Quantitative evaluation of 3D land acquisition geometries with arrays and single sensors: Closing the loop between acquisition and processing

The growing popularity of land nodes demands careful survey design practices to smoothly supersede cabled seismic acquisition with geophone arrays. Unfortunately, trace density is often used as a catchall proxy to describe survey quality, which is a gross oversimplification. We describe comprehensive and quantitative workflows focusing on final image quality for evaluating existing or new 3D land acquisition geometries with arrays and single sensors. They streamline the design process, remove human bias, and close the loop between acquisition and processing. A central element is a data-driven approach for deriving absolute signal-to-noise ratio (S/N) directly from the data. The resulting S/N volumes can be analyzed as cubes or slices or distilled to statistical quantities. We apply new workflows to three typical use cases from 3D land seismic data. First, we quantitatively contrast different 3D data sets acquired with various field acquisition geometries and understand which acquisition parameters are likely responsible for S/N differences. Second, we perform a realistic numerical feasibility study evaluating multiple 3D acquisition geometries with arrays and single sensors and assess their expected performance on a complex SEG Advanced Modeling Arid data set representative of the desert environment. For feasibility studies, complete automation can be achieved by applying migration in lieu of processing and data-driven S/N as evaluation steps. Finally, we show how to predict absolute S/N outcomes of new 3D acquisitions based on the existing legacy data with different acquisition geometry. We demonstrate the excellent predictive power of the analytical signal-strength estimate formula for both field and synthetic elastic data sets. Translating survey design into commonly spoken “image S/N language” improves communication between geoscientists and enables more effective decision-making.