Greetings,
I did some experiments with airborne radiometric data in a region in southern Brazil and I would like to know your suggestion about the following topic:
I tried to classify high, medium and low values of radiometric variables in the region by empirical methods (visualization only). Then I summarize the results in a table (Fig.1) where high values were attributed a red color, while medium and low were attributed yellow and blue, respectively. I did not estimate a range of values to define of what is high or medium, for example. I only checked the alkaline suites (labeled on the image) responses in comparison with their surroundings to classify.
A example of the grids used as the basis of this classification are presented in the Fig.2 while the Fig.1 represents the table.
I would like to know a alternative of how can I try to find a better classification system for this type of variables (which consist most of ratios maps).
I thought in first normalize the data using the min-max normalization, where all the gridded images will have a range between 0 and 1. All my grids have different range of values and units.
My advisor told me to use a technique of “mean + 2 or 3 standard deviation” to identify anomalies. But since the radiometric data are not presented in a normal distribution, I could not use this method. mean + 3 sd are usually identified as outliers in normal distribution and do not represent anomalies. So I think this idea could be discarded.
1) Is the min-max normalization a good idea? Or I should think in another type of normalization to analyze all the variables? Is there a more automated-driven approach that I could follow to classify the data?
The min-max normalization is given by the equation in the link: https://en.wikipedia.org/wiki/Normalization_(statistics)
I have seen some works using z-score (standard score) for radiometric data. I could be wrong, but this type of normalization is suitable for when your population is normally distribute (Gaussian distribution), which is not the case for radiometric data.
The second step was to apply a Principal Component Analysis (PCA) or Self-Organizing Maps (SOM) on it. I would like to create a map of PC1 and PC2, for example. Something similar with the Lima and Marfurt 2018 (p.2279) maps (Fig.3). However, the authors did not cite the routine used in MATLAB.
2) Is there a option to create a similar map of Lima and Marfurt (2018) using Oasis?
If someone can suggest some references about the topic and that was performed using Oasis Montaj or ArcGIS routines I would be grateful.
References:
Lima and Marfurt 2018. https://www.researchgate.net/publication/327611793_Principal_component_analysis_and_K-means_analysis_of_airborne_gamma-ray_spectrometry_surveys
Best regards,
Yes, min-max normalization is a good idea. But it may not provide improvement in term of performance in every case. In case, min-max normalization fails, you can use mean normalization or log transformation. And another thing is that PCA doesn't assure better performance. It only reduces the data. If PCA will make the classification better or worse - that's totally up to the practical experiment.
Dear Ferreira da Silva:
First of all, it is necessary to know the objective of your research because it defines what you should do and wait. You are referring to the task of "classifying aerospetrometric data", but it is important to know why you are doing this classification? For example, three situations diferentes:
1- You could be interested only in classifying the behavior of the region based on the observed redioactivity. In this case, an analysis similar to the one you mention is enough to classify the area in its relative radiometric behavior: high, medium or low.
2- You may be interested in locating radiometric anomalies for uranium or mining prospecting purposes. In this case, you should define a normal field level and from it define what you consider anomaly. There are several criteria for this.
3- You may be interested in using the results of the aerospetrometric survey for geological mapping. In this case, you should study the behavior of the radiometric variables in each of the formations or lithologies present in the area and, through an adequate statistical analysis, conclude if their separation is possible.
If you take into account the results of your figure 1, you will see that there are few possibilities of success to separate all the objects.
If you look at the results of your figure 2, you will see that there are some regularities that would allow you to recognize some of the existing objects in your region.
In my personal opinion, there is no better alternative to perform the geological classification, but it is possible to find the best combinations if you have sufficient knowledge about the geological environment being studied. There are multiple examples of the use of spectrometric information in geological investigations. Something that has been demonstrated in many works is the fact that the original spectrometric variables are not very expressive and because of this relationship a wide variety of complex indices have been designed to improve the results.
In relation to the use of the main components, it is a technique widely used to find new variables that are not correlated with each other and that often allow a better interpretation of the results. If you want to use this technique, you can use the corresponding extension existing in Geosoft's Oasis Montaj and obtain results similar to those of Lima and Marfurt 2018 that you mention in your initial comments. It is also possible to use the PCA from QGIS or ArcGIS.
I hope I have been helpful and we can keep in touch,
Willy Rodríguez
Dear Ferreira da Silva:
By using Oasis montaj you can perform Principal Component and Factor Analysis Principal Component Analysis (PCA) and Factor Analysis which are two methods that can help reveal simpler patterns within a complex set of variables. In particular, these methods seek to discover if the observed variables can be explained largely or entirely in terms of a much smaller number of variables called factors. In mineral exploration, the most common application of these multivariate analysis methods is to characterize and map inter-relationships within high volume surface geochemistry data sets. Data volumes are a growing problem as geochemists seek to extract more information and knowledge from data sets with up to 50 or more variables. The desire to simplify processing and analysis of large data sets is renewing interest in PCA and Factor Analysis algorithms and presentation. Requests from major exploration groups for these capabilities led to Geosoft developing PCA and Varimax Fator Analysis. For more information on Principal Component and Factor Analysis, see Chapter 3 - Exploration Geochemistry System (Geochemistry™) Tutorial and User Guide.
As well as you can try to use 256 channel radiometric processing package RPS
Good luck
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