As an update to the last post on drill compositing, I’ve implemented a limited set of algorithms for the hard boundary compositing of drill hole intervals. It is important to note that the hard boundary aggregation algorithms must honor the smaller intervals in the drill hole. All of the implementations I have seen utilise a version of what I have implemented as the greedy method. One of the issues I have with the greedy algorithm is that there is no guarantee that the solution for a given drill hole is closest to the optimum i.

Contents Intro Configure Python Simulation Feature Extraction References Intro I have become sick of copy and pasting feature extraction code around so I’ve updated pytsg to include some basic feature extraction methods. To learn this new interface let’s discuss some common feature extraction methods for hyperspectral data, we are not going to touch on dimensionality reduction techniques like PCA as the interpretation is less obvious than scalar extraction techniques such as band ratios, polynomial fitting and gaussian decomposition.

Hi Everyone, I would like to make a short comment on drift of the wavelength features in hyperspectral instruments and cross instrument calibration. For the sake of my sanity and yours I’m only going to address wavelength position/alignment, spectral resolution is a slightly separate issue. The first point of clarification is that the wavelength standards have a dual purpose: to monitor the performance of the instrument and drift to allow you to correct the position of the spectral features to a known energy.

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 Everyone, I would like to make a short comment on drift of the wavelength features in hyperspectral instruments and cross instrument calibration. For the sake of my sanity and yours I’m only going to address wavelength position/alignment, spectral resolution is a slightly separate issue. The first point of clarification is that the wavelength standards have a dual purpose: to monitor the performance of the instrument and drift to allow you to correct the position of the spectral features to a known energy.

Hi Everyone, I would like to make a short comment on drift of the wavelength features in hyperspectral instruments and cross instrument calibration. For the sake of my sanity and yours I’m only going to address wavelength position/alignment, spectral resolution is a slightly separate issue. The first point of clarification is that the wavelength standards have a dual purpose: to monitor the performance of the instrument and drift to allow you to correct the position of the spectral features to a known energy.

Hi Everyone, I would like to make a short comment on drift of the wavelength features in hyperspectral instruments and cross instrument calibration. For the sake of my sanity and yours I’m only going to address wavelength position/alignment, spectral resolution is a slightly separate issue. The first point of clarification is that the wavelength standards have a dual purpose: to monitor the performance of the instrument and drift to allow you to correct the position of the spectral features to a known energy.

Hi Everyone, I would like to make a short comment on drift of the wavelength features in hyperspectral instruments and cross instrument calibration. For the sake of my sanity and yours I’m only going to address wavelength position/alignment, spectral resolution is a slightly separate issue. The first point of clarification is that the wavelength standards have a dual purpose: to monitor the performance of the instrument and drift to allow you to correct the position of the spectral features to a known energy.

For any one doing drill planning taking a guess at how long it will take to complete a drill program is one of the more interesting exercises. If you are luckly you look at last years drilling and use those numbers in the current estimate. But for people who: Don’t have last years drilling Are drilling some where totally new You can skip to the results from here:

Let’s run through the restoration of the pgsql backup file supplied by GSWA and expedio for all those times when you just want to have some nice data to play with. Let’s first download the backup file from aws, change the path of the file mdhdb_data_and_structure.backup.gz to suit your setup. curl https://exp-gswa-mdhdb-bkt01.s3.ap-southeast-2.amazonaws.com/mdhdb_data_and_structure.backup.gz --output mdhdb_data_and_structure.backup.gz Of course you have already downloaded and installed postgres if not follow the process here to sort yourself out if you are trying this on windows…(hahaha) you will need to install sed and something to unzip.

Let’s run through the restoration of the pgsql backup file supplied by GSWA and expedio for all those times when you just want to have some nice data to play with. Let’s first download the backup file from aws, change the path of the file mdhdb_data_and_structure.backup.gz to suit your setup. curl https://exp-gswa-mdhdb-bkt01.s3.ap-southeast-2.amazonaws.com/mdhdb_data_and_structure.backup.gz --output mdhdb_data_and_structure.backup.gz Of course you have already downloaded and installed postgres if not follow the process here to sort yourself out if you are trying this on windows…(hahaha) you will need to install sed and something to unzip.

