Simultaneous-source Acquisition and Environmental Noise - Good or Bad?

Abstract

The promise of simultaneous-source acquisition in improving acquisition efficiency and/or sampling has been studied by a number of authors, especially over the last few years. However, most of these studies focussed on processing the signal associated with the known shots, rather than on the impact of environmental noise. The presence of environmental noise will clearly degrade the processed product from a simultaneous-source dataset, much as it does a conventional (sequential) dataset. In addition, the impact of the noise on the separation process is a concern for simultaneous-source data. The story is not all bad, however. Simultaneous-source acquisition generally involves increased source effort compared to an equivalent sequential data set, and, therefore, the signal-to-noise ratio of the acquired data is improved. This observation raises the possibility that simultaneous-source data could be acquired in higher-noise environments than the equivalent sequential data. We study this possibility using real simultaneous-source data acquired with varying environmental noise levels. Comparisons between simulated sequential and simultaneous-source products with varying noise levels indicate that simultaneous-source acquisition can indeed produce equivalent results in a significantly noisier environment.