1887
Volume 63, Issue 5
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

To analyse and invert refraction seismic travel time data, different approaches and techniques have been proposed. One common approach is to invert first‐break travel times employing local optimization approaches. However, these approaches result in a single velocity model, and it is difficult to assess the quality and to quantify uncertainties and non‐uniqueness of the found solution. To address these problems, we propose an inversion strategy relying on a global optimization approach known as particle swarm optimization. With this approach we generate an ensemble of acceptable velocity models, i.e., models explaining our data equally well. We test and evaluate our approach using synthetic seismic travel times and field data collected across a creeping hillslope in the Austrian Alps. Our synthetic study mimics a layered near‐surface environment, including a sharp velocity increase with depth and complex refractor topography. Analysing the generated ensemble of acceptable solutions using different statistical measures demonstrates that our inversion strategy is able to reconstruct the input velocity model, including reasonable, quantitative estimates of uncertainty. Our field data set is inverted, employing the same strategy, and we further compare our results with the velocity model obtained by a standard local optimization approach and the information from a nearby borehole. This comparison shows that both inversion strategies result in geologically reasonable models (in agreement with the borehole information). However, analysing the model variability of the ensemble generated using our global approach indicates that the result of the local optimization approach is part of this model ensemble. Our results show the benefit of employing a global inversion strategy to generate near‐surface velocity models from refraction seismic data sets, especially in cases where no detailed information regarding subsurface structures and velocity variations is available.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12240
2015-04-06
2024-03-29
Loading full text...

Full text loading...

