This dataverse includes the underlying data of the publication: Lee, D., Karadimitriou, N., Ruf, M., & Steeb, H. (2022). Detecting micro fractures: A comprehensive comparison of conventional and machine-learning based segmentation methods. Solid Earth (EGU). Submitted.
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11 to 14 of 14 Results
readme.txt
Jul 7, 2021 - Fracture network segmentation
Plain Text - 2.6 KB - MD5: 1d388e58b5fd9037ef315ff386a6da77
This file contains instruction of how to operate the codes.
Sato.py
Jul 7, 2021 - Fracture network segmentation
Plain Text - 908 B - MD5: 14c9f9428402bc7a550102032725a288
This file contains the workflow of the Sato method
trained_model_U_net_27.hdf5
Jul 7, 2021 - Fracture network segmentation
Hierarchical Data Format - 89.9 MB - MD5: f8caecb9a1ddb337df1df2b6be0d82ed
Trained model of the U-net model. This file is required to reproduce the results.
unet_v2_mod.py
Jul 7, 2021 - Fracture network segmentation
Plain Text - 3.5 KB - MD5: c4dbef168efd4edca836958329652022
The code to create the U-net architecture
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