Let’s run through the restoration of the pgsql backup file supplied by GSWA and expedio for all those times when you just want to have some nice data to play with. Let’s first download the backup file from aws, change the path of the file mdhdb_data_and_structure.backup.gz to suit your setup. curl https://exp-gswa-mdhdb-bkt01.s3.ap-southeast-2.amazonaws.com/mdhdb_data_and_structure.backup.gz --output mdhdb_data_and_structure.backup.gz Of course you have already downloaded and installed postgres if not follow the process here to sort yourself out if you are trying this on windows…(hahaha) you will need to install sed and something to unzip.

Let’s run through the restoration of the pgsql backup file supplied by GSWA and expedio for all those times when you just want to have some nice data to play with. Let’s first download the backup file from aws, change the path of the file mdhdb_data_and_structure.backup.gz to suit your setup. curl https://exp-gswa-mdhdb-bkt01.s3.ap-southeast-2.amazonaws.com/mdhdb_data_and_structure.backup.gz --output mdhdb_data_and_structure.backup.gz Of course you have already downloaded and installed postgres if not follow the process here to sort yourself out if you are trying this on windows…(hahaha) you will need to install sed and something to unzip.

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…

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, I want to help you solve one of those occasional problematic issues that come up every now and then, extracting historical tabular data from old reports. Let’s imagine that you’ve aquired a tenement and reading the historical reports it looks like you’re onto something great. But the only historical data that you have available is very old and unfortunately predates the nice digital formats that we have now.

Hi All, I would like to start my posting journey by talking about an issue that is near and dear to my heart: reading data. In Australia lucky geologists and data enthusiasts have access to a fantastic source of open file geoscientific data provided by our geological services such as GSWA and as tenements are released the data from then slowly becomes available as open file data under the fantastic CC4.

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…

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…

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.

Hi All, In this article we are going to integrate our estimate of background rate of cavities from caliper data we modelled in Part 1 with the likelyhood of detecting a cavity by drillhole spacing we generated in Part 2 . One of the issues with the simulation modelling estimating the background rates in Part 1 was the estimate of cavity distribution was done for the entire data set. As this is an Iron Ore data set from open file WAMEX data I know from experience that there are more cavities in certain stratigraphic units and alteration zones (hard cap).

Wecome back, Before integrating the data we generated in part 1 we are going to estimate the chance of intersecting a cavity on a blast pattern for a series of cavity sizes and blast pattern designs. To start the demonstration we build a baseline drill pattern of 5x7m with a fixed 0.27m hole diameter that we will keep fixed for all simulations. Now that we have a grid, we add a cavity of 1m diameter.

Hi All, Here is the first in a series of posts covering a bit of ground on cavity detection. From estimating the proportion of cavities in exploration drill holes to their chance of detection with MWD on a blast pattern. First off we define a cavity, for the sake of an exploration drill hole a simple definition might be a caliper measurement that flatlines at the maximum value. Looking over the histogram we can see a few points of interest:

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, I want to help you solve one of those occasional problematic issues that come up every now and then, extracting historical tabular data from old reports. Let’s imagine that you’ve aquired a tenement and reading the historical reports it looks like you’re onto something great. But the only historical data that you have available is very old and unfortunately predates the nice digital formats that we have now.

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.

Hi All, In this article we are going to integrate our estimate of background rate of cavities from caliper data we modelled in Part 1 with the likelyhood of detecting a cavity by drillhole spacing we generated in Part 2 . One of the issues with the simulation modelling estimating the background rates in Part 1 was the estimate of cavity distribution was done for the entire data set. As this is an Iron Ore data set from open file WAMEX data I know from experience that there are more cavities in certain stratigraphic units and alteration zones (hard cap).

Wecome back, Before integrating the data we generated in part 1 we are going to estimate the chance of intersecting a cavity on a blast pattern for a series of cavity sizes and blast pattern designs. To start the demonstration we build a baseline drill pattern of 5x7m with a fixed 0.27m hole diameter that we will keep fixed for all simulations. Now that we have a grid, we add a cavity of 1m diameter.

