1,521 to 1,530 of 12,708 Results
May 20, 2022 -
Supporting Data for: Direct multi-modal inversion of geophysical logs using deep learning
Python Source Code - 5.1 KB -
MD5: 8f59527c03a6460aae2a377812b9ca64
Python code that can be used for generation of realizations |
May 20, 2022 -
Supporting Data for: Direct multi-modal inversion of geophysical logs using deep learning
Plain Text - 19.7 KB -
MD5: bb082061306cc1dc0afcd128f972d344
CC-BY-SA-4.0 license |
May 20, 2022 -
Supporting Data for: Direct multi-modal inversion of geophysical logs using deep learning
Python Source Code - 1.2 KB -
MD5: a8803f4a86664b8a22151a0d28926188
Python script for conversion from numpy to nc files, which are used for deposition |
May 20, 2022 -
Supporting Data for: Direct multi-modal inversion of geophysical logs using deep learning
Network Common Data Form - 9.2 MB -
MD5: ceb18211f836ff0eb87db0f7d449a04e
The testing dataset containing the realizations each consisting of series of angles, the stratigraphic vertical depths (SVD), and the vertical section (VS) coordinates of stratigraphic curves |
May 20, 2022 -
Supporting Data for: Direct multi-modal inversion of geophysical logs using deep learning
Network Common Data Form - 457.8 MB -
MD5: 4c1896f602b3feca1810047309d41c52
The training dataset containing the realizations each consisting of a series of angles, the stratigraphic vertical depths (SVD), and the vertical section (VS) coordinates of stratigraphic curves |
May 20, 2022 -
Supporting Data for: Direct multi-modal inversion of geophysical logs using deep learning
Python Source Code - 5.4 KB -
MD5: 8820b78733145a9149eb87620aee4d77
Python script to generate the dataset |
May 20, 2022 -
Supporting Data for: Direct multi-modal inversion of geophysical logs using deep learning
PNG Image - 158.3 KB -
MD5: 18ad687e904961ad85b2d1206d4e0319
Example of generated stratigraphic curves |
Plain Text - 6.6 KB -
MD5: c38eb51d654b31bbd519d44b0dbeaf5f
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Network Common Data Form - 13.6 KB -
MD5: 83b0789ffa752f0c369754e894a4eeb1
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Network Common Data Form - 291.4 MB -
MD5: c2edb32769cb54b101f2156a43046045
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