COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Seismic depth imaging: Pushing the boundaries of resolution and accuracy
Seismic depth imaging: Pushing the boundaries of resolution and accuracyAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact nobody. RNTW04 - Synergistic workshop on Rich and Nonlinear tomography aimed at drawing together all strands of both methods and applications with new insights Depth Imaging uses processed surface seismic data to estimate models and produce images of the Earth’s subsurface with their features correctly positioned in depth. Depth models describe the seismic properties of the subsurface, allowing us to characterise the non-linear relationship between the structure of physical features generating seismic reflections, and the seismic data recorded at the surface. Depth Migration uses these models to produce an image by correctly repositioning the recorded seismic data. Ray-based Tomography and Full Waveform Inversion are the key methods used to obtain these models.I will review the role of these two key inverse problems in seismic depth imaging and explore how advances in modelling the physics of seismic wave propagation (especially seismic anisotropy and elastic effects) have brought improved resolution and accuracy while complicating the inverse problem. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsNational Centre for Statistical Ecology (NCSE) Seminars Cambridge Rape Crisis Public Lectures Dirac LectureOther talksPolitical engagement, profession and socialist economics in fin-de-siècle Europe Nonconnective animated rings and affine stacks Formalising modular forms, Eisenstein series and the statement of the modularity conjecture in Lean Recovery from aphasia after stroke – from network to therapy Relative Entropy Coding for Learned Data Compression Characterising the Gaussian Free Field |