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sep:research:theses:sep169 [2017/10/19 11:42]
gustavo
sep:research:theses:sep169 [2017/10/19 12:44] (current)
gustavo
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**Abstract**\\ **Abstract**\\
Subsurface seismic imaging has relied on the acoustic wave-propagation model for many decades. This choice has been justifi ed by the greater availability of acoustic only data, i.e., ocean streamers, higher computational cost of shear-wave processing, and challenges in wave-mode separation methods. Subsurface seismic imaging has relied on the acoustic wave-propagation model for many decades. This choice has been justifi ed by the greater availability of acoustic only data, i.e., ocean streamers, higher computational cost of shear-wave processing, and challenges in wave-mode separation methods.
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However, in the last few years, seismic exploration has moved to more complex subsurface targets, such as sub-salt and sub-basalt. In these scenarios, including a greater range of physical processes is advantageous. Elastic modeling and inversion achieves that by accounting for both pressure and shear wave propagations. Therefore, a greater understanding of elastic wave-equation methods in seismic imaging becomes fundamental. However, in the last few years, seismic exploration has moved to more complex subsurface targets, such as sub-salt and sub-basalt. In these scenarios, including a greater range of physical processes is advantageous. Elastic modeling and inversion achieves that by accounting for both pressure and shear wave propagations. Therefore, a greater understanding of elastic wave-equation methods in seismic imaging becomes fundamental.
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I formulate the imaging condition for the elastic wave-equation using the stress-velocity set of first-order partial diff erential equations. I show that the elastic imaging condition can be obtained similarly for density-Lame or density-velocity parameterizations of the model space. I demonstrate that these conditions are di fferent than the acoustic case and can be obtained by calculating the adjoint Born approximation of the nonlinear problem. I formulate the imaging condition for the elastic wave-equation using the stress-velocity set of first-order partial diff erential equations. I show that the elastic imaging condition can be obtained similarly for density-Lame or density-velocity parameterizations of the model space. I demonstrate that these conditions are di fferent than the acoustic case and can be obtained by calculating the adjoint Born approximation of the nonlinear problem.
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I discuss how elastic wave-equation modeling and imaging is computationally more intensive than acoustic methods. I propose solutions for memory cost and computational time optimizations and show performance gains in a simple synthetic example. Using the proposed formulation and computational improvements, I apply the elastic imaging condition to the Marmousi 2 synthetic model. I show an elastic reverse time migration (ERTM) result with model components in the density-Lame parameterization. I also show how this image can qualitatively indicate anomalies in a bulk-shear moduli ratio. I discuss how elastic wave-equation modeling and imaging is computationally more intensive than acoustic methods. I propose solutions for memory cost and computational time optimizations and show performance gains in a simple synthetic example. Using the proposed formulation and computational improvements, I apply the elastic imaging condition to the Marmousi 2 synthetic model. I show an elastic reverse time migration (ERTM) result with model components in the density-Lame parameterization. I also show how this image can qualitatively indicate anomalies in a bulk-shear moduli ratio.
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Finally, I combine all methodologies presented into an elastic full waveform inversion (EFWI) workflow. I apply this workflow to a 2D field data set acquired using four-component ocean-bottom nodes (4C OBNs). I obtain inversion results for density, P- and S-velocities up to 10 Hertz (Hz) frequency data. Finally, I combine P- and S-velocities to calculate a Vp/Vs model. The calculated model is composed of layers with Vp/Vs values between 1.5 and 2.3, which is consistent with the expected geology of the basin. Finally, I combine all methodologies presented into an elastic full waveform inversion (EFWI) workflow. I apply this workflow to a 2D field data set acquired using four-component ocean-bottom nodes (4C OBNs). I obtain inversion results for density, P- and S-velocities up to 10 Hertz (Hz) frequency data. Finally, I combine P- and S-velocities to calculate a Vp/Vs model. The calculated model is composed of layers with Vp/Vs values between 1.5 and 2.3, which is consistent with the expected geology of the basin.
**Reproducibility and source codes**\\ **Reproducibility and source codes**\\
 +This thesis has been tested for [[sep:research:reproducible|reproducibility]]. The source codes are made available for [[http://sepwww.stanford.edu/data/media/private/docs/sep169/elastic_fwi_final.tar.gz|download]]. The source code included here covers both the elastic modeling and imaging described in Chapters 2 and 4, as well as the optimizations from Chapter 3 and the complete field data workflow for Chapter 5. Due to data usage permissions, the raw data used in Chapter 5 is not included in the download.\\
**Defense presentation slides**\\ **Defense presentation slides**\\
-[[http://sepwww.stanford.edu/data/media/public/docs/sep167/chap1.pptx|Chapter 1]]\\ +All slides can be download as a .tar.gz file [[http://sepwww.stanford.edu/data/media/public/docs/sep169/slides.tar.gz|here]].\\ 
-[[http://sepwww.stanford.edu/data/media/public/docs/sep167/chap2.pptx|Chapter 2]]\\ +For individual .pptx files, follow the links below. Please acknowledge the source when reproducing these slides.\\ 
-[[http://sepwww.stanford.edu/data/media/public/docs/sep167/chap3.pptx|Chapter 3]]\\ +[[http://sepwww.stanford.edu/data/media/public/docs/sep169/Gustavo Alves - PhD defense - acoustic vs elastic data.pptx|Comparing acoustic and elastic data]]\\ 
-[[http://sepwww.stanford.edu/data/media/public/docs/sep167/chap4.pptx|Chapter 4]]\\+[[http://sepwww.stanford.edu/data/media/public/docs/sep169/Gustavo Alves - PhD defense - single vs multicomponent data.pptx|Comparing single and multicomponent data]]\\ 
 +[[http://sepwww.stanford.edu/data/media/public/docs/sep169/Gustavo Alves - PhD defense - Chapter 2 - Theory.pptx|Chapter 2 - Theory]]\\ 
 +[[http://sepwww.stanford.edu/data/media/public/docs/sep169/Gustavo Alves - PhD defense - Chapter 2 - A visual example of ERTM.pptx|Chapter 2 - A visual example of ERTM]]\\ 
 +[[http://sepwww.stanford.edu/data/media/public/docs/sep169/Gustavo Alves - PhD defense - Chapter 3 - Optimization - Random Boundaries.pptx|Chapter 3 - Random boundaries]]\\ 
 +[[http://sepwww.stanford.edu/data/media/public/docs/sep169/Gustavo Alves - PhD defense - Chapter 5 - EFWI of the Moere Vest data set.pptx|Chapter 5 - EFWI of the Moere Vest data set]]\\ 
 +[[http://sepwww.stanford.edu/data/media/public/docs/sep169/Gustavo Alves - PhD defense - Chapter 3 - Optimization - Reciprocity.pptx|Appendix A - Reciprocity]]\\
/web/html/data/attic/sep/research/theses/sep169.1508413323.txt.gz · Last modified: 2017/10/19 11:42 by gustavo
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