Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal
This work addresses the time-consuming and parameter-dependent nature of seismic multiple removal for practitioners, though it is incremental as it applies an existing U-net architecture to a new domain.
The paper tackled the problem of suppressing multiples in seismic data processing by proposing a deep learning-based alternative to traditional Radon transform methods, achieving competitive results with reduced complexity and preserving data characteristics while removing artifacts.
Seismic processing often requires suppressing multiples that appear when collecting data. To tackle these artifacts, practitioners usually rely on Radon transform-based algorithms as post-migration gather conditioning. However, such traditional approaches are both time-consuming and parameter-dependent, making them fairly complex. In this work, we present a deep learning-based alternative that provides competitive results, while reducing its usage's complexity, and hence democratizing its applicability. We observe an excellent performance of our network when inferring complex field data, despite the fact of being solely trained on synthetics. Furthermore, extensive experiments show that our proposal can preserve the inherent characteristics of the data, avoiding undesired over-smoothed results, while removing the multiples. Finally, we conduct an in-depth analysis of the model, where we pinpoint the effects of the main hyperparameters with physical events. To the best of our knowledge, this study pioneers the unboxing of neural networks for the demultiple process, helping the user to gain insights into the inside running of the network.