Model-based deconvolution for dominant, thin-bed seismic reflections

Abstract

Predictive deconvolution is widely treated as a universally applicable tool for multiple removal and wavelet compression. The fundamental assumption of random reflectivity is seriously compromised in geological situations where the reflection sequence comprises a small number of dominant horizons. This situation is not uncommon in coal environments. Where the primary seismic objective is high quality imaging of particular target horizons, an improved result can be achieved if the deconvolution is designed according to assumptions more relevant to the geological situation. We outline a simple example of this approach, aimed at imaging a production coal seam, of thickness 5-10 m, at a mine in the Bowen Basin, Australia. Using horizon time picks from a preliminary volume, the full reflection package associated with the seam is extracted and deterministically filtered to obtain an estimate of the intrinsic wavelet. A Wiener spiking filter, designed on the extracted wavelet, is then used to deconvolve the seam package. In comparison to the predictive deconvolution approach, this model-based procedure provides improved resolution of the top and base coal interfaces. In addition, derived amplitude and frequency attributes are more robust in terms of known geology. Variants of this simple model-based procedure should have relevance in a range of dominant-horizon situations where predictive deconvolution is invalid.