Logging stratigraphy on a drill rig to decide to continue or end the hole is a skill that every geologist should have. Ideally logging happens at the rig in realtime as we are often wanting to determine end of hole critera. Let’s consider how well hyperspectral and image data perform in classifing samples into prospective and non-prospective units. As usual we are going to use this dataset from from the C3DMM project.

Let’s talk about the performance of using chips vs hyperspectral to classify ore and waste in an Iron ore deposit. Before we go onto the technical work let’s discuss the practical uses of this information. Common use cases would be deciding if a sample is ore/waste, screening samples for further analysis or simply selecting the right sensor for you application and trading off cost/speed/performance/technical difficulty considerations. So with all that in mind I hope that you will find a smart application in your workplace.

Good Afternoon Spectroscopists, I though someone might be interested in using pytsg to process hyperspectral data. For this example we are going to use this dataset provided by CSIRO under a CC4 licence collected for the C3DMM project In this post we will use of PLS to predict Fe grade from the spectra. Let us start by installing the libraries that we are going to need. You can use the tsg file reader that I developed here or you can write your own…

Hi All, Let’s talk about improving these estimates or how to do a site specific adjustment. Revisiting the literature I found this paper Álvaro Corral, Álvaro González (2019). Power Law Size Distributions in Geoscience Revisited one could argue (but you won’t argue) that I should use a power law distribution for the cavities instead of an exponential, but that would require two parameters instead of one and it is easier to reason with less parameters at least initially.