Non-Iterative Data Regularization
Publication Date: 2011-Aug-10
The IP.com Prior Art Database
Page 01 of 17
Page 1 of 17 Abstract
We describe a non-iterative regularization technique for data sampled on an irregular grid. The new technique uses multichannel data, i.e., both data and their gradient. Considering that multichannel data in marine acquisition may be soon available, these types of regularization techniques may find important application areas like time-lapse survey matching, multiple suppression and imaging. When data are not aliased, by using the new technique the accuracy of regularization can be improved compared to existing non-iterative regularization techniques. An important feature of the new method is that it relaxes sampling requirements to avoid aliasing. By conducting simulation studies we show that the new method outperforms its non-iterative counter parts. Since the new method is linear, it can be formulated as a matrix multiplication. Hence, for a fixed sampling grid, the regularization matrix can be computed and stored beforehand to reduce computational load.
The aim of exploration seismology is to obtain an image of the subsurface by probing it with seismic waves at various locations. These waves are generated by using airguns in marine, and vibroseis or dynamite in land. They propagate downwards through the subsurface, and are reflected at interfaces between geological layers. They subsequently propagate upwards to the surface, where they are detected and recorded.
In exploration seismology, although the time coordinate is regularly sampled, spatial coordinates are typically irregularly sampled due to the presence of obstacles in land and strong currents in marine. Also, because of various insertions in a marine cable, the inline sampling can be quite irregular. Furthermore, due to cost considerations, seismic data are usually sparsely sampled along the spatial coordinates in addition to being irregularly sampled.
The regularization of seismic data is very important especially in time-lapse survey matching, multiple suppression and imaging. If the irregular nature of the sampling grid is ignored or handled poorly, notable errors are introduced whose severity may be further amplified at later stages of the processing. This necessitates the use of robust and efficient techniques for seismic data regularization. Furthermore, in next generation of seismic streamers, particle velocity measurement may be also available in addition to the pressure measurement. Therefore, we need novel techniques and approaches to utilize the multichannel data (data and their gradient).
In this disclosure, we introduce a novel technique to regularize the seismic data when gradient measurements are available. The new technique is non-iterative. It accepts a block of input data and provides a block of regularized output data. Since it is linear, it can be represented as a matrix multiplication. Depending on the nature of the data to be regularized, we developed two forms of the new method. First form is used when the dat...