A robust data driven AVO inversion with logarithm absolute error loss function

• Autorzy: Du Siyuan , Zhang Jiashu , Hu Guangmin

Identyfikatory

DOI

10.1007/s11600-020-00416-1

• Języki publikacji: EN

Abstrakty

EN

Amplitude variation with ofset (AVO) inversion is a widely used approach to obtain reliable estimates of elastic parameter. Tikhonov and total variation regularization are commonly used methods to address ill-posed problem of AVO inversion. However, these model-driven methods are only for special geological structure such as smoothness or blockiness. In this letter, a robust data-driven-based regularization method with logarithm absolute error loss function (DDI-Log) for AVO inversion is proposed. In DDI-Log, the information of well-log data and the complex geology are considered in a sparse representation framework. In pre-stack seismic data, outlier noise can negatively infuence inversion results. Thus, diferent from the previous data-driven inversion based on L2 norm loss function, we extend the logarithm absolute error function as the loss function. In the iteration, a new spectral PRP conjugate gradient method is used to solve the large-scale optimization problem. The synthetic data and feld data tests illustrate that the proposed approach is robust against outlier noise and that the resolution and accuracy of the solutions are improved.

Słowa kluczowe

EN

dictionary learning; logarithm absolute error function; AVO inversion

PL

nauka słownika; funkcja błędu bezwzględnego logarytmu

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2020
• Tom: Vol. 68, no. 2
• Strony: 445--458
• Opis fizyczny: Bibliogr. 32 poz.

Twórcy

autor

Du Siyuan

• Sichuan Province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China

autor

Zhang Jiashu

• Sichuan Province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China

autor

Hu Guangmin

• School of Resources and Environments, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China

Bibliografia

• 1. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322
• 2. Aki K, Richards PG (2002) Quantitative seismology. University Science Books
• 3. Anagaw AY, Sacchi MD (2011) Full waveform inversion with total variation regularization. In: Recovery-CSPG CSEG CWLS convention
• 4. Buland A, Omre H (2003) Bayesian linearized avo inversion. Geophysics 68(1):185–198
• 5. Epanomeritakis I, Akçelik V, Ghattas O, Bielak J (2008) A Newton-CG method for large-scale three-dimensional elastic full-waveform seismic inversion. Inverse Probl 24(3):034015
• 6. Fan C, Song Y, Zhang Y, Jiang Z (2015) An amplitude-normalized pseudo well-log construction method and its application on avo inversion in a well-absent marine area. Acta Geophys 63(3):761–775
• 7. Fujin Z, Jiashu Z (2013) Linear discriminant analysis based on l1-norm maximization. IEEE Trans Image Process 22(8):3018–3027
• 8. Gholami A (2015) Nonlinear multichannel impedance inversion by total-variation regularization. Geophysics 80(5):R217–R224
• 9. Guitton A, Verschuur D (2004) Adaptive subtraction of multiples using the l1-norm. Geophys Prospect 52(1):27–38
• 10. Hampson DP, Russell BH, Bankhead B (2005) Simultaneous inversion of pre-stack seismic data. In: SEG technical program expanded abstracts 2005, Society of Exploration Geophysicists, pp 1633–1637
• 11. Ji J (2006) Hybrid L1/L2 norm IRLS method with application to velocity-stack inversion. In: 68th EAGE Conference and Exhibition incorporating SPE EUROPEC 2006
• 12. Li C, Zhang F (2017) Ava inversion based on the L1-norm-based likelihood function and the total variation regularization constraint. Geophysics 82(3):1–54
• 13. Li Z, Hu G, She B (2017) A hybrid regularization approach for AVA inversion of the piecewise smooth model
• 14. Liu Y, Liu C, Wang D (2009) A 1d time-varying median filter for seismic random, spike-like noise elimination. Geophysics 74(1):V17–V24
• 15. Liu Y, Zhang J, Hu G (2015) Iterative reweighted least m-estimate avo inversion. Explor Geophys 46(2):159–167
• 16. Pyun S, Son W, Shin C (2009) Frequency-domain waveform inversion using an l 1-norm objective function. Explor Geophys 40(2):227–232
• 17. Sen MK, Roy IG (2003) Computation of differential seismograms and iteration adaptive regularization in prestack waveform inversion. Geophysics 68(6):2026–2039
• 18. She B, Wang Y, Liang J, Liu Z, Song C, Hu G (2018) A data-driven amplitude variation with offset inversion method via learned dictionaries and sparse representation. Geophysics 83(6):R725–R748
• 19. She B, Wang Y, Liang J, Hu G (2019a) Data-driven simultaneous seismic inversion of multiparameters via collaborative sparse representation. Geophys J Int 218(1):313–332
• 20. She B, Wang Y, Zhang J, Wang J, Hu G (2019b) Avo inversion with high-order total variation regularization. J Appl Geophys 161:167–181
• 21. Siahsar MAN, Gholtashi S, Kahoo AR, Wei C, Chen Y (2017) Data-driven multi-task sparse dictionary learning for noise attenuation of 3d seismic data. Geophysics 82(6):V385–V396
• 22. Sun J, Li Y (2014) Adaptive lp inversion for simultaneous recovery of both blocky and smooth features in a geophysical model. Geophys J Int 197(2):882–899
• 23. Sun W, Yuan YX (2006) Optimization theory and methods: nonlinear programming, vol 1. Springer, Berlin
• 24. Tao L, Jiang X, Li Z, Liu X, Zhou Z (2019) Multiscale incremental dictionary learning with label constraint for sar object recognition. IEEE Geosci Remote Sens Lett 16(1):80–84
• 25. Wan Z, Yang Z, Wang Y (2011) New spectral PRP conjugate gradient method for unconstrained optimization. Appl Math Lett 24(1):16–22
• 26. Yuan S, Wang S, Luo C, He Y (2015) Simultaneous multitrace impedance inversion with transform-domain sparsity promotion. Geophysics 80(2):R71–R80
• 27. Yuan S, Wang S, Luo C, Wang T (2018) Inversion-based 3-d seismic denoising for exploring spatial edges and spatio-temporal signal redundancy. IEEE Geosci Remote Sens Lett 15(11):1682–1686
• 28. Yuan S, Wang S, Luo Y, Wei W, Wang G (2019) Impedance inversion by using the low-frequency full-waveform inversion result as an a priori model. Geophysics 84(2):R149–R164
• 29. Zhang Z, Chunduru RK, Jervis MA (2000) Determining bed boundaries from inversion of em logging data using general measures of model structure and data misfit. Geophysics 65(1):76–82
• 30. Zhang J, Lv S, Liu Y, Hu G (2013) Avo inversion based on generalized extreme value distribution with adaptive parameter estimation. J Appl Geophys 98:11–20
• 31. Zhou Y, Gao J, Chen W, Frossard P (2016) Seismic simultaneous source separation via patchwise sparse representation. IEEE Trans Geosci Remote Sens 54(9):5271–5284
• 32. Zong Z, Li K, Yin X, Zhu M, Du J, Chen W, Zhang W (2017) Broadband seismic amplitude variation with offset inversion. Geophysics 82(3):M43–M53

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)