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

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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 ...
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Drill visualization

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I manage to install the drill visualisation software and received a bit of training on how to navigate it. The ultimate objective of the drill visualisation is to use the prediction data from the flask API to visualise the drill up and down composition in replay mode. this is purely experimental and might not yield the expected result since we haven't used 3d data this far when building the model.
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Developing the Machine learning Model

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Observation while building the model  I noticed that the data I am working with is labeled data, with features and a label outcome. This means I have to look at it from a prediction perspective. prediction can be a classification or a regression problem. In my case, the outcome has only two class labels. 0 or 1 meaning the drill bit is down for mining or up to move to the next mining ground. Hence binary classification. Overall there are obvious relationships and some that are not. Regression will work best in advancing to the output label being a fluctuating value between an upper threshold and lower threshold value. For the data, I initially loaded the data and use scatter matrix and histogram functions from pandas and matplotlib libraries to try and see if there are more relations or something new. To teach the model the relationship between the feature and the outcome, I used several algorithms and compared them to see which one performed best in this scenario. see code below:...
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