Alfiesan / 3DMPM

Learning 3D Mineral Prospectivity from 3D Geological Models Using Convolutional Neural Networks

3DMPM

This is the implementation of the 3DMPM architecture described in this paper:

Learning 3D Mineral Prospectivity from 3D Geological Models Using Convolutional Neural Networks,

by Hao Deng, Yang Zheng, Jin Chen*, Shuyan Yu, Zhankun Liu, Xiancheng Mao

Hardware requirements

• GeForce GTX 1050 Ti or higher

Program language

• MATLAB (eigenfunctions)
• C++ (ScanProjection)
• Python (CNNnetwork)

Dependencies required

ScanProjection

• glad 0.1.29
• glfw 3.2.1
• libpng 1.6.17
• Zlib 1.2.8

CNNnetwork

• Ubuntu 18.04
• Python 3.6
• NumPy 1.14.5
• TensorFlow 1.14.0
• TensorBoard 1.10.0

Usage

1. Run main.m in "eigenfunctions" library to result in a series of Laplace-Beltrami eigenvalues and eigenfunctions.
2. Execute the Visual Studio solution file ScanProjection.sln in "ScanProjection" library to project shape descriptors into images *.bin.
   The paths and directionaries in params.ini should be specified. And the dimension of properties is specified by using macro in params.h as:

    #define KDims 16 // dimension of properties

To set the projection program, you need to specify the input and output directories in params.ini:

    [meshPath]
    YOUR_3D_MODEL_PATH
    
    [propPath]
    YOUR_PROPERTY_CSV_PATH
    
    [voxelPath]
    YOUR_VOXEL_CSV_PATH
    
    [binDir]
    YOUR_BIN_FILE_OUTPUT_DIRECTIONARY
    
    [pngDir]
    YOUR_FILE_FILE_OUTPUT_DIRECTIONARY

User can switch off the output of png files in params.ini by setting

    [withPng]
    0

3. Run the network training procedure finetune.py with loading parameters pretrained on ImageNet on Linux.
4. Run the testing procedure (after executing training) on Linux:

    sh for_cycle_2.sh

,specifying

    tf.flags.DEFINE_integer('pre_size', <your_prediction_batch_size>, 'prediction size')
    tf.flags.DEFINE_integer('iter_epoch', <your_batchs_per_epoch>, 'pre_size data per iter_epoch')

in classifier_v4.py.