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SPDE Geostatistical Filtering for Seismic Data
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 81st EAGE Conference and Exhibition 2019, Jun 2019, Volume 2019, p.1 - 5
Abstract
Factorial Kriging (FKr) is an efficient noise attenuation technique for seismic data. This geostatistical filtering technique relies on a spatial covariance model composed of independent noise and signal elements. When combined with a local-covariance based approach it leads to a dip-steered solution, meaning driven by local dip and azimuths attached to the signal component of the model. Moreover the use of spatially varying covariance parameters related to the noise component(s) opens the door to the processing of non-stationary noise.
With the use of local covariance parameters, the classical FKr solution requires a moving neighborhood, even if applied on a grid. It has two main inconveniences: the high computation time cost due to the multiplication of local kriging systems to solve and the possible generation of local artefacts caused by the limited dimensions of the neighborhood.
In this paper, we present a new FKr solution which enables to tackle these problems. It is based on Stochastic Partial Differential Equations (SPDE). A theoretical description is given in the first part of the paper. In the second part, we show the results of the very first application of the SPDE FKr solution to some pre-stack data.