Deep Compressed Learning for 3D Seismic Inversion

• URL: http://arxiv.org/abs/2311.00107v1
• Date: Tue, 31 Oct 2023 19:34:26 GMT
• Title: Deep Compressed Learning for 3D Seismic Inversion
• Authors: Maayan Gelboim, Amir Adler, Yen Sun, Mauricio Araya-Polo
• Abstract summary: We consider the problem of 3D seismic inversion from pre-stack data using a very small number of seismic sources. The proposed solution is based on a combination of compressed-sensing and machine learning frameworks.
• Score: 5.926203312586109
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We consider the problem of 3D seismic inversion from pre-stack data using a very small number of seismic sources. The proposed solution is based on a combination of compressed-sensing and machine learning frameworks, known as compressed-learning. The solution jointly optimizes a dimensionality reduction operator and a 3D inversion encoder-decoder implemented by a deep convolutional neural network (DCNN). Dimensionality reduction is achieved by learning a sparse binary sensing layer that selects a small subset of the available sources, then the selected data is fed to a DCNN to complete the regression task. The end-to-end learning process provides a reduction by an order-of-magnitude in the number of seismic records used during training, while preserving the 3D reconstruction quality comparable to that obtained by using the entire dataset.

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