Hi All, Here is the first in a series of posts covering a bit of ground on cavity detection. From estimating the proportion of cavities in exploration drill holes to their chance of detection with MWD on a blast pattern. First off we define a cavity, for the sake of an exploration drill hole a simple definition might be a caliper measurement that flatlines at the maximum value. Looking over the histogram we can see a few points of interest:

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.

Hi All, In this article we are going to integrate our estimate of background rate of cavities from caliper data we modelled in Part 1 with the likelyhood of detecting a cavity by drillhole spacing we generated in Part 2 . One of the issues with the simulation modelling estimating the background rates in Part 1 was the estimate of cavity distribution was done for the entire data set. As this is an Iron Ore data set from open file WAMEX data I know from experience that there are more cavities in certain stratigraphic units and alteration zones (hard cap).

Wecome back, Before integrating the data we generated in part 1 we are going to estimate the chance of intersecting a cavity on a blast pattern for a series of cavity sizes and blast pattern designs. To start the demonstration we build a baseline drill pattern of 5x7m with a fixed 0.27m hole diameter that we will keep fixed for all simulations. Now that we have a grid, we add a cavity of 1m diameter.

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.

Hi All, In this article we are going to integrate our estimate of background rate of cavities from caliper data we modelled in Part 1 with the likelyhood of detecting a cavity by drillhole spacing we generated in Part 2 . One of the issues with the simulation modelling estimating the background rates in Part 1 was the estimate of cavity distribution was done for the entire data set. As this is an Iron Ore data set from open file WAMEX data I know from experience that there are more cavities in certain stratigraphic units and alteration zones (hard cap).

Wecome back, Before integrating the data we generated in part 1 we are going to estimate the chance of intersecting a cavity on a blast pattern for a series of cavity sizes and blast pattern designs. To start the demonstration we build a baseline drill pattern of 5x7m with a fixed 0.27m hole diameter that we will keep fixed for all simulations. Now that we have a grid, we add a cavity of 1m diameter.

Hi All, Here is the first in a series of posts covering a bit of ground on cavity detection. From estimating the proportion of cavities in exploration drill holes to their chance of detection with MWD on a blast pattern. First off we define a cavity, for the sake of an exploration drill hole a simple definition might be a caliper measurement that flatlines at the maximum value. Looking over the histogram we can see a few points of interest:

OK so you have decided to learn python, excel just isn’t cutting it anymore and the person you work with makes lots of nice plots in python and you want to be able to do that too…cool let’s begin. Here what we are going to do: How to get python installed Install an IDE Configure your environment Read a .csv or .xlsx Make a scatter plot Installing Python Firstly I’m going to offer my condolences for anyone with corporate managed computers, networks and especially those on windows, ANY of those things is going to make your life more difficult.

Hi All, I would like to start my posting journey by talking about an issue that is near and dear to my heart: reading data. In Australia lucky geologists and data enthusiasts have access to a fantastic source of open file geoscientific data provided by our geological services such as GSWA and as tenements are released the data from then slowly becomes available as open file data under the fantastic CC4.

Hi All, I would like to start my posting journey by talking about an issue that is near and dear to my heart: reading data. In Australia lucky geologists and data enthusiasts have access to a fantastic source of open file geoscientific data provided by our geological services such as GSWA and as tenements are released the data from then slowly becomes available as open file data under the fantastic CC4.

Hi All, I would like to start my posting journey by talking about an issue that is near and dear to my heart: reading data. In Australia lucky geologists and data enthusiasts have access to a fantastic source of open file geoscientific data provided by our geological services such as GSWA and as tenements are released the data from then slowly becomes available as open file data under the fantastic CC4.

Hi All, I would like to start my posting journey by talking about an issue that is near and dear to my heart: reading data. In Australia lucky geologists and data enthusiasts have access to a fantastic source of open file geoscientific data provided by our geological services such as GSWA and as tenements are released the data from then slowly becomes available as open file data under the fantastic CC4.