Viterbi Faculty of Electrical Engineering, Technion
Three-dimensional Seismic Imaging and Sparse Inversion
The objective of many imaging techniques is to give insight into the internal structure of a medium or an object, which cannot be detected by the human eye. Throughout the years, abundant resources have been invested in many fields in attempts to enhance imaging resolution, and remarkable results have been achieved. However, practical implementations are often inefficient or too slow to be plugged in real-life applications. Exploration seismology attempts to produce a three-dimensional (3D) image representing a 3D earth section at depth. In the first part of this talk we study the problem of prestack seismic time migration velocity analysis (MVA). Accurate imaging of seismic data requires knowledge of the velocity of the propagating waves at all points along the reflection paths. We briefly review the basic background, and establish two automated techniques for MVA using recurrent neural nets (RNNs). The proposed methods are evaluated via real data experiments. Secondly, we present a multichannel method for 3D recovery of reflectivity images from 3D seismic data. The proposed algorithm is tailored to take into account attenuation and dispersion propagation effects of the reflected waves, while also promoting the sparsity of the recovered reflectivity image. At the same time, the proposed method considers the relations between spatially-neighboring data points. We provide theoretical guarantees for stable recovery, in the case of horizontal layered sub-terrain. The robustness of the proposed technique is demonstrated, compared to single-channel recovery, via synthetic and real data examples.
*PhD Seminar under the supervision of Prof. Israel Cohen.
Zoom link: https://technion.zoom.us/j/99857323586
Sun 17 Jan 2021
Start Time: 14:30
End Time: 15:30
ZOOM Meeting | Electrical Eng. Building