Seismic survey design deals with determining the acquisition parameters that lead to the best possible imaging and characterization of the subsurface. The design of the survey is constrained by health, safety and environmental considerations and the available budget, seeking for a balance between quality and cost. Because seismic exploration is a widely used geophysical method for revealing underground resources, information about the subsurface is available in many areas. Therefore, it can potentially be used for purposes supplementary to exploration such as the monitoring of producing fields and fluids injection. However, the available budget for these purposes is usually lower than for exploration, and it becomes a priority to maximize the benefits derived from a potentially cheaper acquisition. In this thesis, we propose new methods for the analysis and design of seismic surveys that are based on previous knowledge from existing subsurface models and that aimto maximize image quality with the lowest acquisition efforts.

The quality and business aspects are both of particular importance in determining the type of seismic acquisition. Usually, a strong emphasis on cost reduction is inevitable. On the other hand, there is an increasing demand for the acquisition of high-quality seismic data that can contribute to the various stages in the field development profile. These conflicting desires eventually make conventional seismic surveys an inadequate option. The application of blended acquisition along with efficient detector and source geometries is capable of providing high-quality seismic data in a cost-effective and productive manner. This way of data acquisition also contributes to minimizing health, safety and environment exposure in the field. Blended acquisition allows multiple source-wavefields to be overlapped in time, space, and temporal and spatial frequency, causing blending interference. The acquisition of less data via sparse detector and source geometries likely violates the Nyquist sampling criterion. Therefore, to make the aforementioned approach technically justifiable, deficiencies in recorded data have to be dealt with through the course of subsequent processing steps. One way to encourage this technique is to minimize any imperfection in processing algorithms. In addition, one may derive survey parameters that enable a further improvement in these processes, which is the primary focus in this thesis.

This thesis demonstrates, that bathymetric data improves data imaging results of full wavefield migration using different acquisition designs. The algorithm gets more stable by constraining the inversion with the sea bottom information, especially in case of large source and receiver spacings, without making compromises on image quality. Not using the proposed method will result in uninterpretable images, if the topography of the seafloor gets too complex.
In this thesis it is proposed to use an autonomous underwater vehicle (AUV) to acquire the bathymetry. The quality of the bathymetry is highly dependent on the ability to adequately localize the AUV. Therefore a terrain based navigation system was developed based on Kalman filters. It is shown that using a Kalman filter combined with sparsely sampled sea bottom information was of advantage to locate the vehicle at its true position. This is not possible with just using the inertial navigation system of the AUV.