Research and application of seismic facies analysis based on the empirical mode decomposition

Abstract

Summary This paper introduces the empirical mode decomposition (EMD) method into seismic facies analysis, to determine favorable attributes that reflect subsurface structures and characteristics of hydrocarbon saturation. The EMD method is the core of the Hilbert-Huang transformation which is a newly developed time-frequency analysis technique. The method uses the EMD to decompose seismic data into Intrinsic Mode Functions (IMF), and different IMFs have different frequency characteristics and indicate different geological information. This paper combines the EMD method with the Kohonen's selforganizing Neural Network based seismic facies analysis. The reconstructed seismic data using only the characteristic IMF components can more clearly indicate fault distribution and favorable object areas. Therefore, the EMD-based method can be used to enhance the signal-tonoise ratio and the resolution of seismic attributes, and thus it is of great importance to structure interpretation and reservoir prediction.