DeepSeismic

This repository shows you how to perform seismic imaging and interpretation on Azure. It empowers geophysicists and data scientists to run seismic experiments using state-of-art DSL-based PDE solvers and segmentation algorithms on Azure.

The repository provides sample notebooks, data loaders for seismic data, utilities, and out-of-the-box ML pipelines, organized as follows:

• sample notebooks: these can be found in the examples folder - they are standard Jupyter notebooks which highlight how to use the codebase by walking the user through a set of pre-made examples
• experiments: the goal is to provide runnable Python scripts that train and test (score) our machine learning models in the experiments folder. The models themselves are swappable, meaning a single train script can be used to run a different model on the same dataset by simply swapping out the configuration file which defines the model. Experiments are organized by model types and datasets - for example, "2D segmentation on Dutch F3 dataset", "2D segmentation on Penobscot dataset" and "3D segmentation on Penobscot dataset" are all different experiments. As another example, if one is swapping 2D segmentation models on the Dutch F3 dataset, one would just point the train and test scripts to a different configuration file within the same experiment.
• pip installable utilities: we provide cv_lib and deepseismic_interpretation utilities (more info below) which are used by both sample notebooks and experiments mentioned above

DeepSeismic currently focuses on Seismic Interpretation (3D segmentation aka facies classification) with experimental code provided around Seismic Imaging.

Quick Start

There are two ways to get started with the DeepSeismic codebase, which currently focuses on Interpretation:

• if you'd like to get an idea of how our interpretation (segmentation) models are used, simply review the HRNet demo notebook
• to run the code, you'll need to set up a compute environment (which includes setting up a GPU-enabled Linux VM and downloading the appropriate Anaconda Python packages) and download the datasets which you'd like to work with - detailed steps for doing this are provided in the next Interpretation section below.

If you run into any problems, chances are your problem has already been solved in the Troubleshooting section.

Pre-run notebooks

Notebooks stored in the repository have output intentionally displaced - you can find full auto-generated versions of the notebooks here:

• HRNet Penobscot demo: [HTML] [.ipynb]
• Dutch F3 dataset: [HTML] [.ipynb]

Azure Machine Learning

Azure Machine Learning enables you to train and deploy your machine learning models and pipelines at scale, ane leverage open-source Python frameworks, such as PyTorch, TensorFlow, and scikit-learn. If you are looking at getting started with using the code in this repository with Azure Machine Learning, refer to Azure Machine Learning How-to to get started.

Interpretation

For seismic interpretation, the repository consists of extensible machine learning pipelines, that shows how you can leverage state-of-the-art segmentation algorithms (UNet, SEResNET, HRNet) for seismic interpretation, and also benchmarking results from running these algorithms using various seismic datasets (Dutch F3, and Penobscot).

To run examples available on the repo, please follow instructions below to:

1. Set up the environment
2. Download the data sets
3. Run example notebooks and scripts

Setting up Environment

Follow the instructions below to read about compute requirements and install required libraries.

Compute environment

We recommend using a virtual machine to run the example notebooks and scripts. Specifically, you will need a GPU powered Linux machine, as this repository is developed and tested on Linux only. The easiest way to get started is to use the Azure Data Science Virtual Machine (DSVM) for Linux (Ubuntu). This VM will come installed with all the system requirements that are needed to create the conda environment described below and then run the notebooks in this repository.

For this repo, we recommend selecting a multi-GPU Ubuntu VM of type Standard_NC12. The machine is powered by NVIDIA Tesla K80 (or V100 GPU for NCv2 series) which can be found in most Azure regions.

NOTE: For users new to Azure, your subscription may not come with a quota for GPUs. You may need to go into the Azure portal to increase your quota for GPU VMs. Learn more about how to do this here: https://docs.microsoft.com/en-us/azure/azure-subscription-service-limits.

Package Installation

To install packages contained in this repository, navigate to the directory where you pulled the DeepSeismic repo to run:

conda env create -f environment/anaconda/local/environment.yml

This will create the appropriate conda environment to run experiments. If you run into problems with this step, see the troubleshooting section.

