# Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal

arXiv: https://arxiv.org/abs/2206.12112 · HTML: https://mlsanity.com/paper/2206.12112/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Train faster, generalize better: Stability of stochastic gradient descent](https://mlsanity.com/paper/1509.01240/) (2015)
- [Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions](https://mlsanity.com/paper/2003.01826/) (2020)
- [The Marginal Value of Adaptive Gradient Methods in Machine Learning](https://mlsanity.com/paper/1705.08292/) (2017)
- [On Empirical Comparisons of Optimizers for Deep Learning](https://mlsanity.com/paper/1910.05446/) (2019)
- [An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters](https://mlsanity.com/paper/2201.08465/) (2022)

## Built on by (most cited first, 1 of 1)

- [Deep Diffusion Models for Seismic Processing](https://mlsanity.com/paper/2207.10451/) (2022)

## Abstract (arXiv)

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.