Reproducible material for A physics-aware, low-rank regularization for multidimensional deconvolution - Fuqiang Chen, Matteo Ravasi, and David Keyes submitted to Geophysics.
This repository is organized as follows:
- 📂 mdd_lrr: python library containing routines to perform mdd with factorization-based low-rank regularization;
- 📂 data: folder where input data must be placed. All related data to reproduce our research are available on https://zenodo.org/records/11207932
- 📂 notebooks: set of jupyter notebooks reproducing the experiments in the paper (see below for more details);
NOTE: due to their large size, the datasets used in this repository cannot be shared directly in this repository.
Refer to the README file in the data folder for more details.
The following notebooks are provided:
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📂 toy example:
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mdd_lrr_toy.ipynb: notebook performing mdd with random complex matrices for down- and up-going wavefield to demonstrate the convergence of the proposed factorization-based low-rank regularization;
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📂 OBC redatuming:
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Overthrust2d.ipynb: notebook performing the proposed factorization-based low-rank regularized mdd with the up- and down-going wavefield mimicing OBC survey.
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📂 target-oriented redatuming:
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SeamSubsalt.ipynb: notebook performing the proposed mdd. In this example, the datum for the up- and down-going wavefield are assumed to be below the salt body. The output of MDD is expected to represent the response of target area.
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📂 OBC redatuming with field data:
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Volve.ipynb: notebook performing the proposed mdd with Volve field data.
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To ensure the reproducibility of the results, we suggest using the environment.yml file when creating an environment.
Simply run:
./install_env.sh
It will take some time, if at the end you see the word Done! on your terminal you are ready to go. After that, you can simply install your package:
pip install .
or in developer mode:
pip install -e .
Remember to always activate the environment by typing:
conda activate mdd_lrr