This shows you the differences between two versions of the page.

sep:research:theses:sep169 [2017/10/19 11:39] gustavo created |
sep:research:theses:sep169 [2017/10/19 12:44] (current) gustavo |
||
---|---|---|---|

Line 9: | Line 9: | ||

* Chapter 1: Introduction | * Chapter 1: Introduction | ||

- | * Chapter 2: Seismic rotations from induction-coil magnetometers | + | * Chapter 2: Elastic wave-equation and adjoint formulation |

- | * Chapter 3: Wave-mode separation with translations and rotations | + | * Chapter 3: Computational optimization of elastic modeling and imaging |

- | * Chapter 4: Automatic wave mode identification using machine learning | + | * Chapter 4: Elastic imaging in the Marmousi 2 model |

+ | * Chapter 5: Elastic full waveform inversion of the Moere Vest 2D data set | ||

+ | * Appendix A: Reciprocity in multicomponent elastic data | ||

+ | * Appendix B: 3D Equations | ||

* Bibliography | * Bibliography | ||

Line 17: | Line 20: | ||

**Abstract**\\ | **Abstract**\\ | ||

Subsurface seismic imaging has relied on the acoustic wave-propagation model for many decades. This choice has been justified 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 justified 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. | ||

+ | |||

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. | ||

+ | |||

I formulate the imaging condition for the elastic wave-equation using the stress-velocity set of first-order partial differential 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 different 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 differential 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 different than the acoustic case and can be obtained by calculating the adjoint Born approximation of the nonlinear problem. | ||

+ | |||

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. | ||

+ | |||

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]]\\ |