Importing Scalar Data Exercise (optional)
This method of creating a new scalar dataset is usually used to import assay and coordinate data. We will start by creating a .CSV file and entering some imaginary assay data.
1) Using the 'Export' function from either the pop up menu or the 'Edit' menu select 'to csv (scalars)' or 'to csv (params)'. Give the new .CSV file a name and write it to the TSG data folder.
2) Using a spread sheet program (e.g. MS Excel) open this file, create a new column of imaginary assay data, (make sure you give the new column a heading) save this file in .CSV format overwriting the original, and then close the file (if you don't close the file TSG will be denied permission to access it).
3) Activate the first of the blank columns in your Log Screen (left mouse button). Now select ‘Import' from the 'File' menu, which opens the new scalar dialogue.
4) Give the new scalar a name or leave this option blank if you want to use the data column heading from the .CSV file as the scalar name.
Scatter Screen
Alongside the Log screen the Scatter Plot screen forms the heart of TSG's data analysis capabilities. The Scatter Plot screen allows you to assign any scalar values to X or Y axes and a third scalar to the colour scale (Z) in up to 12 simultaneously displayed graphs. Graph windows can also display frequency histograms for any selected scalar.
Many TSG datasets are composed of a variety of data types including spectral data, geochemistry and coordinate data. The Scatter plot screen is designed to allow you to display and analyse all of these data types as an integrated data set. In addition, each plot window in the Scatter screen is linked so that the selected data point/sample/spectrum in one screen is highlighted in all the other screens currently on display.
By assigning coordinate data to X and Y axes the Scatter Plot screen can be used to display sample spatial distribution in plan or section. This allows the spatial distribution of geochemistry and alteration mineralogy to be simultaneously investigated. This functionality combined with the Floater also allows the simultaneous analysis of hundreds or even thousands of spectra while at the same time allowing you to keep track of individual sample's spectral or mineralogical characteristics.
Floater window
The floating spectrum window (or Floater) is one of TSG most useful features. It is a resizable floating spectrum display window that allows the user to view the spectrum of the 'active' or 'selected' sample from the Scatter, Spectrum, Log or Stack screens. This means that while examining scatter plots containing many hundreds of samples showing geochemistry and other data you can still keep track of the spectral characteristics of each sample. The Floater is automatically updated as your cursor moves over and selects new data points or spectra in the Scatter, Spectrum, Log and Stack screens
The floater window also has additional functions and can be operated on in different modes other than spectrum. These include TSA (The Spectral Assistant) mode, Aux Match mode linescan and picture mode.
The TSA mode allows the user to observe the fit between the TSA calculated match and the unknown spectrum.
Aux match mode allows the user to match the unknown spectrum to a user defined spectral library (another TSG file). This user library is termed the “auxiliary” file, and can be attached to the current TSG file for overlay and 1:1 comparison. The aux match mode gives the goodness of fit between a library spectrum and an unknown spectrum.
Linescan mode allows you to view the linescan image data for the active spectrum in HyLogging data sets – which will show the exact piece of core that was measured.
Picture mode allows the user to display a .bip or .jpg image of a sample or core tray. Both The Spectral Assistant and Aux match methods are discussed in more detail in the next section.
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APPROACHES TO SPECTRAL ANALYSIS
Introduction
There are three main approaches to spectral analysis, these are:
Manual Interpretation
Mineral identification software o Automated
o User defined training libraries
Spectral parameters / digital mineralogy
Each approach is appropriate to different stages of an analysis process and dependent on the number of spectra/samples in a project.
Manual Interpretation
This approach describes the method of individually interpreting each spectrum. This approach requires an experienced operator and can recognise between 1-5 minerals in each spectrum. It is important at the early stages of a project, and for relatively small data sets of <500 spectra, but is generally slow and therefore expensive for large data sets. In addition, the results are often written descriptions, and subtle variations in mineral species such as crystallinity and composition are often missed and it is difficult to track subtle variations spatially
Automatic Mineral Identification
In this approach automated algorithms are used to interpret the spectral data. These algorithms are either dependent on an inbuilt training library (such as The Spectral Assistant) or on a user defined spectral library. A large number of spectra can be processed very fast using this approach, and the data are output in digital form that are easily compared with other data sets in TSG.
However, it is important to note that the method is very dependent on the quality of the training library being used and whether the spectra in the library are representative of the spectra in a project area. As automated methods are largely based on probabilities, the reported interpretations must be considered only to be the most likely results but not necessarily the only, or even the correct, results. This issue is less of a problem when dealing with very large data sets as the errors become part of a certain percentage of noise in the results, but becomes more significant with small data sets.
There is also a danger of users becoming reliant on the automated output without cross checking the results to see if they are happy with the output. It is vital for users to bring in geological knowledge to the results, and not treat the data blindly.
The Spectral Assistant
The Spectral Assistant (TSA) is an algorithm built into TSG for automated spectral unmixing and as an aid to mineralogical identification. TSA uses its training library either to match the spectrum against a single mineral OR to create a simulated mixture of two minerals that most closely resembles the input spectrum (whichever case has the lowest errors). TSA runs automatically in the background when creating a new TSG dataset, and the results are written into the TSG file as Protected System Scalars (i.e. they can be viewed and used in TSG but cannot be directly modified). These scalars are shown in the figure below.
