic Algorithm (GA) for the interpretation of seismic refraction data. The studied subsurface structures were 2-layer and 3-layer models with horizontal and dipping planar interfaces. Actual field tests were also used for the evaluation. Comparing the GA interpretation technique with the conventional Seismic Interpretation Program (SIP), the results showed that the GA can process seismic refraction data well and gives virtually exact solutions for the synthetic structures. On actual field data, the results are quite similar to those from SIP.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

D Palmer. A New Direction for shallow refraction seismology: integrating amplitudes and traveltimes with the refraction convolution section. Geophys. Prospect. 2001; 49, 657-73.

J Holland. Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975, p. 1.

Z Michalewiz, ZC Janikow and BJ Krawczyk. A modified genetic algorithm for optimal control problem. Comput. Math. Appl. 1992; 23, 83-94.

A Homaifar, S Guan and GE Liepins. Schema analysis of the traveling salesman, problem using genetic algorithms. Complex Syst. 1992; 6, 183-217.

D Abramson and J Abela. 1992, A parallel genetic algorithm for solving the school timetabling problem. In: Conference of the 15th Australian Computer Science, Hobart, Australia. 1992, p. 1-11.

JM Ahuaczin, EG Talbi, P Bessiere and E Mazer. Using genetic algorithms for robot motion planning. In: Proceedings of the 10th European Conference on Artificial Intelligence, Vienna, Austria. 1992, p. 671-5.

S Forrest, B Javornik, RE Smith and AS Perelson. Using genetic algorithm to explore pattern recognition in immune system. Evol. Comput. 1993; 1, 191-211.

TDM Purdin and G Harris. A genetic algorithm approach to solving crossword puzzles. In: Proceedings of the 1993 ACM/SIGAPP symposium on applied computing: states of the art and practice, Indiana, United States. 1993, p. 263-70.

VV Savchenko and LM Schmitt. Reconstruting occlusal surface of teeth using a genetic algorithm with simulated annealing type selection. In: Proceedings of the 6th ACM Symposium on Solid Modeling and Applications, Michigan, United States. 2001, p. 39-46.

M Aurnhammer and KD Tonnies. A genetic algorithm for automated horizon correlation across faults in seismic images. IEEE Trans. Evol. Comput. 2005; 9, 201-10.

DA Coley. An Introduction to Genetic Algorithms for Scientists and Engineers, World Scientific Publishing, Singapore, 1999, p. 2.

HJ Stephen. Interpretation of split-spread refraction data in terms of plane dipping layers. Geophysics 1976; 41, 418-24.

Downloads

Published

2011-11-16

How to Cite

LERSVIRIYANANTAKUL, C., AIYARAK, P., & LOHAWIJARN, W. (2011). An Application of Genetic Algorithms in Seismic Refraction Interpretation. Walailak Journal of Science and Technology (WJST), 3(2), 145–165. Retrieved from https://wjst.wu.ac.th/index.php/wjst/article/view/134

Issue

Section

Research Article