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Adaptive Subtraction of Post-Stack Surface Multiples Using the Pseudo-Seismic-Data-Based Convolutional Neural Network
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DOI：10.1109/LGRS.2024.3408145.png)
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Abstract
En 中文
Adaptive subtraction plays a crucial role in the surface-related multiples elimination (SRME) method. Following the acquisition of predicted surface multiples, the traditional adaptive subtraction method, based on a matching algorithm, needs to identify suitable filter operators to optimize the predicted surface multiples with the actual surface multiples present in the original data. To enhance the suppression of surface multiples, we have developed an adaptive subtraction method using the pseudo-seismic-data-based convolutional neural network (P-CNN). P-CNN takes collections of predicted surface multiples as input and can produce an output that matches the actual surface multiples after training. Application to both synthetic and field data demonstrates that the P-CNN method effectively suppresses surface multiples in seismic data. Results from complex synthetic data reveal that compared with the traditional adaptive subtraction method, the P-CNN method enhances the signal-to-noise ratio (SNR) by 3.41 dB while reducing the computation time by approximately two-thirds. Furthermore, in comparison to the CNN method, the P-CNN improves suppression results without significantly increasing computational costs.
Keywords:
Transforms
Convolution
Signal to noise ratio
Geoscience and remote sensing
Surface waves
Feature extraction
Convolutional neural networks
Adaptive subtraction
convolutional neural network (CNN)
pseudo-seismic data
pseudo-seismic-data-based CNN (P-CNN)
surface multiples
Journal
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16.4
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Citations:
5.1K