STACKING SEISMIC DATA BASED ON PRINCIPAL COMPONENT ANALYSIS

Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional equal-weight seismic stacking method cannot obtain optimal performance when the ambient noise is extremely strong. We propose applying a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. We use both synthetic and field data examples to demonstrate the performance of the presented method.