Detection of Low-frequency Shadow Zones using Quantum Swarm Evolutionary Matching Pursuit Decomposition (QSE-MPD)

Low frequency anomalies associated with the presence of hydrocarbons, plays the role of frequency dependent attenuation that can be considered as a direct hydrocarbon indicator. Matching pursuit decomposition (MPD) is a suitable tool for spectral decomposition of seismic data to be able to detect and visualize low frequency zones. However, this method has high computational cost. In this work a combination of two artificial intelligence method which are quantum-inspired evolutionary algorithm and particle swarm optimization, has been applied to accelerate the performance of MPD. The proposed method called quantum-swarm evolutionary-matching pursuit decomposition (QSE-MPD) has been utilized to detect low-frequency shadow zones of an offshore gas reservoir of Iran.