Next, you will need to install the common package for interpretation:

conda activate seismic-interpretation
pip install -e interpretation

Then you will also need to install cv_lib which contains computer vision related utilities:

pip install -e cv_lib

Both repos are installed in developer mode with the -e flag. This means that to update simply go to the folder and pull the appropriate commit or branch.

During development, in case you need to update the environment due to a conda env file change, you can run

conda env update --file environment/anaconda/local/environment.yml

from the root of DeepSeismic repo.

Dataset download and preparation

This repository provides examples on how to run seismic interpretation on two publicly available annotated seismic datasets: Penobscot and F3 Netherlands. Their respective sizes (uncompressed on disk in your folder after downloading and pre-processing) are:

• Penobscot: 7.9 GB
• Dutch F3: 2.2 GB

Please make sure you have enough disk space to download either dataset.

We have experiments and notebooks which use either one dataset or the other. Depending on which experiment/notebook you want to run you'll need to download the corresponding dataset. We suggest you start by looking at HRNet demo notebook which requires the Penobscot dataset.

Penobscot

To download the Penobscot dataset run the download_penobscot.sh script, e.g.

data_dir="$HOME/data/penobscot"
mkdir -p "$data_dir"
./scripts/download_penobscot.sh "$data_dir"

Note that the specified download location should be configured with appropriate write permissions. On some Linux virtual machines, you may want to place the data into /mnt or /data folder so you have to make sure you have write access.

To make things easier, we suggested you use your home directory where you might run out of space. If this happens on an Azure Data Science Virtual Machine you can resize the disk quite easily from Azure Portal - please see the Troubleshooting section at the end of this README regarding how to do this.

To prepare the data for the experiments (e.g. split into train/val/test), please run the following script (modifying arguments as desired):

cd scripts
python prepare_penobscot.py split_inline --data-dir=$data_dir --val-ratio=.1 --test-ratio=.2
cd ..

F3 Netherlands

To download the F3 Netherlands dataset for 2D experiments, please follow the data download instructions at this github repository (section Dataset). Atternatively, you can use the download script

data_dir="$HOME/data/dutch"
mkdir -p "${data_dir}"
./scripts/download_dutch_f3.sh "${data_dir}"

Download scripts also automatically create any subfolders in ${data_dir} which are needed for the data preprocessing scripts.

At this point, your ${data_dir} directory should contain a data folder, which should look like this:

data
├── splits
├── test_once
│   ├── test1_labels.npy
│   ├── test1_seismic.npy
│   ├── test2_labels.npy
│   └── test2_seismic.npy
└── train
    ├── train_labels.npy
    └── train_seismic.npy

To prepare the data for the experiments (e.g. split into train/val/test), please run the following script:

# change working directory to scripts folder
cd scripts

# For section-based experiments
python prepare_dutchf3.py split_train_val section --data-dir=${data_dir}/data


# For patch-based experiments
python prepare_dutchf3.py split_train_val patch --data-dir=${data_dir}/data --stride=50 --patch=100

# go back to repo root
cd ..

Refer to the script itself for more argument options.

Run Examples

Notebooks

We provide example notebooks under examples/interpretation/notebooks/ to demonstrate how to train seismic interpretation models and evaluate them on Penobscot and F3 datasets.

Make sure to run the notebooks in the conda environment we previously set up (seismic-interpretation). To register the conda environment in Jupyter, please run:

python -m ipykernel install --user --name seismic-interpretation

Experiments

We also provide scripts for a number of experiments we conducted using different segmentation approaches. These experiments are available under experiments/interpretation, and can be used as examples. Within each experiment start from the train.sh and test.sh scripts under the local/ (single GPU) and distributed/ (multiple GPUs) directories, which invoke the corresponding python scripts, train.py and test.py. Take a look at the experiment configurations (see Experiment Configuration Files section below) for experiment options and modify if necessary.

Please refer to individual experiment README files for more information.

• Penobscot
• F3 Netherlands Patch
• F3 Netherlands Section

Configuration Files

We use YACS configuration library to manage configuration options for the experiments. There are three ways to pass arguments to the experiment scripts (e.g. train.py or test.py):

• default.py - A project config file default.py is a one-stop reference point for all configurable options, and provides sensible defaults for all arguments. If no arguments are passed to train.py or test.py script (e.g. python train.py), the arguments are by default loaded from default.py. Please take a look at default.py to familiarize yourself with the experiment arguments the script you run uses.

