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Image fusion for travel time tomography inversion

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The travel time tomography technology had achieved wide application, the hinge of tomography was inversion algorithm, the ray path tracing technology had a great impact on the inversion results. In order to improve the SNR of inversion image, comprehensive utilization of inversion results with different ray tracing can be used. We presented an imaging fusion method based on improved Wilkinson iteration method. Firstly, the shortest path method and the linear travel time interpolation were used for forward calculation; then combined the improved Wilkinson iteration method with super relaxation precondition method to reduce the condition number of matrix and accelerate iterative speed, the precise integration method was used to solve the inverse matrix more precisely in tomography inversion process; finally, use wavelet transform for image fusion, obtain the final image. Therefore, the ill- conditioned linear equations were changed into iterative normal system through two times of treatment and using images with different forward algorithms for image fusion, it reduced the influence effect of measurement error on imaging. Simulation results showed that, this method can eliminate the artifacts in images effectively, it had extensive practical significance.
Rocznik
Tom
S 1
Strony
149--156
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
  • College of Computer and Information, Hohai University, Nanjing 210098, China
autor
  • The Fourth Department, Bengbu Naval Petty Officer Academy, Bengbu 233012, China
autor
  • College of Information Engineering, Anhui Science and Technology University, Chuzhou 233100, China
autor
  • Department of Geology, Faculty of Science, University Of Malaya 50603 Kuala Lumpur, Malaysia
  • Faculty of Science & Natural Resources, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia
Bibliografia
  • 1. H.M. Benz, B.A. Chouet, P.B. Dawson, J.C. Lahr, R.A. Page, and J.A. Hole, “Three dimensional P and S wave velocity structure of Redoubt Volcano”. Journal of Geophysical Research,vol.101,pp. 8111-8128, 1996.
  • 2. L. Huang, Z.Y. Huang and Y. Wang, “The inversion algorithm of ultrasonic tomography with concrete structure”. Journal of Hunan University (Natural sciences), vol. 33, no. 5, pp. 26- 30, 2006.
  • 3. T. Nemeth, E. Normark, and F.H. Qing, “Dynamic smoothing in cross well travel time tomography”. Geop hysics,vol.62,no.1,pp.168-176,1997.
  • 4. H. Maurer, A.G. Green, and K. Holliger, “FullWaveform inversion of cross-hole radar data based on 2-D finite-difference time domain solutions of Maxwell‘s equations”. IEEE transactions on geoscience and remote sensing,vol.72,no.5,pp.53-64, 2007.
  • 5. S.C. Su, S.D. Wang and L. Wu, “Smoothing SIRT algorithm with cross well tomography”. Journal of China University of Petroleum (Natural sciences), vol.25,no.6,pp.29-31, 2001.
  • 6. C. C. Paige and M. A. Saunders, “Sparse linear equations and least squares problems”. ACM Transactions on Mathematical Software, vol. 8,no.2,pp.195-209,1982.
  • 7. N. Linde, A. Tryggvason, J.E. Peterson, and S.S. Hubbard, “Joint inversion of cross hole radar and seismic travel times acquired at the South Oyster Bacterial Transport Site”. Geophysics, vol. 73, no.4,pp.39-50, 2008.
  • 8. Moser T J, “Shortest path calculation of seismic rays”. Geophysics, vol. 56,no.1,pp. 59~67,1991.
  • 9. Asakawa, Eiichi and Kawanaka, Taku, “Seismic ray tracing using linear travel time interpolation”. Geophysical Prospecting,vol.41,no.l,pp.99~ 112,1993.
  • 10. J.X. Cao and Z.Q. Yan. “Estimation of seismic crosshole travel time tomography resolution”. Journal of Chengdu University of Science & Engineering, vol.22,no.4,pp.95-101, 1995.
  • 11. Y. Saad and M.H. Schultz. “GMRES: Generalized minimal residual algorithm for solving non-symmetric line system”. SLAM J.SCI.STAT.COMPUT, vol.7, no.3,pp.856-869,1986.
  • 12. G.L.G. Sleijpen and D.R. Fokkema, “Bicgstab (L) for linear equations involving unsymmetric matrices with complex spectrum”. Electronic trasactions on numerical analysis, vol.1, pp.11 -32,1993.
  • 13. W.C. Yang and J.Y. Du, “A new algorithm for tomographic imaging and its application in engineering detection”. Chinese Journal of Geophysics, vol.37,no.2,pp.239-244, 1994.
  • 14. F. Boschetti, M.D. Dentith and R.D. List, “A fractalbased algorithm for detecting first arrivals on seismic traces”. Geophysics, vol.61,no.4,pp.1095-1102,1996.
  • 15. C.X. Chen and H.D. Yin, “Using super relaxation preconditioned conjugate gradient method for solving large sparse equations”. Science technology and Engineering, vol.10,no.10,pp. 2389-2394, 2010.
  • 16. X.Y. WU, R. SHAO and Y.R. ZHU, “Iterative improvement of a solution for an ill conditioned system of linear equations based on a linear dynamic system”. Computers and Mathematics with Applications,vol.44,pp.1109-1116,2002.
  • 17. W.Z. Zhang and P.Y. Huang, “Precise iterative method for solving ill conditioned algebraic system”. Applied Mathematics and Mechanics, vol.34,no.7,pp.736-741,2013.
  • 18. Li H, Manjunath BS and Mitra SK, “Multisensor image fusion using the wavelet transform”. Graphical Models and Image Processing,vol.57,no.3,pp.235~245,1995.
  • 19. Adam Pidlisecky, Eldad Haber, and Rosemary Knight, “RESINVM3D: A 3D resistivity inversion package”. Geophysics, vol.72,no.2, pp.H1-H10,2007.
  • 20. Ajo-Franklin J B. “Using high resolution borehole geophysics for DNAPL detection and environmental site characterization”, California:Department of Geophysics, Stanford University,2005
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-37ca6e82-ac76-4257-a800-b5ea8b71cbf0
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