Wang, Hongwei (Geotrace Technologies Incorporated) | Sun, Yong (Geotrace Technologies Incorporated) | Boyer, Scott (Geotrace Technologies Incorporated) | Yu, Gary (Geotrace Technologies Incorporated) | Stein, Jaime (Geotrace Technologies Incorporated) | Van Reenen, Shelley (Geotrace Technologies Incorporated)
Shallow Water Demultiple (SWD) is a very challenging problem for marine seismic data processing. In shallow water environments, water bottom reflections are recorded only on a few near offset traces because critical reflection angle is reached quickly. In very shallow water, water bottom reflections may disappear completely. This poses a limitation to any convolution based demultiple methods such as Surface Related Multiple Elimination (SRME) and SWD to predict first order multiple.
In this paper we propose a way to enhance these aforementioned methods by modeling the water bottom reflection and then adding it to the recorded seismic data. The modified data can then be used to predict better first order multiple using SRME and/or SWD. We call these methods enhanced SRME and enhanced SWD, respectively. We shall also demonstrate that an optimal way to perform the multiple elimination is to cascade the enhanced SWD followed by SRME. We call this methodology Cascaded Enhanced Shallow Water Demultiple (CESWD).
Our test results show that enhanced SWD is better than enhanced SRME, and CESWD is better than enhanced SWD. Finally a comparison of these methods is presented by applying them to a real data example. The enhanced methods produce better results than their conventional counterparts.