3D Geological Data Model, NON -Euclidean Data, Geological Deep Learning, and Graph Convolutional Networks

After waiting some time, I received the 3D data for the panel that was mined on the days I selected to train the model against.

I intend to train two models, one with geological 3d data and the other with drill bit data. The plan is that they will work together to achieve the desired outcome or to find an algorithm that will consider both data types and produce the binary classification with a distance in meters of how much to pick up the drill bit by. This will ultimately save time because you will not need to pull up the bit to the default 8 meters knowing the geology or lithology of the sea floor.

Research into GCN (Graph Convolutional Networks )

I have also found this video that speaks about GCN. The content encourages using dynamic graph CNN for learning on point clouds, in our case the .xyz file. it also advised considering the points in a point cloud data as nodes in a directed graph. these models help learn from non- euclidean data like graphs and 3d objects using a different mathematical approach than the classic convolutional layers.

 Reference: 

https://www.youtube.com/watch?v=D3fnGG7cdjY

https://thegradient.pub/beyond-the-pixel-plane-sensing-and-learning-in-3d/