Publication

Abstract : The seismic horizons estimation from the seismic data is essential for structural and stratigraphic modeling of reservoirs. Until now, manual interpretation and semi-automated techniques were used to estimate the seismic horizon from the seismic data. However, the seismic horizon estimation takes more time and needs a human expert for analysis. To overcome those limitations, we propose a novel method to estimate the seismic horizon using a deep sparse convolutional autoencoder (DSCA). The convolutional layers in the DSCA network extract the complex features from the seismic data which improves the accuracy of seismic horizon estimation. The DSCA network is trained in a supervised way with the labeled seismic data. The performance of the proposed method is tested and compared with an existing method based on quantitative metrics such as Mean Squared Error (MSE), Coefficient of Determination (r2), and Pearson Correlation Coefficient (PCC). Experimental results prove that the proposed method has shown better results compared to the existing method.