Building the best possible 3D model from observed geological data is critical before grade estimation is performed. For many decades, 3D modelling of faults has been a challenging task. Currently, explicit or implicit modelling techniques are widely used to model faults.
In real-world data sets, missing values are unavoidable for various reasons. These missing values are typically represented by NaNs, default placeholders, or simply left as blank entries. Depending on the extent of missing data, this can significantly reduce the performance of statistical methods.