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Inverse Theory, Monte Carlo Method

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Encyclopedia of Solid Earth Geophysics

Part of the book series: Encyclopedia of Earth Sciences Series ((EESS))

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Definition

Monte Carlo method. A computational technique making use of random numbers to solve problems that are either probabilistic or deterministic in nature. Named after the famous Casino in Monaco.

Monte Carlo inversion method. A method for sampling a parameter space of variables representing unknowns, governed by probabilistic rules.

Markov chain Monte Carlo (McMC). A probabilistic method for generating vectors or parameter variables whose values follow a prescribed density function.

Introduction

Because geophysical observations are made at (or very near) the Earth’s surface, all knowledge of the Earth’s interior is based on indirect inference. There always exists an inverse problem where models of physical properties are sought at depth that are only indirectly constrained by the available observations made at the surface. Geophysicists have been dealing with such problems for many years and in doing so have made substantial contributions to the understanding of inverse problems.

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Bibliography

  • Aster R, Borchers R, Thurber CH (2005) Parameter estimation and inverse problems. International Geophysics Series, vol 90. Elsevier, Amsterdam

    Google Scholar 

  • Backus GE, Gilbert JF (1967) Numerical applications of a formalism for geophysical inverse problems. Geophys J R Astron Soc 13:247–276

    Article  Google Scholar 

  • Backus GE, Gilbert JF (1968) The resolving power of gross Earth data. Geophys J R Astron Soc 16:169–205

    Article  Google Scholar 

  • Backus GE, Gilbert JF (1970) Uniqueness in the inversion of inaccurate gross Earth data. Philos Trans R Soc Lond A 266:123–192

    Article  Google Scholar 

  • Bernardo JM, Smith AFM (1994) Bayesian theory. Wiley, Chichester

    Book  Google Scholar 

  • Bodin T, Sambridge M (2009) Seismic tomography with the reversible jump algorithm. Geophys J Int 178:1411–1436

    Article  Google Scholar 

  • Charvin K, Gallagher K, Hampson G, Labourdette R (2009) A Bayesian approach to infer environmental parameters from stratigraphic data 1: methodology. Basin Res 21:5–25

    Article  Google Scholar 

  • Duijndam AJW (1988a) Bayesian estimation in seismic inversion part I: principles. Geophys Prospect 36:878–898

    Article  Google Scholar 

  • Duijndam AJW (1988b) Bayesian estimation in seismic inversion part II: uncertainty analysis. Geophys Prospect 36:899–918

    Article  Google Scholar 

  • Gallagher K, Charvin K, Nielsen S, Sambridge M, Stephenson J (2009) Markov chain Monte Carlo (McMC) sampling methods to determine optimal models, model resolution and model choice for Earth science problems. Mar Pet Geol 26:525–535

    Article  Google Scholar 

  • Geyer CJ, Møller J (1994) Simulation procedures and likelihood inference for spatial point processes. Scand J Stat 21:369–373

    Google Scholar 

  • Gilks WR, Richardson S, Spiegalhalter DJ (1996) Markov chain Monte Carlo in practice. Chapman & Hall, London

    Google Scholar 

  • Green PJ (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82:711–732

    Article  Google Scholar 

  • Green PJ (2003) Chapter 6: Trans-dimensional McMC. In: Green PJ, Hjort N, Richardson S (eds) Highly structured stochastic systems. Oxford statistical sciences series. Oxford University Press, Oxford, pp 179–196

    Google Scholar 

  • Hammersley JM, Handscomb DC (1964) Monte Carlo methods. Chapman & Hall, London

    Book  Google Scholar 

  • Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57:97–109

    Article  Google Scholar 

  • Hopcroft P, Gallagher K, Pain CC (2009) A Bayesian partition modelling approach to resolve spatial variability in climate records from borehole temperature inversion. Geophys J Int 178:651–666

    Article  Google Scholar 

  • Jasra A, Stephens DA, Gallagher K, Holmes CC (2006) Analysis of geochronological data with measurement error using Bayesian mixtures. Math Geol 38:269–300

    Article  Google Scholar 

  • Kennett BLN, Brown DJ, Sambridge M, Tarlowski C (2003) Signal parameter estimation for sparse arrays. Bull Seismol Soc Am 93:1765–1772

    Article  Google Scholar 

  • Lee PM (1989) Bayesian statistics: an introduction. Edward Arnold, New York/Toronto

    Google Scholar 

  • Malinverno A (2002) Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem. Geophys J Int 151:675–688

    Article  Google Scholar 

  • Malinverno A, Parker RL (2005) Two ways to quantify uncertainty in geophysical inverse problems. Geophysics 71:15–27

    Article  Google Scholar 

  • Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equations of state calculations by fast computing machines. J Chem Phys 21:1087–1092

    Article  Google Scholar 

  • Mosegaard K, Sambridge M (2002) Monte Carlo analysis of inverse problems. Inverse Probl 18:R29–R54

    Article  Google Scholar 

  • Mosegaard K, Tarantola A (1995) Monte Carlo sampling of solutions to inverse problems. J Geophys Res 100:12431–12447

    Article  Google Scholar 

  • Sambridge M (1999) Geophysical inversion with a neighbourhood algorithm – I. Searching a parameter space. Geophys J Int 138:479–494

    Article  Google Scholar 

  • Sambridge M, Mosegaard K (2002) Monte Carlo methods in geophysical inverse problems. Rev Geophys 40:3.1–3.29

    Article  Google Scholar 

  • Sambridge M, Gallagher K, Jackson A, Rickwood P (2006) Trans-dimensional inverse problems, model comparison and the evidence. Geophys J Int 167:528–542

    Article  Google Scholar 

  • Smith AFM (1991) Bayesian computational methods. Philos Trans R Soc Lond A 337:369–386

    Article  Google Scholar 

  • Smith AFM, Roberts GO (1993) Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. J R Stat Soc Ser B 55:3–23

    Google Scholar 

  • Tarantola A, Valette B (1982) Inverse problems = quest for information. J Geophys 50:159–170

    Google Scholar 

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Acknowledgments

We would like to thank FAST (French-Australia Science and Technology exchange program) for their support during the preparation of this entry. This project is supported by the Commonwealth of Australia under the International Science Linkages program.

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Correspondence to Malcolm Sambridge .

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Sambridge, M., Gallagher, K. (2020). Inverse Theory, Monte Carlo Method. In: Gupta, H. (eds) Encyclopedia of Solid Earth Geophysics. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-10475-7_192-1

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  • DOI: https://doi.org/10.1007/978-3-030-10475-7_192-1

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