Classical Predictive Deconvolution

Predictive deconvolution represents a classical processing technique widely used in seismic data analysis. This algorithm enables the inversion of subsurface structures to enhance understanding of Earth's internal architecture. The fundamental principle involves utilizing seismic waves to scan underground formations, followed by advanced wave processing to construct velocity models of subsurface layers. The predictive deconvolution algorithm typically implements Wiener filtering techniques to effectively transform seismic records into clear images of geological structures, assisting geoscientists in better comprehending Earth's internal composition and evolutionary history.

In code implementations, this procedure often involves key functions such as autocorrelation calculation, prediction filter design, and convolution operations. The algorithm commonly employs a prediction distance parameter to separate primary reflections from multiple reflections, with optimization techniques applied to minimize prediction errors. The implementation typically includes steps for wavelet estimation, gap selection, and filter application to enhance seismic resolution and suppress unwanted reverberations.