An estimation of RC recovery is needed to assess how representative the sample is of the ground that is extracted from. In iron ore the density of the material is an important consideration when assessing how much of the material has made it to the top of the hole. As downhole density variations can make considerable differences in apparent recovery. Contents So in this example we are going to:

Assays are not always able to provide all the information that we need for a geological problem. On occasion we might need to know the mineralogical density of a sample to estimate porosity or some other physical property of the minerals themselves. Normative mineralogy is a cheap solution to these problems if you know how to do it. In this post I will demonstrate how to calculate a limited but useful set of minerals from assay for iron ore similar methods can be used for other deposit types too.

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…

One of the failings of the previous model was that it didn’t encode texture, reviewing the stratigraphic units that we have in the map we can see that most of these units are encoded by both colour and texture. With this knowledge in hand we will try and create a model that uses a window rather than the single pixel that we’ve used in part 1. If you’ve not downloaded the data used in the first article please start there, if you have then we can begin by loading the data and viewing the stratigraphic units.