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Estimation of lateral correlation length from deep seismic refection profle based on stochastic model

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The fractal nature of crustal seismic velocity heterogeneity makes the interpretation of deep seismic refection data difcult by conventional methods developed in oil and gas exploration. Thus, several statistical approaches have been introduced as promising tools for interpreting the complex refection patterns of deep seismic data. Because stochastic models have been successfully used to describe the heterogeneity of crustal rocks, stochastic parameter estimation has become a potentially powerful tool for recovering information on geometric geological variations. However, there are many factors that infuence parameter estimation, with limited data being a profound one. We present a novel algorithm to estimate the lateral correlation length, an important stochastic parameter, from deep seismic refection data. First, an autoregressive power spectrum-based method was introduced to calculate the autocorrelation function from limited data. Second, the average multi-trace 1D autocorrelation function was used to replace the 2D autocorrelation function to improve the computation efciency, accuracy, and stability. Compared with other algorithms, a velocity model test showed that our method exhibited signifcantly better performance for a small dataset. Then, an appropriately sized sliding window of synthetic seismic data was applied to map the relative variations of lateral stochastic parameters. The results indicated that our method could distinguish the lateral variations in stochastic parameters as well as vertical changes. Finally, the geological meaning of diferent seismic refection patterns was discussed after applying our methods to deep seismic refection feld data. The results demonstrated that lateral correlation can clearly identify Moho discontinuity, crustal refections, and some sedimentary structures.
Czasopismo
Rocznik
Strony
1297--1312
Opis fizyczny
Bibliogr. 31 poz.
Twórcy
autor
  • China University of Geosciences Beijing, Beijing, China
  • Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, 84 Jinguang Road, Langfang, China
autor
  • Chinese Academy of Geological Sciences, Xicheng District, Beijing, China
autor
  • Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, 84 Jinguang Road, Langfang, China
autor
  • Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, 84 Jinguang Road, Langfang, China
autor
  • Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, 84 Jinguang Road, Langfang, China
Bibliografia
  • 1. Bean CJ, Marsan D, Martini F (1999) Statistical measures of crustal heterogeneity from reflection seismic data: the role of seismic bandwidth. Geophys Res Lett 26(21):3241–3244
  • 2. Buffett GG, Hurich CA, Vsemirnova EA, Hobbs RW, Sallarès V, Carbonell R, Klaeschen D, Biescas B (2010) Stochastic heterogeneity mapping around a mediterranean salt lens. Ocean Sci 6(1):423–429
  • 3. Carpentier SFA, Roy-Chowdhury K (2009) Conservation of lateral stochastic structure of a medium in its simulated seismic response. J Geophys Res 114(B10). https://doi.org/10.1029/2008JB006123
  • 4. Carpentier SFA, Roy-Chowdhury K, Hurich CA (2011) Mapping correlation lengths of lower crustal heterogeneities together with their maximum-likelihood uncertainties. Tectonophysics 508(1–4):117–130
  • 5. Carpentier S, Roy Chowdhury K (2007) Underestimation of scale lengths in stochastic fields and their seismic response: a quantification exercise. Geophys J Int 169(2):547–562. https://doi.org/10.1111/j.1365-246X.2007.03333.x
  • 6. Carpentier S, Roy-Chowdhury K, Stephenson RA, Stovba S (2009) Delineating tectonic units beneath the Donbas Fold Belt using scale lengths estimated from DOBRE 2000/2001 deep reflection data. J Geophys Res Atmos 114(B10). https://doi.org/10.1029/2008JB006124
  • 7. Clayton RW, Frankel A (1986) Finite difference simulations of seismic scattering implications for the propagation of short-period seismic waves in the crust and models of crustal heterogeneity. J Geophys Res 91(B6):6465–6489
  • 8. Eneva M (1996) Effect of limited data sets in evaluating the scaling properties of spatially distributed data: an example from mining-induced seismic activity. Geophys J Int 124(3):773–786
