Several areas of Earth with large accumulations of oil and gas also have huge deposits of salt below the surface. Salt bodies are known for their propensity to form nice oil traps. However, knowing where large salt deposits are precisely is very difficult. Professional seismic imaging still requires expert human interpretation of salt bodies. This leads to very subjective, highly variable renderings. More alarmingly, it leads to potentially dangerous situations for oil and gas company drillers. That's why the oil & gas industry is now employing AI-based approaches to automatically identify subsurface salt bodies. This presentation showcases how Deep Learning is used to scan underground seismic images, looking for potentially resource-rich areas. Python code included and publicly available at: https://github.com/neaorin/kaggle-tgs-challenge

1. Technology Solutions Professional, Data & AI @ Microsoft
source:xkcd.com

2. The Problem

4. Oil Traps

5. Salt Dome Traps

7. github.com/neaorin/kaggle-tgs-challenge

8. The Data
TRAIN: 4000 images and masks
101x101, grayscale
TEST: 18000 images
101x101, grayscale

9. The Data
VALIDATION: X images and masks
101x101, grayscale
TRAIN: 4000 - X images and masks
101x101, grayscale

10. The Metric: IoU (Intersection over Union)

11. The Tools

12. Hardware
GeForce GTX 1080 Ti
332 GFLOPS
$769
Tesla K80
1,864 GFLOPS
$1,798
Tesla P100
4,036 GFLOPS
$6,598

13. Going Cloud
NC6
Tesla K80
1,864 GFLOPS
$0.9 / hour
NC6 v2
Tesla P100
4,036 GFLOPS
$2.07 / hour
N-Series VMs

14. https://azure.microsoft.com/en-us/free

15. https://azure.microsoft.com/en-us/free/students

16. Software
• Python
• Numpy
• Pandas
• Scikit-learn
• Scikit-image
• …

17. Azure Data Science Virtual Machines
https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/

18. Azure DSVM for Linux (Ubuntu)
https://azuremarketplace.microsoft.com/en-gb/marketplace/apps/microsoft-ads.linux-data-science-vm-
ubuntu

19. The Approach

20. What Can Machine Vision Do Today?

21. Input Output

22. OutputInput
w11
w21
w31
Weights
Activation
Outputs

23. 3
Error back propagation
Feedforward of information
Loss Function (L)
≠2
Optimizer
Gradient of the loss with respect
to each weight:
𝜕L
𝜕w
= ?
w11
w21
w31
Gradient Descent
𝜕L
𝜕w

24. https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050

25. https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050

27. https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050

28. U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation https://arxiv.org/abs/1505.04597

29. Deconvolutions

30. inputs = Input((im_height, im_width, im_chan))
s = Lambda(lambda x: x / 255) (inputs)
c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (s)
c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (c1)
p1 = MaxPooling2D((2, 2)) (c1)
c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (p1)
c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (c2)
p2 = MaxPooling2D((2, 2)) (c2)
...
c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (p4)
c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (c5)
...
u8 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c7)
u8 = concatenate([u8, c2])
c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (u8)
c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (c8)
u9 = Conv2DTranspose(8, (2, 2), strides=(2, 2), padding='same') (c8)
u9 = concatenate([u9, c1], axis=3)
c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (u9)
c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (c9)
outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9)
model = Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=[iou])

31. U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation https://arxiv.org/abs/1505.04597

32. Overfitting

33. Overfitting

34. Overfitting

35. Get More Data!
DATA

36. Image Augmentation
Original
Flip
Shear
Rotate

37. Image Augmentation with Keras
image_datagen = ImageDataGenerator(
horizontal_flip=True,
rotation_range=20,
shear_range=0.3,
zoom_range=0.25,
fill_mode='reflect')

38. Image Augmentation with imgaug
image_datagen = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.GaussianBlur((0, 3.0)),
iaa.Dropout(0.02),
iaa.AdditiveGaussianNoise(scale=0.01*255),
iaa.AdditiveGaussianNoise(loc=32,
scale=0.0001*255),
iaa.Affine(translate_px={"x": (-40, 40)})])

39. Test-Time Augmentation

40. Overfitting - Dropout Layers

41. Cross-Validation

42. www.image-net.org

43. Pre-Trained Architectures on ImageNet Data

44. Transfer Learning

45. Loss Function
Binary Cross-Entropy (BCE)

46. Loss Function
Lovász Loss
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in
neural networks https://arxiv.org/abs/1705.08790
https://github.com/bermanmaxim/LovaszSoftmax

47. http://wiki.fast.ai/index.php/Lesson_1_Notes
Gradient Descent

48. Optimizers
http://cs231n.github.io/neural-networks-3/

49. http://ruder.io/optimizing-gradient-descent/index.html

50. Learning Rate

51. Learning Rate Annealing

52. Learning Rate – Reduce on Plateau
reduce_lr = ReduceLROnPlateau(
monitor='val_iou’,
patience=20,
factor=0.5,
min_lr=0.0001)
history = model1.fit(x_train, y_train,
callbacks=[reduce_lr],
...)

53. Learning Rate – Cosine Annealing

54. Visualization

55. Visualization
tb = TensorBoard
(log_dir=f'tb_logs/{model_name}’,
batch_size=batch_size)

56. Postprocessing: A Jigsaw Puzzle
https://www.kaggle.com/vicensgaitan/salt-jigsaw-puzzle/notebook

57. Other Things to Try Out
Deep Supervision https://arxiv.org/abs/1505.02496
Snapshot Ensembles https://arxiv.org/abs/1704.00109
Hypercolumns https://arxiv.org/abs/1411.5752
'Squeeze & Excitation' Blocks https://arxiv.org/abs/1808.08127
Conditional Random Fields https://arxiv.org/abs/1502.03240
Convolutional Block Attention Module https://arxiv.org/abs/1807.06521
Stochastic Weight Averaging https://arxiv.org/abs/1803.05407

58. From Testing
to Production

59. run = experiment.submit(config=TensorFlow(
entry_script=entry_script,
script_params=script_params,
compute_target=compute_target,
use_gpu=True,
use_docker=True,
pip_requirements_file_path='requirements.txt'))

60. https://azure.microsoft.com/en-us/services/machine-learning-service/

61. Thank You