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Frozen Gaussian Approximation for Elastic Waves, Seismic Inversion and Deep Learning
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Frozen Gaussian Approximation for Elastic Waves, Seismic Inversion and Deep Learning. |
| 개인저자 | Hateley, James Charles, IV. |
| 단체저자명 | University of California, Santa Barbara. Mathematics. |
| 발행사항 | [S.l.]: University of California, Santa Barbara., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 141 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781088310304 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Santa Barbara, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Yang, Xu. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | The frozen Gaussian approximation (FGA) is an efficient solver for high frequency wave propagation. This work is to generalize the FGA to solve the 3-D elastic wave equation and use it as the forward modeling tool for seismic tomography with high-frequency initial datum. The evolution equation is derived by weak asymptotic analysis in conjunction with projecting onto an orthonormal frame |
| 일반주제명 | Applied mathematics. Geophysics. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
