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:

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 touch on one of the many subtle issues around wireline logging and gamma logging in particular and it’s flow on effects. I have seen a few arguments on where to place the stratigraphic contact on a gamma log: above the peak, on the peak, below the peak, this then rapidly escalates to we need to get all the surfaces to snap to interpretations exactly! A solution for the problem might be as simple as “within the assay interval”.

Hi Everyone, A post has been doing the rounds from an exploration manager looking for a fieldy and geologist and was having trouble finding anyone. Considering the current environment this is likely to be an ongoing issue. I would love for a technical solution to this problem so lets run though a small set of considerations around commercial of the shelf sensing on a exploration drill rig. Imagery Ensure that the light output is stable in both intensity and colour balance, LEDs are a good choice here for practical reasons if you are willing to suffer higher maintenance costs and calibration issues quartz halogen bulbs are preferred.

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.

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.

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, 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 touch on one of the many subtle issues around wireline logging and gamma logging in particular and it’s flow on effects. I have seen a few arguments on where to place the stratigraphic contact on a gamma log: above the peak, on the peak, below the peak, this then rapidly escalates to we need to get all the surfaces to snap to interpretations exactly! A solution for the problem might be as simple as “within the assay interval”.

Hi Everyone, A post has been doing the rounds from an exploration manager looking for a fieldy and geologist and was having trouble finding anyone. Considering the current environment this is likely to be an ongoing issue. I would love for a technical solution to this problem so lets run though a small set of considerations around commercial of the shelf sensing on a exploration drill rig. Imagery Ensure that the light output is stable in both intensity and colour balance, LEDs are a good choice here for practical reasons if you are willing to suffer higher maintenance costs and calibration issues quartz halogen bulbs are preferred.

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…

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.

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.

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…

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 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.

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.

Hi All, After a while between posts I thought it would be interesting to talk about image classification and its application to scanned geological maps. GSWA has many many 1:250K and 1:100K maps where the only digital version is a scanned print, which depending on your use case might be ok but for machine learning and targeting leave a little to be desired. So let’s discuss a few methods of converting these scanned maps into vector datasets or at least a sensible raster representation.

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.

Hi All, After a while between posts I thought it would be interesting to talk about image classification and its application to scanned geological maps. GSWA has many many 1:250K and 1:100K maps where the only digital version is a scanned print, which depending on your use case might be ok but for machine learning and targeting leave a little to be desired. So let’s discuss a few methods of converting these scanned maps into vector datasets or at least a sensible raster representation.

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.

Hi All, After a while between posts I thought it would be interesting to talk about image classification and its application to scanned geological maps. GSWA has many many 1:250K and 1:100K maps where the only digital version is a scanned print, which depending on your use case might be ok but for machine learning and targeting leave a little to be desired. So let’s discuss a few methods of converting these scanned maps into vector datasets or at least a sensible raster representation.

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.

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 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 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 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 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 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 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, 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, 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, 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, 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, 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, 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 touch on one of the many subtle issues around wireline logging and gamma logging in particular and it’s flow on effects. I have seen a few arguments on where to place the stratigraphic contact on a gamma log: above the peak, on the peak, below the peak, this then rapidly escalates to we need to get all the surfaces to snap to interpretations exactly! A solution for the problem might be as simple as “within the assay interval”.

Hi Everyone, A post has been doing the rounds from an exploration manager looking for a fieldy and geologist and was having trouble finding anyone. Considering the current environment this is likely to be an ongoing issue. I would love for a technical solution to this problem so lets run though a small set of considerations around commercial of the shelf sensing on a exploration drill rig. Imagery Ensure that the light output is stable in both intensity and colour balance, LEDs are a good choice here for practical reasons if you are willing to suffer higher maintenance costs and calibration issues quartz halogen bulbs are preferred.

Hi All, Let’s touch on one of the many subtle issues around wireline logging and gamma logging in particular and it’s flow on effects. I have seen a few arguments on where to place the stratigraphic contact on a gamma log: above the peak, on the peak, below the peak, this then rapidly escalates to we need to get all the surfaces to snap to interpretations exactly! A solution for the problem might be as simple as “within the assay interval”.

Hi All, Let’s touch on one of the many subtle issues around wireline logging and gamma logging in particular and it’s flow on effects. I have seen a few arguments on where to place the stratigraphic contact on a gamma log: above the peak, on the peak, below the peak, this then rapidly escalates to we need to get all the surfaces to snap to interpretations exactly! A solution for the problem might be as simple as “within the assay interval”.

Hi All, Let’s touch on one of the many subtle issues around wireline logging and gamma logging in particular and it’s flow on effects. I have seen a few arguments on where to place the stratigraphic contact on a gamma log: above the peak, on the peak, below the peak, this then rapidly escalates to we need to get all the surfaces to snap to interpretations exactly! A solution for the problem might be as simple as “within the assay interval”.

Hi Everyone, A post has been doing the rounds from an exploration manager looking for a fieldy and geologist and was having trouble finding anyone. Considering the current environment this is likely to be an ongoing issue. I would love for a technical solution to this problem so lets run though a small set of considerations around commercial of the shelf sensing on a exploration drill rig. Imagery Ensure that the light output is stable in both intensity and colour balance, LEDs are a good choice here for practical reasons if you are willing to suffer higher maintenance costs and calibration issues quartz halogen bulbs are preferred.

Hi Everyone, A post has been doing the rounds from an exploration manager looking for a fieldy and geologist and was having trouble finding anyone. Considering the current environment this is likely to be an ongoing issue. I would love for a technical solution to this problem so lets run though a small set of considerations around commercial of the shelf sensing on a exploration drill rig. Imagery Ensure that the light output is stable in both intensity and colour balance, LEDs are a good choice here for practical reasons if you are willing to suffer higher maintenance costs and calibration issues quartz halogen bulbs are preferred.

Hi Everyone, A post has been doing the rounds from an exploration manager looking for a fieldy and geologist and was having trouble finding anyone. Considering the current environment this is likely to be an ongoing issue. I would love for a technical solution to this problem so lets run though a small set of considerations around commercial of the shelf sensing on a exploration drill rig. Imagery Ensure that the light output is stable in both intensity and colour balance, LEDs are a good choice here for practical reasons if you are willing to suffer higher maintenance costs and calibration issues quartz halogen bulbs are preferred.

Hi Everyone, A post has been doing the rounds from an exploration manager looking for a fieldy and geologist and was having trouble finding anyone. Considering the current environment this is likely to be an ongoing issue. I would love for a technical solution to this problem so lets run though a small set of considerations around commercial of the shelf sensing on a exploration drill rig. Imagery Ensure that the light output is stable in both intensity and colour balance, LEDs are a good choice here for practical reasons if you are willing to suffer higher maintenance costs and calibration issues quartz halogen bulbs are preferred.

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.