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comparative petrographic analysis of volcanic rocks in 5 different areas using r
- 1. Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R Jejen Ramdani Muhamad Fauzan Septiana Dr. Dasapta Erwin Irawan, ST., M.T. Achmad Darul November 2 - 3 2016 V.2
- 2. Table of content Background Result and Discussion ResultConclusion4 Material and Method 3 1 2 ResultClosing5 3/10/2016 2 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R
- 3. Background 01 02 03 04 PETROGRAPHICAL Method in geological mapping for rock classification RSoftware Introduce a quantitative analysis, based on multivariable statistics principles Visualization 1. Mineral percentage data set 2. Reducing the number of variables 3. Creating a new set of variables 4. Extracting clusters ClusterMethod 1. PCA 2. CA 3 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 3/10/2016
- 4. MATERIALS AND METHOD Mount Wangi-Purworejo (WAN) (Rina Wahyuningsih, 1992) Banyuresmi-Bogor (BAN) (Daud Yusup Tanghamap, 2009) Mount Sangkur-Garut (SAN) (Andi Wisnu, 1989) Kromong-Palimanan (KRM) (Jaka Hadinata, 2009) Mount Lamongan- Probolinggo (LAM) (Jhonny, 2006) 4 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 3/10/2016 Volcanic System in 5 Location
- 5. Sample TOTAL SAMPLE = 89 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 5 3 Sample (pyroclastics) tuffaceous sands 1 Sample sedimentary deposit 85 Sample (igneous rocks) from basalt to andesite 3/10/2016
- 6. Research Flow The Concept of PCA and CA (Gio and Irawan. D., 2015) 6 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 3/10/2016
- 7. Research Flow The Concept of PCA and CA (Gio and Irawan. D., 2015) 7 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R The PCA will reduce the number of variables and creates a set of new variables (or grouping of old variables), while CA will make a classification of samples based on the data structure. 3/10/2016
- 8. Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 8 Figure 2. Variables Factor Map-PCA Result and Discussion 3/10/2016
- 9. Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 9 Figure 3 Individuals Factor Map (PCA) Clustering of Variables 3/10/2016
- 10. We identified three clusters with various strong variables (see Figure 4): 1. Cluster 1 BAN with a strong influence of: chlorite, calcite, and quartz; 2. Cluster 2 LAM, KRM, SAN, WAN, and BAN with strong influence of: piroxene, plagioklas, olivine, glass,clay mineral, foraminifera etc 3. Cluster 3 KRM, WAN, and BAN with strong control of piroxen, glass, plagioklas, olivin. Figure 4 Clusplot PCA 10 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 3/10/2016 1 3 2
- 11. Figure 5 Dendogram from CA 11 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 3/10/2016 We identified three clusters with various strong variables (see Figure 5): 1. Cluster 1 LAM, KRM, SAN, WAN, and BAN with a strong influence of vulkanik minerals olv, prx, plg, glass etc 2. Cluster 2 WAN with strong influence of: clay mineral and foraminifera. 3. Cluster 3 BAN with strong control of kalsit, quartz, clay mineral, and klorit. 1 2 3
- 12. 12 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 3/10/2016 Conclusion Based on the results of the PCA and CA showed different results, we think the Dendogram is better, because the dendogram (figure 5) shows the 3 classification based on mineral composition that show a significant difference, namely cluster 1 Quaternary volcanic rock, cluster 2 sedimentary rocks, and cluster 3 tertiary volcanic rocks. we perceive that multivariable statistics can support visual petrographic analysis by reading the data structure, selecting stronger variables and extracting clusters.
- 13. Closing 10/31/2016 Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 13 We hope this talk can raise awareness to open source software and how it helps to push open science movement to the top. This slide, along with full text, data and R code will be available soon on Open Science Framework Repository (www.osf.io) For more infos please drop us an email to jejenramdani28@gmail.com or mention @dasaptaerwin on Twitter.
- 14. Comparative Petrographic Analysis of Volcanic Rocks in 5 Different Areas Using R 14 Thank You 3/10/2016