Novel Advances in Simultaneous Source Acquisition and Processing

Abstract

There has been considerable interest recently in data acquisition using simultaneous sources because of the enormous improvements in acquisition efficiency and source sampling that the method proffers. Realizing these improvements in practice requires an appropriate combination of survey design, acquisition technology and data processing capabilities. Given a suitably-designed survey, I show that simultaneous-source data can be separated effectively into equivalent datasets for each source. These datasets may then be processed using conventional techniques, which benefit naturally from any improvements in sampling. Several field datasets, employing a variety of acquisition geometries, illustrate the viability and limitations of this approach. The main limitation comes from ambiguities in the separation process, which can be resolved to a large degree by using source dithering techniques in combination with a separation algorithm that includes an effective sparseness constraint. The use of more than two sources simultaneously adds to the potential of the method and is shown to be viable provided the survey is designed appropriately.