• yml config files - YAML configuration files under configs/ are typically created one for each experiment. These are meant to be used for repeatable experiment runs and reproducible settings. Each configuration file only overrides the options that are changing in that experiment (e.g. options loaded from defaults.py during an experiment run will be overridden by arguments loaded from the yaml file). As an example, to use yml configuration file with the training script, run:

  python train.py --cfg "configs/hrnet.yaml"
  

• command line - Finally, options can be passed in through options argument, and those will override arguments loaded from the configuration file. We created CLIs for all our scripts (using Python Fire library), so you can pass these options via command-line arguments, like so:

  python train.py DATASET.ROOT "/mnt/dutchf3" TRAIN.END_EPOCH 10
  

Pretrained Models

HRNet

To achieve the same results as the benchmarks above you will need to download the HRNet model pretrained on ImageNet. We are specifically using the HRNet-W48-C pre-trained model; other HRNet variants are also available here - you can navigate to those from the main HRNet landing page for object detection.

Unfortunately, the OneDrive location which is used to host the model is using a temporary authentication token, so there is no way for us to script up model download. There are two ways to upload and use the pre-trained HRNet model on DS VM:

• download the model to your local drive using a web browser of your choice and then upload the model to the DS VM using something like scp; navigate to Portal and copy DS VM's public IP from the Overview panel of your DS VM (you can search your DS VM by name in the search bar of the Portal) then use scp local_model_location username@DS_VM_public_IP:./model/save/path to upload
• alternatively, you can use the same public IP to open remote desktop over SSH to your Linux VM using X2Go: you can basically open the web browser on your VM this way and download the model to VM's disk

Viewers (optional)

For seismic interpretation (segmentation), if you want to visualize cross-sections of a 3D volume (both the input velocity model and the segmented output) you can use segyviewer. To install and use segyviewer, please follow the instructions below.

segyviewer

To install segyviewer run:

conda env create -n segyviewer python=2.7
conda activate segyviewer
conda install -c anaconda pyqt=4.11.4
pip install segyviewer

To visualize cross-sections of a 3D volume, you can run segyviewer like so:

segyviewer "${HOME}/data/dutchf3/data.segy"

Benchmarks

Dense Labels

This section contains benchmarks of different algorithms for seismic interpretation on 3D seismic datasets with densely-annotated data.

Below are the results from the models contained in this repo. To run them check the instructions in folder. Alternatively, take a look in for how to run them on your own dataset

Netherlands F3

Source	Experiment	PA	FW IoU	MCA
Alaudah et al.	Section-based	0.905	0.817	.832
	Patch-based	0.852	0.743	.689
DeepSeismic	Patch-based+fixed	.869	.761	.775
	SEResNet UNet+section depth	.917	.849	.834
	HRNet(patch)+patch_depth	.908	.843	.837
	HRNet(patch)+section_depth	.928	.871	.871

Penobscot

Trained and tested on the full dataset. Inlines with artifacts were left in for training, validation and testing. The dataset was split 70% training, 10% validation and 20% test. The results below are from the test set

Source	Experiment	PA	IoU	MCA
DeepSeismic	SEResNet UNet + section depth	1.0	.98	.99
	HRNet(patch) + section depth	1.0	.97	.98

Reproduce benchmarks

In order to reproduce the benchmarks, you will need to navigate to the experiments folder. In there, each of the experiments are split into different folders. To run the Netherlands F3 experiment navigate to the dutchf3_patch/local folder. In there is a training script [(train.sh) which will run the training for any configuration you pass in. Once you have run the training you will need to run the test.sh script. Make sure you specify the path to the best performing model from your training run, either by passing it in as an argument or altering the YACS config file.

To reproduce the benchmarks for the Penobscot dataset follow the same instructions but navigate to the penobscot folder.

Scripts

• parallel_training.sh: Script to launch multiple jobs in parallel. Used mainly for local hyperparameter tuning. Look at the script for further instructions

• kill_windows.sh: Script to kill multiple tmux windows. Used to kill jobs that parallel_training.sh might have started.

• run_all.sh: similar to parallel_training.sh above, provides a multiprocess execution on an ND40 VM with 8 GPUs. Designed to work with test_all.sh script below. Trains 8 models concurrently.

