Mathematical Geosciences

, Volume 50, Issue 4, pp 477–489 | Cite as

Geostatistical Inversion of Seismic Oceanography Data for Ocean Salinity and Temperature Models

  • Leonardo Azevedo
  • Xinghui Huang
  • Luís M. Pinheiro
  • Rúben Nunes
  • Maria Helena Caeiro
  • Haibin Song
  • Amílcar Soares
Article
  • 192 Downloads

Abstract

Conventional multi-channel seismic reflection data, known as seismic oceanography, has recently been used for the qualitative interpretation of meso- to large-scale hydrographic structures of interest. Seismic oceanography has been successfully imaging oceanographic structures in an intermediate scale not sampled by traditional oceanographic tools, such as conductivity, depth and temperature measurements and eXpendable BathyThermograph (XBT) data. However, few attempts have been made for successfully quantifying ocean properties, such as ocean temperature and salinity, directly from the seismic reflection data. This work presents an iterative geostatistical methodology capable of inverting conventional seismic oceanographic data simultaneously for high-resolution temperature and salinity ocean models. The proposed methodology was developed and implemented in a real set of contemporaneous XBT data and two-dimensional seismic profile acquired southwest of Portugal. The resulting high-resolution temperature and salinity models reproduce existing XBT data not used to constrain the geostatistical inversion, which permits reliable quantification of the ocean properties of interest.

Keywords

Seismic oceanography Geostatistical inversion Ocean properties 

Notes

Acknowledgements

The authors would like to thank CERENA and CESAM for supporting this work and D. Klaeschen and R. Hobbs from the GO project for making available the processed line GO-LR-12 used in this study. The authors also acknowledge the two anonymous reviewers for their valuable suggestions.

References

  1. Azevedo L, Nunes R, Soares A, Neto GS (2013) Stochastic seismic AVO inversion. In: 75th EAGE conference and exhibition, 10–13 June 2013Google Scholar
  2. Azevedo L, Nunes R, Soares A, Neto GS, Guerreiro L (2013) Stochastic direct facies seismic AVO inversion. SEG Annual Meeting, Houston, USGoogle Scholar
  3. Bosch M, Mukerji T, González EF (2010) Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: a review. Geophysics 75(5):75A165.  https://doi.org/10.1190/1.3478209 CrossRefGoogle Scholar
  4. Boschetti F, Dentith MC, List RD (1996) Inversion of seismic refraction data using genetic algorithms. Geophysics 61(6):1715–1727.  https://doi.org/10.1190/1.1444089 CrossRefGoogle Scholar
  5. Buland A, Omre H (2003) Bayesian linearized AVO inversion. Geophysics 68(1):185–198CrossRefGoogle Scholar
  6. Francis AM (2006) Understanding stochastic inversion: part 1. First Break 24:79–84Google Scholar
  7. Grana D, Della Rossa E (2010) Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion. Geophysics 75(3):O21–O37.  https://doi.org/10.1190/1.3386676 CrossRefGoogle Scholar
  8. Hass A, Dubrule O (1994) Geostatistical inversion—a sequential method of stochastic reservoir modelling constrained by seismic data. First Break 12(11):561–569Google Scholar
  9. Hobbs R et al (2007) GO—geophysical oceanography: a new tool to understand the thermal structure and dynamics of oceans. D318 Cruise Rep., Durham Univ., Durham, UKGoogle Scholar
  10. Holbrook WS, Paramo P, Pearse S, Schmitt RW (2003) Thermohaline fine structure in an oceanographic front from seismic reflection profiling. Science 301:821–824CrossRefGoogle Scholar
  11. Horta A, Soares A (2010) Direct sequential co-simulation with joint probability distributions. Math Geosci 42(3):269–92.  https://doi.org/10.1007/s11004-010-9265-x CrossRefGoogle Scholar
  12. Loertzer GJ, Berkhout AJ (1992) An integrated approach to lithologic inversion; part I, theory. Geophysics 57(2):233–244CrossRefGoogle Scholar
  13. Ma X (2002) Simultaneous inversion of prestack seismic data for rock properties using simulated annealing. Geophysics 67(6):1877–1885.  https://doi.org/10.1190/1.1527087 CrossRefGoogle Scholar
  14. Mallick S (1995) Model based inversion of amplitude variations with offset data using a genetic algorithm. Geophysics 60(4):939–954.  https://doi.org/10.1190/1.1443860 CrossRefGoogle Scholar
  15. 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(2):326–36CrossRefGoogle Scholar
  16. Mariethoz G, Renard P, Straubhaar J (2010) The direct sampling method to perform multiple-point geostatistical simulations. Water Resour Res 46(11):1–14.  https://doi.org/10.1029/2008WR007621 Google Scholar
  17. Nunes R, Soares A, Neto GS, Dillon L, Guerreiro L, Caetano H, Maciel C, Leon F (2012) Geostatistical inversion of prestack seismic data. In: Ninth international geostatistics congress, Oslo, Norway, pp 1–8Google Scholar
  18. Papenberg C, Klaeschen D, Krahmann G, Hobbs RW (2010) Ocean temperature and salinity inverted from combined hydrographic and seismic data. Geophys Res Lett 37:L04601CrossRefGoogle Scholar
  19. Pinheiro LM, Song H, Ruddick B, Dubert J, Ambar I, Mustafa K, Bezerra R (2010) Detailed 2-D imaging of the mediterranean outflow and meddies off W Iberia from multichannel seismic data. J Mar Syst 79:89–100CrossRefGoogle Scholar
  20. Ruddick B, Song HB, Dong CZ, Pinheiro L (2009) Water column seismic images as maps of temperature gradient. Oceanography 22(1):192–205CrossRefGoogle Scholar
  21. Sen MK, Stoffa PL (1991) Nonlinear one dimensional seismic waveform inversion using simulated annealing. Geophysics 56(10):1624–1638.  https://doi.org/10.1190/1.1442973 CrossRefGoogle Scholar
  22. Soares A (2001) Direct sequential simulation and cosimulation. Math Geol 33(8):911–926CrossRefGoogle Scholar
  23. Soares A, Diet JD, Guerreiro L (2007) Stochastic inversion with a global perturbation method. In: Petroleum geostatistics, EAGE, Cascais, Portugal, 10–14 September 2007Google Scholar
  24. Strebelle S (2002) Conditional simulation of complex geological structures using multiple-point statistics. Math Geol 34(1):1–21CrossRefGoogle Scholar
  25. Tarantola A (2005) Inverse problem theory. SIAM, PhiladelphiaGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2018

Authors and Affiliations

  1. 1.CERENA, DECivil, Instituto Superior TécnicoLisbon UniversityLisbonPortugal
  2. 2.Graduate University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.Departamento de Geociências and CESAMUniversidade de AveiroAveiroPortugal
  4. 4.Partex Oil and GasLisbonPortugal
  5. 5.Geophysical InstituteChinese Academy of SciencesBeijingPeople’s Republic of China

Personalised recommendations