1887
ASEG2001 - 15th Geophysical Conference
  • ISSN: 2202-0586
  • E-ISSN:

Abstract

The neighbourhood algorithm (NA) is a recently proposed direct search (i.e. derivative free) approach to nonlinear inversion which is finding increasing numbers of applications from problems in earthquake seismology to production uncertainty quantification in oil reservoirs.

An inverse problem occurs whenever data only indirectly constrain some physical, chemical or parameters of interest. For example when seismic data, collected at the Earth’s surface is used to constrain structure at depth. Inverse problems occur in many areas of the physical and mathematical sciences. The NA is applicable in cases where the relationship between unknowns and observations is highly nonlinear and simple derivative calculations are undesirable, or impossible. NA is in the same class of technique as Genetic Algorithms (GA) and Simulated Annealing (SA), which are often associated with global optimization. The NA makes use of simple geometrical concepts, and requires just two tuning parameters, but has been shown to produce a sophisticated ‘self-adaptive’ search behaviour in multi-dimensional parameter spaces. It also allows a fully nonlinear estimation of uncertainty in unknowns arising from noise, or other uncertainties, in the data.

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2001-12-01
2026-01-19
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References

  1. Davis, L. 1987. Genetic algorithms and simulated annealing, Research notes in Artificial Intelligence, Pitman, London.
  2. Kennett, B. L. N., Marson-Pidgeon, K., and Sambridge, M., 2000. Seismic Source Characterisation using a Neighbourhood Algorithm. Geophys. Res. Lett., 27 No. 20., 3401-3404.
  3. Marson-Pidgeon, K., Kennett, B. L. N., Sambridge, M., 2001. Source depth and mechanism inversion at teleseismic distances, using a neighbourhood algorithm. Bull, seism. Soc. Am., in press.
  4. Resovsky, J. 2000. Model Space Mapping Quantifies Uncertainty in 3D Density Models, EOS, 81, F823.
  5. Sambridge, M. (1999a). Geophysical Inversion with a Neighbourhood Algorithm I: searching a parameter space, Geophys. J. Int.,138, 479-494.
  6. Sambridge, M. (1999b). Geophysical Inversion with a Neighbourhood Algorithm II: appraising the ensemble, Geophys. J. Int.138, 727-746.
  7. Sambridge, M., and Kennett, B. L. N., 2001. Seismic event location: Nonlinear inversion using a Neighbourhood Algorithm, Pageoph., in press.
  8. Snoke, J. A., and Sambridge M., 2001. Constraints on the S-wave Velocity Structure in a Continental Shield From Surface-Wave Data: Comparing Non-linear Least-Squares Inversion and the Direct-Search Neighborhood Algorithm, J. Geophys. Res., submitted.
  9. Vallee, M., and Bouchon, M. P., 2000. Far Field Study of the Izmit Earthquake Kinematics, EOS, 81, F817.
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  • Article Type: Research Article
Keyword(s): direct search method; Nonlinear inversion; uncertainty quantification
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