Classifying hot water chemistry: Application of multivariate statistics

Prihadi Sumintadireja[1], Dasapta Erwin Irawan*[1], Yuano Rezky[2], Prana Ugiana Gio[3], Anggita Agustin[1]

[1] Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Jalan Ganesa No. 10, Bandung 40132

[2] Ministry of Energy and Mineral Resources

[3] Faculty of Math and Natural Sciences, Universitas Sumatera Utara, Jl. dr. T. Mansur No. 9, Medan 20155

* email: dasaptaerwin[@]outlook[.]co[.]id

* twitter: @dasaptaerwin ORCID (0000-0002-1526-0863)

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versioning

The following slides use versioning system on “Keynote”. This version has some improvements based on the presentation and some discussions

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license

I claim no right other than attribution

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Background

Classification is very important

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Background

Classification leads to characterisation of geothermal system

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Background

Classification: Direct method

Indirect method

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Background

Classification: Direct method (geology, drilling, etc) → strongly qualitative

Indirect method

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Background

Classification: Direct method (geology, drilling, etc) → strongly qualitative Indirect method → supports direct method, more quantitative

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Method

Here we used: Hydrochemistry,

Multivariate analysis, R programming

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Method

Here we used: Hydrochemistry (relatively cheap),

Multivariate analysis, R programming

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Method

Here we used: Hydrochemistry (relatively cheap),

Multivariate analysis (robust and powerful), R programming

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Method

Here we used: Hydrochemistry (relatively cheap),

Multivariate analysis (robust and powerful), R programming (robust, powerful, and free)

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Method

Samples = 11 Locations = Gorontalo

Sources = EBTKE Dataset and code =

available at zenodo.org (free repo)

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Method

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We combine:

linear regression from multi-regression technique with

axis rotation from PCA and CA.

Method

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Results and discussions

|atemp>=27.1

B< 3.27

elv>=78.5

elv>=209.5

4887n=11

2276n=9

1204n=6

195n=2

1708n=4

4420n=3

1.664e+04n=2

1.529e+04n=1

1.799e+04n=1

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Results and discussions

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Variables (elements) extracted from the dataset.

We detect some strong correlations among the following elements.

Results and discussions

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Results and discussions

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Our preliminary remarks is that we have three system running in the area (see the dots).

Results and discussions

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Results and discussions

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Closing remarks

Regression tree technique has failed to read the data structure.

Collinearity effect is major problem

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Closing remarks

PCA and cluster analysis have successfully classify the samples.

Need more samples to validate training model.

Hopefully we can make a useable model to classify other samples.

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Closing remarks

Proposed work around:

manual variable selection then re-run the regression

use other technique without regression principles, in this case: PCA and CA

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Acknowledgment

ITB for funding this research under 2016 ITB Research Grant Scheme.

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Thank you

Classifying hot water chemistry: Application of multivariate statistics

for more communications with the authors please:

- send your email to dasaptaerwin[@]outlook[.]co[.]id

- mention on twitter: @dasaptaerwin

- connect ORCID (0000-0002-1526-0863)

Dataset and R code are stored in RG: http://dx.doi.org/10.13140/RG.2.1.3510.1205

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Thank you

R for all

@dasaptaerwin

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