Jiang, L.; Castagna, J.P.; Zhang, Z.; Russell, B. Prediction of Reflection Seismic Low-Frequency Components of Acoustic Impedance Using Deep Learning. Minerals2023, 13, 1187.
Jiang, L.; Castagna, J.P.; Zhang, Z.; Russell, B. Prediction of Reflection Seismic Low-Frequency Components of Acoustic Impedance Using Deep Learning. Minerals 2023, 13, 1187.
Jiang, L.; Castagna, J.P.; Zhang, Z.; Russell, B. Prediction of Reflection Seismic Low-Frequency Components of Acoustic Impedance Using Deep Learning. Minerals2023, 13, 1187.
Jiang, L.; Castagna, J.P.; Zhang, Z.; Russell, B. Prediction of Reflection Seismic Low-Frequency Components of Acoustic Impedance Using Deep Learning. Minerals 2023, 13, 1187.
Abstract
The unreliable prediction of the low frequency components from inverted acoustic impedance causes uncertainty in quantitative seismic interpretation. To address this issue, we first calculate various seismic and geological attributes that contain low frequency information, such as relative geological age, interval velocity, and integrated instantaneous amplitude. Then, we develop a method to predict the low frequency content of seismic data using these attributes, their high frequency components, and recurrent neural networks. Next, we test how to predict the low frequency components using stacking velocity obtained from velocity analysis. Using all the attributes and seismic data, we propose a supervised deep learning method to predict the low frequency components of the inverted acoustic impedance. The results obtained in both synthetic and real data cases show that the proposed method can improve the prediction accuracy of the low frequency components of the inverted acoustic impedance, with the best improvement in a real data example of 57.7% compared with the impedance predicted using well log interpolation.
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
