Fully convolutional network for seismic horizon detection.
Seismic horizons are the borders between two rock layers with different physical properties. The task is to find these horizons all over the seismic cube and perform the binary segmentation: does this pixel is horizon or not.
Currently, the task of finding seismic horizons is solved by classical computer vision methods, which require preliminary marking of each new seismic cube and often break on a complex underground relief.
In this work, we introduce a new approach to this task using deep learning methods.
For training and validating we will use Netherlands F3 dataset containing the seismic cube and marked seismic facies. Seismic facies is the rock bodies between two seismic horizons, so we can easily perform transformation between facies and horizons.
Since the seismic cube is 3D data we can get two types of vertical sections along two axes. In seismic exploration, they are called inlines and crosslines. This is a very useful property that allows you to get more training examples. Below is an example of crossline and inline.
We performed train and validation split and get following shapes of data sets. It is important to note that we dropped from original dataset those inlines that we considered corrupted.
| Data split | Num of Inlines | Num of Crosslines | Size percentage |
|---|---|---|---|
| All | 581 | 701 | 100% |
| Train | 401 | 701 | 69% |
| Validation | 180 | 701 | 21% |
In this section we describe data preparation and in particular data augmentations.
In each classification task, there is a problem of class imbalance. And in our case, this problem is most acute: seismic horizons occupy a very small part of the sections of the seismic cube. The solution to the problem may be the correct selection of the thickness of the horizon. We can well make the horizon more than one pixel in thickness, because the marking of the horizon occurs with strong assumptions. Expanding the boundary, we also increase the likelihood that a seismic horizon is actually located in the marked area. The best experimental thickness is 5 and 7.
Let's see the class distribution.
| Class | Percentage if thickness is 1 | Percentage if thickness is 3 | Percentage if thickness is 5 | Percentage if thickness is 7 |
|---|---|---|---|---|
| Empty | 97.9% | 95.9% | 94.1% | 92.4% |
| Horizon | 2.1% | 4.1% | 5.9% | 7.6% |
Data augmentation helps to train models with better generalization, which is extremely beneficial to us because of the very limited dataset for training. During augmentation, sections of the seismic cube were interpreted as grayscale images. However, unlike the standard tasks of computer vision, we must choose augmentations very carefully, focusing on a different nature of the data. For training we chose the following augmentations:
All these augmentations was chosen by following our ideas about data. Seismic data is wave reflection data, and seismic horizons are the most clearly reflected waves. As we know, waves have two phases, which in our image are in the intervals from 0 to 0.5 and from 0.5 to 1. The sharpness of wave reflection in each phase can be estimated as the L1 distance to 0 or 1, respectively.
This is what underlies the seismic color map.
This information allows us to understand which augmentations can be applied to seismic data and which cannot. Perhaps the most important augmentation is the color inversion since it allows the neural network to learn the horizon color invariance. Seismic data is invariant to horizontal flipping, so choosing this augmentation was clear enough. Gaussian blur is also a very important augmentation, which removes unnecessary reflections and leaves only the clearest ones. All remaining augmentations are applied for reasons of better generalization of the model.
Since horizon detection is the binary segmentation problem we will use metrics and loss functions applied to segmentation. It is worth remembering that our task is faced with the problem of class imbalance, which also affects the choice of loss functions. Good choice for imbalanced classification (binary segmentation is a classification problem) is Dice and Jaccard (also known as Intersection over Union) metrics and loss functions. Also, we will use binary cross-entropy loss as well.
Finally, the loss function for our task will be defined as:
loss = alpha * Weighted_binary_cross_entropy(weight_1, weight_2) + beta * JaccardLoss + gamma * DiceLoss
Where alpha, beta, gamma, weight_1 and weight_2 are hyperparameters, chosen like 0.85, 1, 1, 0.45, 0.55.
And our main metric will be Intersection over Union.
In this work we use U-Net architecture with one input channel and one output channel, and as an improvement, we added a batch normalization. This model architecture showing good results in segmentation tasks with small amout of classes.
We conducted several experiments with different horizont thickness and train same model with same hyperparameters for 128 epochs.
Results of experiments are showed in this plot. At left side is training graph, at right side is validaton graph.
As we can see, the worst metrics values are shown by the model trained on the most imbalanced dataset with horizon thickness equal to one. All other models showing similar results with IoU value around 0.4.
The best result was shown by the model trained on the horizon mask with the thickness equal to five, but using visual analysis we chose another model, which was trained on thickness equal to seven.
The trained model shows good results in the segmentation of horizons well traced by the eye, however, it cannot cope with a complex underground relief. As we see in the picture below, the model cannot segment the horizon in a breaking fault from the bottom right and top right.
However, the presented model has an obvious advantage over traditional methods of correlation of seismic horizons: it is very wrong only in places of breaking faults, while classical models lose the horizon and are mistaken everywhere after a breaking faults.
This model can be used as a support for other methods for detecting seismic horizons, such as horizon tracking. To improve the performance of the model, information on breaking faults is needed, therefore, further research on their segmentation is necessary.
If you want to check the results or use the model in any way known to you, you can do this following this steps.
#Install required libraries
pip install -r requirements.txt
#Download data
python download.py
#Train your model (if needed)
python train.py --config config.txt --path data/train/
#Predict something
python predict.py --config config.txt --path data/valid/ --filename seismic.npy --suffix valid
#Check results
streamlit run explore.py