  • 9. Frenje L, Juhlin C (2000) Scattering attenuation: 2-D and 3-D finite difference simulations vs. theory. J Appl Geophys 44(1):33–46
  • 10. Goff JA, Jordan TH (1988) Stochastic modeling of seafloor morphology: inversion of sea beam data for second-order statistics. J Geophys Res Part B Solid Earth 11:13589–13608
  • 11. Goff JA, Levander A (1995) Incorporating “sinuous connectivity” into stochastic models of crustal heterogeneity: Examples from the Lewisian gneiss complex Scotland the Franciscan formation California and the Hafafit Gneiss Complex Egypt. J Geophys Res Solid Earth 101(B4):8489–8501. https://doi.org/10.1029/96JB00110
  • 12. Goff JA, Levander A (1996) Incorporating “sinuous connectivity” into stochastic models of crustal heterogeneity: examples from the Lewisian gneiss complex Scotland, the Franciscan formation, California, and the Hafafit gneiss complex, Egypt. J Geophys Res 101(B4):8489–8501
  • 13. Goff JA, Holliger K, Levander A (1994) Modal fields: a new method for characterization of random seismic velocity heterogeneity. Geophys Res Lett 21(6):493–496
  • 14. Holliger K, Levander AR (1992) A stochastic view of lower crustal fabric based on evidence from the Ivrea Zone. Geophys Res Lett 19(11):1153–1156
  • 15. Holliger K, Levander A (1994) Structure and seismic response of extended continental crust: stochastic analysis of the Strona-Ceneri and Ivrea zones, Italy. Geology 22(1):79–82
  • 16. Hurich CA (1996) Statistical description of seismic reflection wavefields: a step towards quantitative interpretation of deep seismic reflection profiles. Geophys J Int 125(3):719–728
  • 17. Hurich CA, Kocurko A (2000) Statistical approaches to interpretation of seismic reflection data. Tectonophysics 329(1):251–267
  • 18. Ikelle L, Yung S, Daube F (1993) 2-D random media with ellipsoidal autocorrelation functions. Geophysics 58(9):1359–1372
  • 19. Irving J, Holliger K (2010) Geostatistical inversion of seismic and ground-penetrating radar reflection images: What can we actually resolve? Geophysical Research Letters 37(21). https://doi.org/10.1029/2010GL044852
  • 20. Leary P (1991) Deep borehole log evidence for fractal distribution of fractures in crystalline rock. Geophys J Int 107(3):615–627
  • 21. Levander A, England RW, Smith SK, Hobbs RW, Goff JA, Holliger K (1994a) Stochastic characterization and seismic response of upper and middle crustal rocks based on the Lewisian gneiss complex, Scotland. Geophys J Int 119(1):243–259
  • 22. Levander A, Hobbs RW, Smith SK, England RW, Snyder DB, Holliger K (1994b) The crust as a heterogeneous “optical” medium, or “crocodiles in the mist.” Tectonophysics 232(1–4):281–297
  • 23. Poppeliers C (2007) Estimating vertical stochastic scale parameters from seismic reflection data: deconvolution with non-white reflectivity. Geophys J Int 168(2):769–778
  • 24. Poppeliers C, Levander A (2004) Estimation of vertical stochastic scale parameters in the Earth's crystalline crust from seismic reflection data. Geophys Res Lett 31(13). https://doi.org/10.1029/2004GL019538
  • 25. Pullammanappallil S, Levander A, Larkin SP (1997) Estimation of crustal stochastic parameters from seismic exploration data. J Geophys Res Solid Earth 102(B7):15269–15286
  • 26. Scholer M, Irving J, Holliger K (2010) Estimation of the correlation structure of crustal velocity heterogeneity from seismic reflection data. Geophys J Int 183(3):1408–1428
  • 27. Vasudevan K, Cook FA (1998) Skeletons and fractals — a statistical approach to deep crustal seismic data processing and interpretation. Tectonophysics 286(1):93–109
  • 28. Vasudevan K, Eckel S, Fleischer F, Schmidt V, Cook FA (2007) Statistical analysis of spatial point patterns on deep seismic reflection data: a preliminary test. Geophys J Int 171(2):823–840
  • 29. Von Karman T (1948) Progress in the statistical theory of turbulence. Proc Natl Acad Sci USA 34(11):530–539
  • 30. Wu R, Aki K (1985) The fractal nature of inhomogeneities in the lithosphere evidenced from seismic wave scattering. Pure Appl Geophys PAGEOPH 123(6):805–818. https://doi.org/10.1007/BF00876971
  • 31. Yuan G, Pei-Min Z, Hui LI, Xiao-Yong LI (2014) Estimation of 2D stationary random medium parameters from post-stack seismic data. Chin J Geophys 57(4):450–461
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8f43a596-a545-4cb7-9340-dcbe8640efc5
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