TSA has been trained using a Training Library of over 500 samples, representing 45 “pure” minerals and 11 different non-mineral artefact spectra. The samples have been collected from many sites around the world, in an attempt to represent the diversity of samples of the same mineral.
However, before copying a table of TSA results straight into a final report, you should understand that the word "Assistant" was not included in the method's name just because it has a nice ring to it. It is a reminder that even TSA v6.1, which is a state-of-the-art automated spectral unmixing technique, is not perfect. TSA results should be authenticated by a human - ideally an expert. At least results should be checked against the reference spectra provided in the TSG reference libraries, to ensure consistency.
TSA Spectral Weights
A singleton (one mineral) result always has a weight of 1, meaning that the result accounts for the matched mineral and nothing else. In this case, the single mineral is reported in TSA Mineral 1, with the TSA Mineral 2 slot reporting NULL (as there is no 2nd mineral).
Amongst the best fitting mixtures of 2 minerals, the 2 weights are constrained to be positive and to sum to 1, and so can be interpreted as relative spectral proportions.
IMPORTANT NOTE: The proportions reported by TSA should not be interpreted literally as representing the weight or volume proportions of the components of the mixtures. This is because the fitting procedure is based on some idealised assumptions, the most fundamental of which is that there is log- linear mixing of pure spectra whose absorption features have standardised intensities.
In addition, it should be noted that although TSA has been trained to recognise most of the more common minerals, these are still based on a limited class of minerals that generate spectral responses in the SWIR (in the form of distinctive absorption features). Thus unexpected minerals and those that are not "SWIR-active", such as quartz and feldspar, will not contribute to the estimated total bulk composition of the sample. For instance, a sample containing 60% quartz, 20% standardised kaolinite and 20% standardised illite with a good signal-to-noise ratio is likely to be identified by TSA as a spectral mixture of about 50% kaolinite and 50% illite.
TSA Error
TSA reports a goodness-of-fit (or error) measure for each match, termed the Standardised Residual Sum of Squares, or SRSS. This value is saved as the TSA Error.
In real-world samples, it is unlikely that there will ever be a zero-error match. The samples in TSA's training library are not perfectly "pure" - they are real-world samples themselves, which vary. Even if all of the training samples were pure, a pure kaolinite sample, say, would probably not get a zero-error fit on account of the natural crystal variations in what we loosely call "kaolinite", and on account of optical effects (like scattering) that are impossible to control with a field spectrometer.
Therefore there is a range of SRSS scores over which TSA results should be taken seriously. In general, a result with an SRSS (TSA Error) score of 1000 or less should be considered viable. However, results with large SRSS scores, even around 2000, may still be worth a look, but not without critical scrutiny.
Possibility of Erroneous results
As indicated previously, before copying a table of TSA results straight into a final report, you should take care to authenticate TSA’s results and remember that it is wise to understand that these are the most “likely” results and are possibly not the “only” results. Note that TSG provides the means to add your own interpretations and comments into the sample headers, and also to build your own class scalars that can be based on TSA results and your own interpretation.
Under some circumstances, including issues quite beyond the algorithm’s control, TSA can produce erroneous results. Known causes may include any of the following:
1. The sample contains one or more minerals or mineral variants that are not in TSA's training library (see TSA Training Library).
2. The sample's composition is too complex for TSA to deal with as the spectrum comprises a mix of more than two minerals.
3. The sample's spectrum is too poor, either too dark, noisy or "aspectral" (featureless). (Note, however, that TSA will flag many of these cases.)
4. The sample contains a spectrally ambiguous component. (This problem may occur in conjunction with one or more of the above problems.) Some minerals have very similar spectral signatures - especially minerals from the same "family".
Examples include:
a) Illite and muscovite spectra (same family) differ only in the strengths of their water features; b) The features that distinguish epidote from chlorite (different families) may be masked if a strong water feature is also present.
5. The sample's spectrum was not measured correctly, due to a spectrometer miscalibration. Miscalibration of the instrument may result in a slight shifting of important absorption features. TSA is particularly sensitive to such shifts because it is designed to discriminate between certain minerals whose main spectral difference is a slightly different location of one important absorption feature (e.g. Muscovite from phengite).
User defined Spectral Libraries and Custom Libraries
A user defined or “Custom Library” can be simply other TSG file attached to a current file of unknown spectra. You can select and attach any other TSG file you wish to your current data file. In TSG Custom Libraries are termed Auxiliary (or Aux) libraries.
The idea behind Custom or Aux libraries is that it allows the building of characterised datasets that relate to specific projects, alteration styles or geologic environments. Other data files can also be used as custom libraries simply to compare the mineralogical similarities between two data sets, these might be two drill holes from the same project or they could be from new targets suspected to be similar to known mineralisation.
The big advantage of Custom Libraries is that they can comprise spectra of mixtures of spectra and actual examples of the spectra from a project area rather than pure mineral spectra from museum or private collections. This is in contrast with TSA, which has a library of single mineral spectra.
For best results Custom libraries need to be created by a spectral expert, as they need to be fully representative of the spectral variations in the project area or deposit style.
A Custom Library is usually a project specific TSG file built as a result of a detailed pilot study of the spectral characteristics of a project area. This type of Custom Library often comprises spectra that have been interpreted in detail and are representative of the full spectral variation in a project area. Often such a library is a customised version of a more general library, such as the GESSL ("Geological Environment Specific Spectral Library") library.