CNN for ASI
Code for the paper:
Convolutional Neural Networks for Automated Seismic Interpretation,
A. U. Waldeland, A. C. Jensen, L. Gelius and A. H. S. Solberg
The Leading Edge, July 2018
EAGE E-lecture: Seismic interpretation with deep learning (YouTube)
This repository contains python/pytorch code for applying Convolutional Neural Networks (CNN) on seismic data. The input is a segy-file containing post-stack seismic amplitude data. The training labels are given as images of the training slices, colored to indicate the classes.
Setup to get started
CNNs requires time-demanding computations, consider using a computer with a fast NVIDIA GPU with at least 6GB ram.
- Install the required python packages (REQUIREMENTS.txt)
- Clone this repository
- Download the demo data set here. This is the F3 Netherlands dataset - originally made available by OpenDTect - and available via the MalenoV Project.
- Locate the '.segy'-file, rename it to 'data.segy' and put it in the 'F3'-folder.
How to use tensorboard (for visualization)
- Open a terminal
- cd to the code-folder
- run: tensorboard --logdir='log'
- Open a web-browser and go to localhost:6006
More information can be found here.
Usage
- train.py - train the CNN
- test.py - Example of how the trained CNN can be applied to predict salt in a slice or the full cube. In addition it shows how learned attributes can be extracted.
Files
In addition, it may be useful to have a look on these files
- texture_net.py - this is where the network is defined
- batch.py - provide functionality to generate training batches with random augmentation
- data.py - load/save data sets with segy-format and labeled slices as images
- tb_logger.py - connects to the tensorboard functionality
- utils.py - some help functions
- test_parallel.py - An implemenation of test.py supporting multi-gpu prediction (thanks to Max Kaznady).
Using a different data set and custom training labels
If you want to use a different data set, do the following:
- Make a new folder where you place the segy-file
- Make a folder for the training labels
- Save images of the slices you want to train on as 'SLICETYPE_SLICENO.png' (or jpg), where SLICETYPE is either 'inline', 'crossline', or 'timeslice' and SLICENO is the slice number.
- Draw the classes on top of the seismic data, using a simple image editing program with the class colors. Currently up to six classes are supported, indicated by the colors: red, blue, green, cyan, magenta and yellow.
Contact
Email: anders.u.waldeland@gmail.com