• run_distributed.sh: sequentially launches distributed training jobs, which should produce the same results on Dutch F3 dataset with patch based methods as the single-GPU training (just takes less time per model to train). Also designed to work with test_all.sh script below.

• test_all.sh: after running run_all.sh and run_distributed.sh scripts above, this script scores single-GPU-trained and multi-GPU-trained models in the repo to reproduce the results given in the table.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

Submitting a Pull Request

We try to keep the repo in a clean state, which means that we only enable read access to the repo - read access still enables one to submit a PR or an issue. To do so, fork the repo, and submit a PR from a branch in your forked repo into our staging branch.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Build Status

Build	Branch	Status
Legal Compliance	staging
Legal Compliance	master
Core Tests	staging
Core Tests	master
Notebook Tests	staging
Notebook Tests	master

Troubleshooting

For Data Science Virtual Machine conda package installation issues, make sure you locate the anaconda location on the DSVM, for example by running:

which python

A typical output will be:

someusername@somevm:/projects/DeepSeismic$ which python
/anaconda/envs/py35/bin/python

which will indicate that anaconda folder is /anaconda. We'll refer to this location in the instructions below, but you should update the commands according to your local anaconda folder.

Data Science Virtual Machine conda package installation errors

It could happen that you don't have sufficient permissions to run conda commands / install packages in an Anaconda packages directory. To remedy the situation, please run the following commands

rm -rf /anaconda/pkgs/*
sudo chown -R $(whoami) /anaconda

After these commands complete, try installing the packages again.

Data Science Virtual Machine conda package installation warnings

It could happen that while creating the conda environment defined by environment/anaconda/local/environment.yml on an Ubuntu DSVM, one can get multiple warnings like so:

WARNING conda.gateways.disk.delete:unlink_or_rename_to_trash(140): Could not remove or rename /anaconda/pkgs/ipywidgets-7.5.1-py_0/site-packages/ipywidgets-7.5.1.dist-info/LICENSE.  Please remove this file manually (you may need to reboot to free file handles)  

If this happens, similar to instructions above, stop the conda environment creation (type Ctrl+C) and then change recursively the ownership /anaconda directory from root to current user, by running this command:

sudo chown -R $USER /anaconda

After these command completes, try creating the conda environment in environment/anaconda/local/environment.yml again.

Model training or scoring is not using GPU

To see if GPU is being used while your model is being trained or used for inference, run

nvidia-smi

and confirm that you see your Python process using the GPU.

If not, you may want to try reverting to an older version of CUDA for use with PyTorch. After the environment has been set up, run the following command (by default we use CUDA 10) after running conda activate seismic-interpretation to activate the conda environment:

conda install pytorch torchvision cudatoolkit=9.2 -c pytorch

To test whether this setup worked, right after you can open ipython and execute the following code

import torch
torch.cuda.is_available() 

The output should say "True".

If the output is still "False", you may want to try setting your environment variable to specify the device manually - to test this, start a new ipython session and type:

import os
os.environ['CUDA_VISIBLE_DEVICES']='0'
import torch                                                                                  
torch.cuda.is_available() 

The output should say "True" this time. If it does, you can make the change permanent by adding

export CUDA_VISIBLE_DEVICES=0

to your $HOME/.bashrc file.

GPU out of memory errors

You should be able to see how much GPU memory your process is using by running

nvidia-smi

and see if this amount is close to the physical memory limit specified by the GPU manufacturer.

If we're getting close to the memory limit, you may want to lower the batch size in the model configuration file. Specifically, TRAIN.BATCH_SIZE_PER_GPU and VALIDATION.BATCH_SIZE_PER_GPU settings.

How to resize Data Science Virtual Machine disk

1. Go to the Azure Portal and find your virtual machine by typing its name in the search bar at the very top of the page.

2. In the Overview panel on the left-hand side, click the Stop button to stop the virtual machine.

3. Next, select Disks in the same panel on the left-hand side.

4. Click the Name of the OS Disk - you'll be navigated to the Disk view. From this view, select Configuration on the left-hand side and then increase Size in GB and hit the Save button.

5. Navigate back to the Virtual Machine view in Step 2 and click the Start button to start the virtual machine.