Mapping The Layers of The Ocean Floor With a Convolutional Neural Network

Guilherme G. D. Fernandes, Vitor S. P. P. Oliveira, João P. I. Astolfo
12/4/2024
cs.LGcs.AIcs.CV

No official code found. Best implementation: ada-ggf25/Mapping-The-Layers-of-the-Ocean-Floor-With-a-Convolutional-Neural-Network (1 star, MIT, PyTorch, last commit 2025-05-10).

Abstract

The mapping of ocean floor layers is a current challenge for the oil industry. Existing solution methods involve mapping through seismic methods and wave inversion, which are complex and computationally expensive. The introduction of artificial neural networks, specifically UNet, to predict velocity models based on seismic shots reflected from the ocean floor shows promise for optimising this process. In this study, two neural network architectures are validated for velocity model inversion and compared in terms of stability metrics such as loss function and similarity coefficient, as well as the differences between predicted and actual models. Indeed, neural networks prove promising as a solution to this challenge, achieving Sørensen-Dice coefficient values above 70%.

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Code Implementations(1)

ada-ggf25/Mapping-The-Layers-of-the-Ocean-Floor-With-a-Convolutional-Neural-NetworkCommunity59%
PyTorchMIT

Mapping the layers of the ocean floor with a Convolutional Neural Network to analyze geological structures and understand the seabed composition

10Jupyter NotebookAug 25, 20241 years agoMIT
bigdatageophysical-inversionsgeophysicsmachine-learningoceanography+8 more

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Cite this paper

@article{fernandes2024mapping,
  title  = {Mapping The Layers of The Ocean Floor With a Convolutional Neural Network},
  author = {Guilherme G. D. Fernandes and Vitor S. P. P. Oliveira and João P. I. Astolfo},
  year   = {2024},
  eprint = {2412.05329},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.05329v1}
}

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