Citation: | Gangding Feng, Chao Chen. Nonlinear Inversion of Potential-Field Data Using an Improved Genetic Algorithm. Journal of Earth Science, 2004, 15(4): 420-424. |
The genetic algorithm is useful for solving an inversion of complex nonlinear geophysical equations. The multi-point search of the genetic algorithm makes it easier to find a globally optimal solution and avoid falling into a local extremum. The search efficiency of the genetic algorithm is a key to producing successful solutions in a huge multi-parameter model space. The encoding mechanism of the genetic algorithm affects the searching processes in the evolution. Not all genetic operations perform perfectly in a search under either a binary or decimal encoding system. As such, a standard genetic algorithm (SGA) is sometimes unable to resolve an optimization problem such as a simple geophysical inversion. With the binary encoding system the operation of the crossover may produce more new individuals. The decimal encoding system, on the other hand, makes the mutation generate more new genes. This paper discusses approaches of exploiting the search potentials of genetic operations with different encoding systems and presents a hybrid-encoding mechanism for the genetic algorithm. This is referred to as the hybrid-encoding genetic algorithm (HEGA). The method is based on the routine in which the mutation operation is executed in decimal code and other operations in binary code. HEGA guarantees the birth of better genes by mutation processing with a high probability, so that it is beneficial for resolving the inversions of complicated problems. Synthetic and real-world examples demonstrate the advantages of using HEGA in the inversion of potential-field data.
Berg, E., 1990. Simple Convergent Genetic Algorithm for Inversion of Multiparameter Data. 60th Ann. Internat. Mtg., Soc. of Expl. Geophys., 1126-1128 |
Berg, E., 1991. Convergent Genetic Algorithm for Inversion. 61st Ann. Internat. Mtg., Soc. of Expl. Geophys., 948 -950 |
Curtis, A., Snieder, R., 1997. Reconditioning Inverse Problems Using the Genetic Algorithm and Revised Parameterization. Geophysics, 62: 1524-1532 doi: 10.1190/1.1444255 |
Docherty, P., Silva, R., Singh, S., et al., 1997. Migration Velocity Analysis Using a Genetic Algorithm. Geophys. Prosp., 45: 865-878 doi: 10.1046/j.1365-2478.1997.640298.x |
Farrell, S.M., Jessell, M.W., Barr, T.D., 1996. Inversion of Geological and Geophysical Data Sets Using Genetic Algorithms. 66th Ann. Internat. Mtg., Soc. of Expl. Geophys., 1404-1406 |
Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Publishing Company |
He, Q., Fu, D., 1999, The Improvement of Genetic Algorithm and Its Applications for the Inversion of Orthorhombic Anisotropic Media. 69th Ann. Internat. Mtg., Soc. of Expl. Geophys., Houston, 1791-1792 |
Holland, J.H., 1975. Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, Michigan |
Mallick, S., 1999. Some Practical Aspects of Prestack Waveform Inversion Using a Genetic Algorithm: An Example from the East Texas Woodbine Gas Sand. Geophysics, 64: 326-336 doi: 10.1190/1.1444538 |
Porsani, M., Ursin, B., 2000. Deconvolution and Wavelet Estimation by Using a Genetic Algorithm. 70th Ann. Internat. Mtg., Soc. of Expl. Geophys., Calgary, 2185-2188 |
Roth, M., Holliger, K., 1998. Joint Inversion of Rayleigh and Guided Waves in High-Resolution Seismic Data Using a Genetic Algorithm. 68th Ann. Internat. Mtg., Soc. of Expl. Geophys., New Orleans, 1570-1573 |
Schraudolph, N.N., Belew, R.K., 1992. Dynamic Parameter Encoding for Genetic Algorithm. Machine Learning, 9 (9): 9-21 |
Talwani, M.J., Worzel, L., Landisman, M., 1959. Rapid Gravity Computations for Two-Dimensional Bodies with Application to the Mendocino Submarine Fracture Zone. J. Geophys. Res., 64(1): 49-59 doi: 10.1029/JZ064i001p00049 |
Zhang, X., Fang, H., Dai, G., 1997. Study on Encoding Mechanism of Genetic Algorithm. Information and Control, 26: 134-139 |