Full Elastic Waveform Search Engine for Near Surface Imaging

Abstract:

For processing land seismic data, the near-surface problem is often very complex and may severely affect our capability to image the subsurface. The current state-of-the-art technology for near surface imaging is the early arrival waveform inversion that solves an acoustic wave-equation problem. However, fitting land seismic data with acoustic wavefield is sometimes invalid. On the other hand, performing elastic waveform inversion is very time-consuming. Similar to a web search engine, we develop a full elastic waveform search engine that includes a large database with synthetic elastic waveforms accounting for a wide range of interval velocity models in the CMP domain. With each CMP gather of real data as an entry, the search engine applies Multiple-Randomized K-Dimensional (MRKD) tree method to find approximate best matches to the entry in about a second. Interpolation of the velocity models at CMP positions creates 2D or 3D Vp, Vs, and density models for the near surface area. The method does not just return one solution; it gives a series of best matches in a solution space. Therefore, the results can help us to examine the resolution and nonuniqueness of the final solution. Further, this full waveform search method can avoid the issues of initial model and cycle skipping that the method of full waveform inversion is difficult to deal with.