End to End solution that gives above 80% Accuracy

Seismic Facies Identification Challenge
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Seismic facies identification refers to the interpretation of facies type from the seismic reflector information. The key elements used to determine seismic facies and depositional setting are bedform internal and external configuration/geometry, lateral continuity, amplitude, frequency, and interval velocity.

The classification of seismic facies is an important first step in exploration, prospecting, reservoir characterization, and field development.

Classification and interpretation of depositional facies from the chronostratigraphic units can provide initial indication as to whether the area of interest is a viable hydrocarbon system and merits additional research.

Furthermore, seismic facies classification can help in the approximation of grain size, sorting, mineralogy, porosity distribution, and permeability of the various deposition units.

When combined with open hole logging data, DHI, and advanced processing such as AVO, it is possible to estimate recovery and the potential for an economically viable prospect.

In modern seismic interpretation workflows, seismic facies classification is often automated or partially automated using computer algorithms, such as clustering and supervised learning.

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Updated the solution bit with some fine tuning and reshaping. The solution that able to give above 80% accuracy now.

Colab Link of end 2 end solution - [Updated]

[Google Colab]

Above solution that handle →

  1. Loading the data
  2. Slicing the data
  3. Resize the data
  4. Design UNet
  5. Train the data
  6. Predict the test data
  7. Submission.npz

Github Link → https://github.com/saikrithik/Seismic-Facies-Identification-Challenge/blob/main/Seismic_Facies_Identification_Challenge_BASELINE.ipynb

Few things we can tryout →

  1. Augumenting the data
  2. Chunking and training insead of resizing the data
  3. PSPNet, FPN ( other networks ) :wink:
  4. Applying different filters

USEFULL NOTEBOOKS -

  1. segmentation_models/examples/multiclass segmentation (camvid).ipynb at master · qubvel/segmentation_models · GitHub
  2. [cnn-facies-classifier/notebooks/scratchpad.ipynb at master · thurbridi/cnn-facies-classifier · GitHub]
  3. AI_Crowd_Blitz_-3/LNDST/LNDST.ipynb at master · jayaramanjay97/AI_Crowd_Blitz_-3 · GitHub
  4. https://github.com/rekalantar/CT_lung_3D_segmentation/blob/master/CT_lung_segmentation.ipynb
  5. VolumMedSeg/notebooks/train_UNet_BRATS2019.ipynb at master · ViiSkor/VolumMedSeg · GitHub

USEFULL LINKS -

  1. https://github.com/frankkramer-lab/MIScnn [ 3D / 2D ]The open-source Python library MIScnn is an intuitive API allowing fast setup of image segmentation pipelines with state-of-the-art convolutional neural network and deep learning models in just a few lines of code.
  2. GitHub - microsoft/seismic-deeplearning: Deep Learning for Seismic Imaging and Interpretation · GitHub
  3. https://github.com/JesperDramsch/seismic-transfer-learning
  4. GitHub - wolny/pytorch-3dunet: 3D U-Net model for volumetric semantic segmentation written in pytorch · GitHub
  5. https://github.com/goodok/fastai_sparse
  6. GitHub - arnab39/FewShot_GAN-Unet3D: Tensorflow implementation of our paper: Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning · GitHub
  7. GitHub - black0017/MedicalZooPytorch: A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation · GitHub
  8. GitHub - anindox8/Ensemble-of-Multi-Scale-CNN-for-3D-Brain-Segmentation: Fully supervised, multi-class 3D brain segmentation in T1 MRI using an ensemble of diverse CNN architectures (3D FCN, 3D U-Net) with multi-scale input. · GitHub
  9. https://github.com/ShouYuqing/3D-UNet-for-Segmentation
  10. GitHub - fitushar/3DUnet_tensorflow2.0: This Repo is for implementation of 3D unet in Tensorflow 2.0v · GitHub
  11. https://neptune.ai/blog/image-segmentation-tips-and-tricks-from-kaggle-competitions
  12. GitHub - nikhilroxtomar/Deep-Residual-Unet: ResUNet, a semantic segmentation model inspired by the deep residual learning and UNet. An architecture that take advantages from both(Residual and UNet) models. · GitHub
  13. GitHub - nikhilroxtomar/Polyp-Segmentation-using-UNET-in-TensorFlow-2.0: Implementing polyp segmentation using the U-Net and CVC-612 dataset. · GitHub
  14. GitHub - shivangi-aneja/Multi-Modal-Brain-Segmentation: 3D Multi-modal (FLAIR and T1) Brain MRI Scan Segmentation using Generative Adversarial Learning · GitHub
  15. GitHub - ardamavi/3D-Medical-Segmentation-GAN: 3D Liver Segmentation with GAN · GitHub

FEW RECENT PAPERS-

  1. https://www.researchgate.net/publication/326307470_Deep_Learning_Applied_to_Seismic_Facies_Classification_a_Methodology_for_Training
  2. https://www.researchgate.net/publication/281783417_A_comparison_of_classification_techniques_for_seismic_facies_recognition
  3. https://library.seg.org/doi/10.1190/geo2019-0627.1
  4. Seismic Facies Analysis Based on Deep Learning | IEEE Journals & Magazine | IEEE Xplore
  5. Improved Automating Seismic Facies Analysis Using Deep Dilated Attention Autoencoders | IEEE Conference Publication | IEEE Xplore
  6. Client Challenge
  7. https://hanyang.elsevierpure.com/en/publications/facies-classification-using-semi-supervised-deep-learning-with-ps
  8. https://arxiv.org/pdf/1901.07659.pdf

Thanks to the people who contribute that help us to learn more and also big thanks to aicrowd for these amazing challenges :blush:

23 Likes
📝🏆 Seismic Facies Id Community Prize Winners (Round 1)
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seems amazing thanks

4 Likes
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Amazing bro. It gives a lot of info.

2 Likes