 |
| Titel |
Identification of Some Zeolite Group Minerals by Application of Artificial Neural Network and Decision Tree Algorithm Based on SEM-EDS Data |
| VerfasserIn |
Efe Akkaş, H. Evren Çubukçu, Lutfiye Akin, Volkan Erkut, Yasin Yurdakul, Ali Ihsan Karayigit |
| Konferenz |
EGU General Assembly 2016
|
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250121899
|
| Publikation (Nr.) |
 EGU/EGU2016-788.pdf |
|
|
|
|
|
| Zusammenfassung |
| Identification of zeolite group minerals is complicated due to their similar chemical formulas
and habits. Although the morphologies of various zeolite crystals can be recognized under
Scanning Electron Microscope (SEM), it is relatively more challenging and problematic
process to identify zeolites using their mineral chemical data. SEMs integrated with energy
dispersive X-ray spectrometers (EDS) provide fast and reliable chemical data of minerals.
However, considering elemental similarities of characteristic chemical formulae of zeolite
species (e.g. Clinoptilolite ((Na,K,Ca)2 −3Al3(Al,Si)2Si13O3612H2O) and Erionite
((Na2,K2,Ca)2Al4Si14O36⋅15H2O)) EDS data alone does not seem to be sufficient for correct
identification. Furthermore, the physical properties of the specimen (e.g. roughness, electrical
conductivity) and the applied analytical conditions (e.g. accelerating voltage, beam
current, spot size) of the SEM-EDS should be uniform in order to obtain reliable
elemental results of minerals having high alkali (Na, K) and H2O (approx. %14-18)
contents.
This study which was funded by The Scientific and Technological Research Council of
Turkey (TUBITAK Project No: 113Y439), aims to construct a database as large as possible
for various zeolite minerals and to develop a general prediction model for the identification of
zeolite minerals using SEM-EDS data. For this purpose, an artificial neural network
and rule based decision tree algorithm were employed. Throughout the analyses,
a total of 1850 chemical data were collected from four distinct zeolite species,
(Clinoptilolite-Heulandite, Erionite, Analcime and Mordenite) observed in various
rocks (e.g. coals, pyroclastics). In order to obtain a representative training data
set for each minerals, a selection procedure for reference mineral analyses was
applied. During the selection procedure, SEM based crystal morphology data, XRD
spectra and re-calculated cationic distribution, obtained by EDS have been used for
the characterization of the training set. Consequently, for each zeolite species 250
EDS data (as elemental intensities) used for training and 200 ±50 analyses were
tested. Finally, two prediction models were developed. The constructed models with
various cross-correlation values (r) yielded an average accuracy of >91% for the best
predictions using C5.0 Decision Tree algorithm and back propagation artificial
neural network. Despite having similar accuracies, the developed models exhibit
different prediction behaviors for some zeolite minerals. The results demonstrate that
artificial neural network as a nonlinear tool and decision tree algorithm as a rule based
prediction model would be employed to provide considerably efficient and reliable
identification/classification of some zeolite minerals regardless of their similar elemental
compositions.
Keywords: mineral identification; zeolites; energy dispersive spectrometry; artificial
neural networks; decision tree. |
|
|
|
|
|
|