Identification of Thin Layer Via Source Wave-Field Dictionary Learning

Seismic data is an aggregation of traces that shows a spatial and temporal sampling of reflected wavefields—the identification of reservoir localization based on thin layer selection from the earth layers. The challenge to finding thin layers is that the information acquired gets distorted limiting reservoir localization due to subsurface complexity and random environmental noise. Dictionary elements from structure functions represent reflectivity patterns used in inversion seismic data. The seismic traces is a superposition of constituting reflectivity patterns. Traditionally noise separation is done by utilizing different properties in the various transform domains. The results lack adaptability and consideration of physical layer properties. In this paper, the dictionary is carefully chosen based on spatial source wavefield considers horizontal and vertical wavefield components having translational and rotational components. The horizontal source wavefield components coupled with wedges formed from reflectivity pair then convolved with seismic wavelet. The generated output is reflectivity inversion is based on the source wavefield dictionary. The proposed method is robust to noise as the dictionary used in inversion is built with the same spatial source wavefield; hence it could significantly improve the resolution of seismic data.