Inversion of velocity models using genetic algorithm method with sigmoidal parameterization
DOI:
https://doi.org/10.5433/1679-0375.2022v43n1Espp17Keywords:
Seismic inversion, Genetic algorithm, Ray tracing, Sigmoidal functions, Velocity field parameterizationAbstract
A seismic traveltime inversion method is proposed for building smooth velocity models using traveltime observed on irregular surface. Model parameterization in this study is described by a piecewise constant velocity field on a rectangular grid parameterized by sigmodal functions, which is beneficial for the description of irregular surface with high degree of approximation. The velocity field is defined in the rectangular grid which is used for the description of velocity distribution everywhere in the model sigmoidal interpolation. In addition, we use the simple Genetic Algorithm for the inversion procedure. Through this global scope inversion method, we provide high-resolution estimates of the model parameter and ensure that the results obtained are in accordance with the actual data. Our method is validated with synthetic examples of heterogeneous isotropic media and compared to Simulating Annealing. The inverted velocity models and approximate ray paths obtained coincide well with the trajectories simulated using the seismic ray tracing in synthetic heterogeneous isotropic media.
Downloads
References
CERQUEIRA, A. G.; FIGUEIRÓ, W. M.; CUNHA, P. E. M. Seismic tomography using metropolis method of velocity fields parameterized by Haar wavelet series. Re- vista Brasileira de Geofísica, São Paulo, v. 34, n. 2, p. 251–260, 2016.
CERVENY, V. Seismic Ray Theory. Cambridge: Cam- bridge University Press, 2001.
DATTA, D.; SEN, M. K. Estimating a starting model for full-waveform inversion using a global optimization method. Geophysics, Tulsa, v. 81, n. 4, p. R211–R223, 2016.
DOCHERTY, P.; SILVA, R.; SINGH, S.; SONG, Z.-M.; WOOD, M. Migration velocity analysis using a genetic algorithm. Geophysical Prospecting, Oxford, v. 45, n. 5, p. 865–878, 1997.
ELY, G.; MALCOLM, A.; POLIANNIKOV, O. V. Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method. Geophysics, Tulsa, v. 83, n. 2, p. R63–R75, 2018.
FERREIRA, N. R.; PORSANI, M. J.; OLIVEIRA, S. P. A hybrid genetic-linear algorithm for 2d inversion of sets of vertical electrical sounding. Revista Brasileira de Ge- ofísica, São Paulo, v. 21, p. 235–248, 2003.
HAJIAN, A.; STYLES, P. Application of soft computing and intelligent methods in geophysics. Berlin: Springer, 2018.
HANYGA, A.; SEREDYN´ SKA, M. Ray tracing in elastic and viscoelastic media. Pure and Applied Geophysics, Basel, v. 157, n. 5, p. 679–717, 2000.
JIN, C.; CAO, D.; YIN, X. Joint waveform inversion with the separated upgoing and downgoing wavefields of VSP data. Journal of Geophysics and Engineering, Oxford, v. 17, n. 1, p. 53–64, 2020.
MENKE, W. Geophysical data analysis: discrete inverse theory: MATLAB edition. Cambridge: Academic press, 2012. v. 45.
MOLLEHUARA-CANALES, R.; KOZLOVSKAYA, E.; LUNKKA, J.; MOISIO, K.; PEDRETTI, D. Non-invasivegeophysical imaging and facies analysis in mining tailings. Journal of Applied Geophysics, Amsterdam, v. 192, p. 104402, 2021.
OLIVEIRA, S. P.; AZEVEDO, J. S.; FIGUEIRÓ, W. M.; GUIMARÃES, R. A.; SILVA, W. J.; OLIVEIRA, A. Representation of discontinuous seismic velocity fields by sigmoidal functions for ray tracing and traveltime mod- elling. Geophysical Journal International, Oxford, v. 224, n. 1, p. 435–448, 2021.
RAWLINSON, N.; FICHTNER, A.; SAMBRIDGE, M.; YOUNG, M. K. Seismic tomography and the assessment of uncertainty. Advances in geophysics, New York,, v. 55, p. 1–76, 2014.
REZAIE, M. A sigmoid stabilizing function for fast sparse 3d inversion of magnetic data. Near Surface Geophysics, Houton, v. 18, n. 2, p. 149–159, 2020.
SAJEVA, A.; ALEARDI, M.; STUCCHI, E.; BIENATI, N.; MAZZOTTI, A. Estimation of acoustic macro models using a genetic full-waveform inversion: Applications to the Marmousi model. Geophysics, Tulsa, v. 81, n. 4, p. R173–R184, 2016.
SAMBRIDGE, M.; DRIJKONINGEN, G. Genetic algorithms in seismic waveform inversion. Geophysical Jour- nal International, Oxford, v. 109, n. 2, p. 323–342, 1992.
SAMBRIDGE, M.; MOSEGAARD, K. Monte Carlo methods in geophysical inverse problems. Reviews of Geo- physics, Washington, v. 40, n. 3, p. 3–1, 2002.
SEN, M. K.; STOFFA, P. L. Global optimization methods in geophysical inversion. 2. ed. Cambridge: Cambridge University Press, 2013.
STUART, G. K.; MINKOFF, S. E.; PEREIRA, F. A two-stage Markov chain Monte Carlo method for seismic in- version and uncertainty quantification. Geophysics, Tulsa, v. 84, n. 6, p. R1003–R1020, 2019.
Downloads
Published
How to Cite
Issue
Section
License
The Copyright Declaration for articles published in this journal is the author’s right. Since manuscripts are published in an open access Journal, they are free to use, with their own attributions, in educational and non-commercial applications. The Journal has the right to make, in the original document, changes regarding linguistic norms, orthography, and grammar, with the purpose of ensuring the standard norms of the language and the credibility of the Journal. It will, however, respect the writing style of the authors. When necessary, conceptual changes, corrections, or suggestions will be forwarded to the authors. In such cases, the manuscript shall be subjected to a new evaluation after revision. Responsibility for the opinions expressed in the manuscripts lies entirely with the authors.
This journal is licensed with a license Creative Commons Attribution-NonCommercial 4.0 International.
