Joint imaging with streamer and ocean bottom data

Joint imaging

Linearized inversion can combine different sets of data that share the same model. In Wong et al. (2010), we show that up-going and down-going (mirror) imaging can be combined in a joint inversion. We now extend the method to joint-inversion of nodes and streamers data. The fitting goal is:

$$0 \approx \left[ \begin{array}{c} \mathbf L_{str} \\ \mathbf L_{O... ...gin{array}{c} \mathbf d_{str} \\ \mathbf d_{OBN\downarrow} \end{array} \right],$$ (3)

where ${\mathbf L}$ is the forward-modeling operator that corresponds to equation 1. The subscripts $_{str}$ and $_{\downarrow}$ denote streamer and OBN-mirror, respectively. Our goal is to obtain a final image that consistently explains the two data sets.

The major benefit of joint inversion is that it attenuates the migration artifacts caused by spurious energy in the original recorded data. Spurious energy can occur due to imperfect pre-processing. For example, even with the most advanced multiple-removal methods, surface and internal multiples energy can still be present in the field data. For OBN, there is an additional challenge of removing the shear and up-going energy in the mirror signal. Next, we will look at a 2D synthetic study based on the Marmousi model.

Joint imaging with streamer and ocean bottom data