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JDR Vol.17 No.5 pp. 683-693
(2022)
doi: 10.20965/jdr.2022.p0683

Paper:

Introduction to Automated Tools for the Analysis of Volcanic Ejecta Built on an Analysis Platform Developed in the INeVRH Project

Atsushi Yasuda and Natsumi Hokanishi

Earthquake Research Institute, The University of Tokyo
1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan

Corresponding author

Received:
December 17, 2021
Accepted:
June 24, 2022
Published:
August 1, 2022
Keywords:
analysis platform, automated, texture, database, EPMA
Abstract

Volcanic activity is diverse. Therefore, a lot of volcanic ejecta need to be analyzed to properly assess the condition of a volcano. However, until now, rapid analysis in this regard has been insufficient. To accurately evaluate both the transition and the characteristics of eruptions, we constructed a platform to efficiently analyze volcanic ejecta incorporating a number of automatic processing functions into a data processing system composed of an electron beam microprobe analyzer (EPMA) and personal computers. A number of time-consuming tasks, such as crystal size distribution analysis and ash particle classification, were automated. Further, the analysis platform is equipped with a database function for collecting various analytical data as the basis for the future development of volcanology. Various quantitative values related to the composition and microtexture of the volcanic ejecta were collected and entered into the database. This paper introduces an outline of the system, collected data, and usage examples.

Cite this article as:
A. Yasuda and N. Hokanishi, “Introduction to Automated Tools for the Analysis of Volcanic Ejecta Built on an Analysis Platform Developed in the INeVRH Project,” J. Disaster Res., Vol.17, No.5, pp. 683-693, 2022.
Data files:
References
  1. [1] Integrated Program for Next Generation Volcano Research and Human Resource Development, https://www.kazan-pj.jp/en [accessed December 1, 2021]
  2. [2] F. Chayes, “A simple point counter for thin-section analysis,” Am. Mineral., Vol.34, Nos.1-2, pp. 1-11, 1949.
  3. [3] S. Couch, C. L. Harford, R. S. J. Sparks, and M. R. Carroll, “Experimental Constraints on the Conditions of Formation of Highly Calcic Plagioclase Microlites at the Soufrière Hills Volcano, Montserrat,” J. Petrol., Vol.44, No.8, pp. 1455-1475, 2003.
  4. [4] C. Martel, “Eruption Dynamics Inferred from Microlite Crystallization Experiments: Application to Plinian and Dome-forming Eruptions of Mt. Pelée (Martinique, Lesser Antilles),” J. Petrol., Vol.53, No.4, pp. 699-725, 2012.
  5. [5] About Python™, https://www.python.org/about/ [accessed December 1, 2021]
  6. [6] Apache HTTP Server Project, https://httpd.apache.org [accessed December 1, 2021]
  7. [7] M.-N. Guilbaud, S. Blake, T. Thordarson, and S. Self, “Role of Syn-eruptive Cooling and Degassing on Textures of Lavas from the AD 1783–1784 Laki Eruption, South Iceland,” J. Petrol., Vol.48, No.7, pp. 1265-1294, doi: 10.1093/petrology/egm017, 2007.
  8. [8] S. B. Cichy, R. E. Botcharnikov, F. Holtz, and H. Behrens, “Vesiculation and Microlite Crystallization Induced by Decompression: A Case Study of the 1991–1995 Mt Unzen Eruption (Japan),” J. Petrol., Vol.52, Nos.7-8, pp. 1469-1492, doi: 10.1093/petrology/egq072, 2011.
  9. [9] ImageJ, https://imagej.net/software/imagej/ [accessed December 1, 2021]
  10. [10] S. Triebold, A. Kronz, and G. Wörner, “Anorthite-calibrated backscattered electron profiles, trace elements, and growth textures in feldspars from the Teide–Pico Viejo volcanic complex (Tenerife),” J. Volcanol. Geotherm. Res., Vol.154, Nos.1-2, pp. 117-130, doi: 10.1016/j.jvolgeores.2005.09.023, 2006.
  11. [11] G. A. R. Gualda, M. S. Ghiorso, R. V. Lemons, and T. L. Carley, “Rhyolite-MELTS: A modified calibration of MELTS optimized for silica-rich, fluid-bearing magmatic systems,” J. Petrol., Vol.53, No.5, pp. 875-890, 2012.
  12. [12] M. S. Ghiorso and G. A. R. Gualda, “An H2O–CO2 mixed fluid saturation model compatible with rhyolite-MELTS,” Contrib. Mineral. Petrol., Vol.169, No.6, Article No.53, doi: 10.1007/s00410-015-1141-8, 2015.
  13. [13] A. Toramaru, S. Noguchi, S. Oyoshihara, and A. Tsune, “MND (microlite number density) water exsolution rate meter,” J. Volcanol. Geotherm. Res., Vol.175, Nos.1-2, pp. 156-167, 2008.
  14. [14] Y. Suzuki, M. Nagai, F. Maeno, A. Yasuda, N. Hokanishi, T. Shimano, M. Ichihara, T. Kaneko, and S. Nakada, “Precursory activity and evolution of the 2011 eruption of Shinmoe-dake in Kirishima volcano – insights from ash samples,” Earth Planet Space, Vol.65, No.6, Article No.11, doi: 10.5047/eps.2013.02.004, 2013.
  15. [15] D. Shoji, R. Noguchi, S. Otsuki, and H. Hino, “Classification of volcanic ash particles using a convolutional neural network and probability,” Scientific Reports, Vol.8, Article No.8111, doi: 10.1038/s41598-018-26200-2, 2018.
  16. [16] T. Miwa, T. Shimano, and T. Nishimura, “Characterization of the luminance and shape of ash particles at Sakurajima volcano, Japan, using CCD camera images,” Bull. Volcanol., Vol.77, No.1, Article No.5, doi: 10.1007/s00445-014-0886-7, 2015.
  17. [17] T. Shimano, M. Iguchi, S. Nakada, Y. Suzuki, F. Maeno, M. Yoshimoto, A. Zaennudin, N. Hokanishi, and A. Yasuda, “Detection of transition in eruption style by spectrophotometric colorimetry of time-series ash samples during long-lasting eruptions,” Abstract of IAVCEI2017 Scientific assembly, p. 1000, 2017.
  18. [18] D. Nurfiani and C. B. de Maisonneuve, “Furthering the investigation of eruption styles through quantitative shape analyses of volcanic ash particles,” J. Volcanol. Geotherm. Res., Vol.354, pp. 102-114, 2018.
  19. [19] SciPy, https://scipy.org [accessed December 1, 2021]

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Last updated on Aug. 05, 2022