Skip to content

An automatic interpretation method for lineaments in aeromagnetic datasets using Convolution Neural Networks.

License

Notifications You must be signed in to change notification settings

TomasNaprstek/Aeromagnetic_CNN

Repository files navigation

Convolution Neural Network for Aeromagnetic Interpretation of Lineaments

This project holds the trained CNN model used in the paper:

Tomas Naprstek and Richard S. Smith, (2022), "Convolutional neural networks applied to the interpretation of lineaments in aeromagnetic data," GEOPHYSICS 87: K1-K13. https://doi.org/10.1190/geo2020-0779.1

The CNN, NaprstekSmith_CNN_v1.h5, was developed using TensorFlow 2.0. The python script (which requires the TensorFlow 2.0 library) will load in the trained CNN model and apply it to a new aeromagnetic dataset using its moving-window approach. For an example of its usage, it can be run and applied to the test_model.csv dataset. This test model dataset was created using GRAV_MAG_PRISM (de Barros, A., S. Bongiolo, J. de Souza, F. J. F. Ferreira, and L. G. de Castro, 2013, Grav mag prism: a matlab/octave program to generate gravity and magnetic anomalies due to rectangular prismatic bodies, Brazilian Journal of Geophysics, 31, 347-363.)

About

An automatic interpretation method for lineaments in aeromagnetic datasets using Convolution Neural Networks.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages