Uncertainty based multi-step seismic analysis for near-surface imaging

Abstract

Abstract Near-surface seismic surveys are often designed for surface wave and seismic tomographic analysis. In recent years, seismic imaging methods have been more frequently used at this scale. Recognition of near-surface structures using a single method is insufficient because of the ambiguity of the inversion problem. As a solution, the authors propose a multi-step approach, where several different seismic methods are used in a particular order, to achieve an optimal model. A multi-method approach allows utilisation of a whole spectrum of recorded data, even the elements that are treated as background noise in other techniques. In classical processing approach, information about data uncertainty is often omitted or used in the simplest way for the single method only. This work presents an updated approach to uncertainty analysis by transferring estimated uncertainty between processing steps. By assuming that every consecutively applied method is more certain, the authors were able to obtain accurate velocity fields for seismic imaging, as the main information received from the previous steps. Based on information from multiple methods, a seismic stack in the depth domain was created as a final result, with an estimate of uncertainty.