Articles | Volume 33, issue 2
https://doi.org/10.5194/npg-33-173-2026
https://doi.org/10.5194/npg-33-173-2026
Research article
 | 
20 Apr 2026
Research article |  | 20 Apr 2026

Bayesian inference based on algorithms: MH, HMC, MALA and Lip-MALA for prestack seismic inversion

Richard Perez-Roa, Saba Infante, Gabriel Barragan, and Raul Manzanilla

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This preprint is open for discussion and under review for Geoscientific Instrumentation, Methods and Data Systems (GI).
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Cited articles

Aki, K. and Richards, P.: Quantitative Seismology, University Science Books, ISBN 978-0935702965, 2002. 
Beskos, A., Pillai, N. S., Roberts, G. O., Sanz-Serna, J.-M., and Stuart, A. M.: Optimal tuning of the hybrid Monte Carlo algorithm, Bernoulli, 19, 1501–1534, https://doi.org/10.3150/12-BEJ414, 2013. 
Bosch, M.: The optimization approach to lithological tomography: Combining seismic data and petrophysics for porosity prediction, Geophysics, 69, 1272–1282, https://doi.org/10.1190/1.1801944, 2004. 
Bosch, M., Cara, L., Rodrigues, J., Navarro, A., and Díaz, M.: A Monte Carlo approach to the joint estimation of reservoir and elastic parameters from seismic amplitudes, Geophysics, 72, O29–O39, https://doi.org/10.1190/1.2783766, 2007. 
Buland, A. and Omre, H.: Bayesian linearized AVO inversion, Geophysics, 68, 185–198, https://doi.org/10.1190/1.1543206, 2003. 
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We explored four methods to improve how underground rock properties are estimated from seismic data. By comparing these methods on both simulated and real-world oilfield data, we found that techniques using gradient information give better accuracy but require more computing time. Our results help guide the choice of method depending on whether speed or precision is more important in subsurface exploration.
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