Blended-acquisition encoding with generalized blending operators

Abstract

Recently, we established a generalized blending model, which can explain any methods of blended acquisition by including the encoding into the generalized operators. With this highly flexible and tolerant model, we come up with a challenging question: what it is to be, and how to find an optimal blended-acquisition design, which should be the most suitable for deblended-data reconstruction among plenty of concepts of blended acquisition. In this paper, we introduce a method of blended-acquisition encoding: temporally modulated and spatially dispersed source array, namely M-DSA, that jointly uses modulation sequencing in the time dimension and dispersed source array in the space dimension. This allows quite straightforward deblending by filtering and physically separating frequency channels in the frequency domain. We run our blended-acquisition designing based on the deblending performance for several scenarios of blended acquisition. These examples show that: M-DSA attains the best deblending performance; this method has less constraints in the encoding with more operational flexibility, compared to other methods being developed in the industry today. Indeed, this method requires only simple signaturing in the encoding; merely frequency-banded and modulated signatures in the time dimension for each shot in the blended-source array. This could even render any other blending properties unnecessary. Those, such as distance separation among shot locations and time shifts among shot times, might not be required anymore. There might be no limitation on the number of sources, thus no limitation on the blending fold, in order to secure successful deblending. Furthermore, this method allows random sampling; randomly distributed sources in the space dimension in the blended-source array. Consequently, this method makes the blended-acquisition encoding and operations significantly simple and robust, as well as for the deblending processing. We believe that our M-DSA method should be one of the best methods of blended acquisition.