Full text loading...
-
Internal Multiple Suppression Using a Physics-Constrained Deep Neural Network
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 84th EAGE Annual Conference & Exhibition, Jun 2023, Volume 2023, p.1 - 5
Abstract
Summary
Internal multiples are common in field land data and can affect migration and subsequent interpretation. They generally need to be suppressed and eliminated. We propose a physics-constrained deep neural network (PCDNN) method based on the orthogonal constraint and ensemble learning to suppress internal multiples. The designed PCDNN includes three deep neural networks (DNNs), one input data, six output data, and six pseudo-label (PL) data. We use the adaptive virtual event (AVE) method to obtain the predicted internal multiples to calibrate the true internal multiples. The predicted internal multiples are fed into PCDNN as input data and mapped nonlinearly to three estimated true internal multiples. The total loss function for PCDNN takes characteristics of the orthogonality of primaries and internal multiples to avoid the residue of internal multiples in the de-multiple result. Also, the total loss function uses the ensemble learning loss term to improve the effectiveness of multiple suppression. Three examples are given to verify the effectiveness of our proposed method in internal multiple suppression.
© EAGE Publications BV