1D inversion of frequency-domain marine controlled-source electromagnetic data using a parallelized real-coded genetic algorithm
- Mohit Ayani (University of Wyoming) | Subhashis Mallick (University of Wyoming) | Lucy MacGregor (Rock Solid Images)
- Document ID
- Society of Exploration Geophysicists
- 2018 SEG International Exposition and Annual Meeting, 14-19 October, Anaheim, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Exploration Geophysicists
- Electrical/resistivity, Isotropic, CSEM, Inversion, Frequency-domain
- 3 in the last 30 days
- 17 since 2007
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We carry out the inversion of marine controlled-source electromagnetic data using real coded genetic algorithm to estimate the isotropic resistivity. Unlike linearized inversion methods, genetic algorithms belonging to class of stochastic methods are not limited by the requirement of the good starting models. The objective function to be optimized contains data misfit and model roughness. The regularization weight is used as a temperature like annealing parameter. This inversion is cast into a Bayesian framework where the prior distribution of the model parameters is combined with the physics of the forward problem to estimate the aposteriori probability density function in the model space. The probability distribution derived with this approach can be used to quantify the uncertainty in the estimation of vertical resistivity profile. We apply our inversion scheme on three synthetic data sets generated from horizontally stratified earth models. For all cases, our inversion estimated the resistivity to a reasonable accuracy. The results obtained from this inversion can serve as starting models for linearized/higher dimensional inversion.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: Poster Station 13
Presentation Type: Poster
|File Size||568 KB||Number of Pages||5|
Ayani,M.,S.Mallick,J.Hunziker, andL.MacGregor,2017,Inversion of frequency-domain marine- controlled-source electromagnetic data using genetic algorithm:87th Annual International Meeting, SEG,Expanded Abstracts,1252–1256,10.1190/segam2017-17783322.1.
Christensen,N., andK.Dodds,2007,1D inversion and resolution analysis of marine CSEM data:Geophysics,72,no.2,WA27–WA38,10.1190/1.2437092.
Constable,S. C.,R. L.Parker, andC. G.Constable,1987,Occam’s inversion: A practical algorithm for generating smooth models from EM sounding data:Geophysics,52,289–300,10.1190/1.1442303.
Key,K.,2009,1D inversion of multicomponent, multifrequency marine CSEM data: Methodology and synthetic studies for resolving thin resistive layers:Geophysics,74,no.2,F9–F20,10.1190/1.3058434.
Li,T., andS.Mallick,2015,Multicomponent, multi-azimuth pre-stack seismic waveform inversion for azimuthally anisotropic media using a parallel and computationally efficient non-dominated sorting genetic algorithm:Geophysical Journal International,200,1134–1152,10.1093/gji/ggu445.
Mallick,S.,1995,Model-based inversion of amplitude-variations-with offset data using a genetic algorithm:Geophysics,60,939–954,10.1190/1.1443860.
Mallick,S.,1999,Some practical aspects of prestack waveform inversion using a genetic algorithm: An example from the east Texas Woodbine gas sand:Geophysics,64,326–336,10.1190/1.1444538.
Padhi,A., andS.Mallick,2013.Multicomponent prestack seismic waveform inversion in transversely isotropic media using a non-dominated sorting genetic algorithm:Geophysical Journal International,196,1600–1618,10.1093/gji/ggt460.
Sen,M. K., andR.Biswas,2015,Choice of regularization weight in basis pursuit reflectivity inversion:Journal of Geophysics and Engineering,12,70–79,10.1088/1742-2132/12/1/70.
Sen,M. K., andP. L.Stoffa,1992,Rapid sampling of model space using genetic algorithms: examples from seismic waveform inversions:Geophysical Journal International,108,281–292,10.1111/j.1365-246X.1992.tb00857.x.
Sen,M. K., andP. L.Stoffa,2013,Global optimization methods in geophysical inversion:Cambridge University Press,10.1017/cbo9780511997570.
Stoffa,P. L., andM. K.Sen,1991,Nonlinear multiparameter optimizations using genetic algorithms: inversion of plane- wave seismograms:Geophysics,56,1794–1810,10.1190/1.1442992.
Wheelock,B.,S.Constable, andK.Key,2015,The advantages of logarithmically scaled data for electromagnetic inversion:Geophysical Journal International,201,1765–1780,10.1093/gji/ggv107.