Predictive painting of 3-D seismic volumes

Introduction

Structural information is the most important content of seismic images. One way to characterize structure is to assign a dominant local slope attribute to all elements in a volume. Claerbout (1992) proposed the method of plane-wave destruction for detecting local slopes of seismic events. Closely related ideas were developed in the differential semblance optimization framework (Symes, 1994; Kim and Symes, 1998). Plane-wave destruction finds many important applications in seismic data analysis, including data regularization, noise attenuation, and velocity-independent imaging (Fomel, 2007b; Burnett and Fomel, 2009; Fomel et al., 2007; Fomel, 2002).

The main principle of plane-wave destruction is prediction: each seismic trace gets predicted from its neighbors that are shifted along the event slopes, and the prediction error gets minimized to estimate optimal slopes. In this paper, I propose to extract the prediction operator from the plane-wave destruction process and to use it for recursive spreading of information inside the seismic volume. I call this spreading predictive painting.

One particular kind of information that becomes meaningful when spread in a volume is relative geologic age, in the terminology of Stark (2004): seismic layers arranged according to the relative age of sedimentation. Once relative geological age is established everywhere in the volume, it is possible to flatten seismic images by extracting stratal slices (Zeng et al., 1998a) without manual picking of horizons. Even though flattened seismic horizons do not necessarily correspond to equivalent true geologic ages, flattening improves the interpreter's ability to understand and quantify the structural architecture of sedimentary layers (Zeng et al., 1998b). The idea of using local shifts for automatic picking was introduced by Bienati and Spagnolini (2001) and Lomask et al. (2006). Stark (2003) presents an alternative approach involving instantaneous phase unwrapping. Analogous techniques are implemented by (de Groot et al., 2006; Bruin et al., 2007).

Flattening and automatic picking of horizons are important not only for final structural interpretation but also for prestack imaging and data analysis and for extracting prestack amplitude attributes. The idea of prestack gather flattening using local cross-correlations was developed previously by a number of authors (Hinkley et al., 2004; Duveneck and Traub, 2006; Gulunay et al., 2007b,a).

The predictive painting method, introduced in this paper, provides a new approach for extracting and applying structural patterns, with superior computational performance. The advantages of the proposed method include both conceptual simplicity and computational efficiency. In the next sections, I describe the basic algorithm for automatic painting and demonstrate its performance with synthetic and field data examples.

Predictive painting of 3-D seismic volumes