References

  1. BoschettiF., DentithM.C. and ListR.D.1996. Inversion of seismic refraction data using genetic algorithms. Geophysics61, 1715–1727.
    [Google Scholar]
  2. BrattonD. and KennedyJ.2007. Defining a standard for particle swarm optimization. In IEEE Swarm Intelligence Symposium.
  3. DrijkoningenG.G. and WhiteR.S.1995. Seismic velocity structure of oceanic crust by inversion using genetic algorithms. Geophysical Journal International123, 653–664.
    [Google Scholar]
  4. EberhartR.C. and ShiY.2000. Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation, 84–88.
    [Google Scholar]
  5. ElbeltagiE., HegazyT. and GriersonD.2005. Comparison among five evolutionary‐based optimization algorithms. Advanced Engineering Informatics19, 43–53.
    [Google Scholar]
  6. Fernández MartínezJ.L., García GonzaloE., Fernández ÁlvarezJ.P., KuzmaH.A. and Menéndez PérezC.O.2010. PSO: a powerful algorithm to solve geophysical inverse problems: application to a 1D‐DC resistivity case. Journal of Applied Geophysics71, 13–25.
    [Google Scholar]
  7. FomelS.1997. A variational formulation of the fast marching eikonal solver. SEP‐95: Stanford Exploration Project.
    [Google Scholar]
  8. HagedoornJ.G.1959. The plus‐minus method of interpreting seismic refraction sections. Geophysical Prospecting7(2), 158–182.
    [Google Scholar]
  9. HamannG. and TronickeJ.2014. Global inversion of GPR traveltimes to assess uncertainties in CMP velocity models. Near Surface Geophysics12(4), 505–514.
    [Google Scholar]
  10. HampsonD. and RussellB.1984. First‐break interpretation using generalized linear inversion. Journal of the Canadian Society of Exploration Geophysicists, 20(1), 40–54.
    [Google Scholar]
  11. IvanovJ., MillerR.XiaJ., SteeplesD. and ParkC.2005. The inverse problem of Refraction travel times, part I: Types of geophysical nonuniqueness through minimization. Pure and Applied Geophysics162, 447–459.
    [Google Scholar]
  12. IyerH.
    and HiraharaK. , eds. 1993. Seismic Tomography: Theory and Practice. Chapman & Hall. ISBN 0412371901.
    [Google Scholar]
  13. KennedyJ. and EberhartR.C.1995. Particle swarm optimization. Proceedings of the IEEE International Joint Conference on Neural Networks, 1942–1948.
    [Google Scholar]
  14. KnödelK., LangeG. and VoigtH.J.2007. Environmental Geology: Handbook of Field Methods and Case Studies. Springer‐Verlag. ISBN 3540746692.
    [Google Scholar]
  15. PalmerD.1981. An introduction to the generalized reciprocal method of seismic refraction interpretation. Geophysics46(11), 1508–1518.
    [Google Scholar]
  16. PalmerD.2010a. Non‐uniqueness with refraction inversion–the Mt Bulga shear zone. Geophysical Prospecting58(4), 561–575.
    [Google Scholar]
  17. PalmerD.2010b. Non‐uniqueness with refraction inversion–a syncline model study. Geophysical Prospecting58(2), 203–218.
    [Google Scholar]
  18. PullammanappallilS. and LouieJ.1993. Inversion of seismic reflection traveltimes using a nonlinear optimization scheme. Geophysics58, 1607–1620.
    [Google Scholar]
  19. PullammanappallilS. and LouieJ.1994. A generalized simulated‐annealing optimization for inversion of first‐arrival times. Bulletin of the Seismological Society of America84(5), 1397–1409.
    [Google Scholar]
  20. RoyL., SenM.K., McIntoshK., StoffaP.L. and NakamuraY.2005. Joint inversion of first arrival seismic travel‐time and gravity data. Journal of Geophysics and Engineering2, 277–289.
    [Google Scholar]
  21. RumpfM., BönigerU. and TronickeJ.2012. Refraction seismics to investigate a creeping hillslope in the Austrian Alps. Engineering Geology151, 37–46.
    [Google Scholar]
  22. RumpfM. and TronickeJ.2014. Predicting 2D geotechnical parameter fields in near‐surface sedimentary environments. Journal of Applied Geophysics101, 95–107.
    [Google Scholar]
  23. SambridgeM. and MosegaardK.2002. Monte Carlo methods in geophysical inverse problems. Reviews of Geophysics40, 3.1–3.29.
    [Google Scholar]
  24. SenM.K. and StoffaP.L.1995. Global optimization methods in geophysical inversion. Elsevier Science B.V.
    [Google Scholar]
  25. SethianJ.A.1996. A fast marching level set method for monotonically advancing fronts. Proceedings of the National Academy of USA93, 1591–1595.
    [Google Scholar]
  26. SethianJ.A. and PopoviciA.M.1999. 3‐D traveltime computation using the fast marching method. Geophysics64, 516–523.
    [Google Scholar]
  27. TronickeJ., PaascheH., and BönigerU.2011. Joint global inversion of GPR and P‐wave seismic traveltimes using particle swarm optimization. 6th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), Expanded Abstracts, 4 pp.
  28. TronickeJ.PaascheH. and BönigerU.2012. Crosshole traveltime tomography using particle swarm optimization: a near‐surface field example. Geophysics77(1), R19–R32.
    [Google Scholar]
  29. Van den BerghF.2002. An analysis of particle swarm optimizers. PhD thesis, University of Pretoria, South Africa.
    [Google Scholar]
  30. WeberZ.2000. Seismic traveltime tomography: a simulated annealing approach. Physics of the Earth and Planetary Interiors119, 149–159.
    [Google Scholar]
  31. WienhöferJ., LindenmaierF. and ZeheE.2011. Challenges in understanding the hydrologic controls on the mobility of slow‐moving landslides. Vadose Zone Journal9, 1–16.
    [Google Scholar]
  32. ZeltC., HainesS., PowersM.SheehanJ., RohdewaldS., LinkC.et al. 2013. Blind test of methods for obtaining 2‐D near‐surface seismic velocity models from first‐arrival traveltimes. Journal of Environmental & Engineering Geophysics, 18(3), 183–194.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12240
Loading
/content/journals/10.1111/1365-2478.12240
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Inversion; Seismic refraction; Uncertainty

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error