seismic-deeplearning

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# Features - generation of 3D output in test.py (3D segmentation maps) with *geometric* and *arithmetic* feature map averages in inline and crossline directions - added generation of segy files...

maxkazmsft

Add a module to do quality checks on the data, e.g. check if the data is within a pre-specified lower and upper bound!

sogandak

Support SEGY files that do not have geometry - consistent support of segy files

1

The current code uses segyio.tools.cube() to extract a volume of data from segy files that can easily be chuncked up. However, most segy files do not have geometry in them...

MJZawacki

sogandak

Fix seeds in both train and test python drive scripts to make sure results are reproducible on re-run on a single type of GPU. Switching GPUs will most likely change...

maxkazmsft

When using patch-based and section-based approaches, generate whole volume (score the entire test set) and write that volume into segy format at the end of scoring; for approaches where crosslines...

maxkazmsft

add unit test to check that each input slice has uniform distribution - facilitates uniform sampling of training data for better model accuracy

1

Related to #259 - this is essentially a test for it can test that for checkerboard dataset train and val patches have a uniform 50/50 class distribution across all patches

maxkazmsft

To test trained models are saved and loaded correctly.

yalaudah

add Azure ML Training Pipeline Jupyter notebook which showcases the use of recently added pipeline as detailed here: https://github.com/microsoft/seismic-deeplearning/blob/contrib/interpretation/deepseismic_interpretation/azureml_pipelines/README.md Probably a good idea to re-write this README into notebook format

maxkazmsft

create HRNet Azure ML notebook based on existing HRNet notebook - showcases the use of Azure ML

1

blocked by Azure ML pipelines

maxkazmsft