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JDR Vol.17 No.5 pp. 587-599
(2022)
doi: 10.20965/jdr.2022.p0587

Paper:

Identifying Anomalies in Seismic Velocity and Scattering Property Changes at Active Volcanoes Based on Seismic Interferometry and the Local Outlier Probability Method

Takashi Hirose, Hideki Ueda, and Eisuke Fujita

National Research Institute for Earth Science and Disaster Resilience (NIED)
3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan

Corresponding author

Received:
January 13, 2022
Accepted:
April 20, 2022
Published:
August 1, 2022
Keywords:
anomaly detection, seismic interferometry, active volcano, local outlier probability
Abstract

The identification of anomalies in seismic wave interferometry data is important in the prediction of imminent volcanic eruptions. Herein, we propose using the local outlier probability (LoOP) method to evaluate the degree of anomaly in seismic wave velocities and scattering properties, estimated via seismic wave interferometry. LoOP is the likelihood that an observation is anomalous and is always in the range of 0–1 (0–100%). We quantitatively evaluated the degree of anomaly in seismic wave velocities and scattering properties before and after the eruption of Mt. Aso, Japan, in October 2016 and Mt. Shinmoedake, which lies within the Mt. Kirishima cluster of volcanoes, Japan, in 2017 and 2018. We found that LoOP exceeded 70% 2 to 3 days before Mt. Aso erupted on October 8, 2016, and it exceeded 70% 1 to 5 days before Mt. Shinmoedake erupted on October 11, 2017 and March 6, 2018. Adjusting the reference and quiet periods for the estimation of changes in seismic velocity/scattering property and LoOP calculations can allow the tracking of repeated, significant LoOP increases during times of high volcanic activity. The quantitative evaluation of temporal anomalies in seismic datasets will improve the precision of predictions of imminent volcanic eruptions.

Cite this article as:
T. Hirose, H. Ueda, and E. Fujita, “Identifying Anomalies in Seismic Velocity and Scattering Property Changes at Active Volcanoes Based on Seismic Interferometry and the Local Outlier Probability Method,” J. Disaster Res., Vol.17, No.5, pp. 587-599, 2022.
Data files:
References
  1. [1] A. Curtis, P. Gerstoft, H. Sato, R. Snieder, and K. Wapenaar, “Seismic interferometry – turning noise into signal,” The Leading Edge, Vol.25, No.9, pp. 1082-1092, 2006.
  2. [2] N. M. Shapiro and M. Campillo, “Emergence of broadband Rayleigh waves from correlations of the ambient seismic noise,” Geophys. Res. Lett., Vol.31, No.7, Article No.L07614, 2004.
  3. [3] R. Snieder, A. Grêt, H. Douma, and J. Scales, “Coda Wave Interferometry for Estimating Nonlinear Behavior in Seismic Velocity,” Science, Vol.295, No.5563, pp. 2253-2255, 2002.
  4. [4] A. Budi-Santoso and P. Lesage, “Velocity variations associated with the large 2010 eruption of Merapi volcano, Java, retrieved from seismic multiplets and ambient noise cross-correlation,” Geophys. J. Int., Vol.206, No.1, pp. 221-240, 2016.
  5. [5] Y. Yukutake, T. Ueno, and K. Miyaoka, “Determination of temporal changes in seismic velocity caused by volcanic activity in and around Hakone volcano, central Japan, using ambient seismic noise records,” Prog. Earth Planet. Sci., Vol.3, Article No.29, 2016.
  6. [6] G. Olivier, F. Brenguier, R. Carey, P. Okubo, and C. Donaldson, “Decrease in seismic velocity observed prior to the 2018 eruption of Kilauea volcano with ambient seismic noise interferometry,” Geophys. Res. Lett., Vol.46, No.7, pp. 3734-3744, 2019.
  7. [7] C. Sens-Schönfelder and U. Wegler, “Passive image interferometry and seasonal variations of seismic velocities at Merapi Volcano, Indonesia,” Geophys. Res. Lett., Vol.33, No.21, Article No.L21302, 2006.
  8. [8] V. C. Tsai, “A model for seasonal changes in GPS positions and seismic wave speeds due to thermoelastic and hydrologic variations,” J. Geophys. Res. Solid Earth, Vol.116, No.B4, Article No.B04404, 2011.
  9. [9] Q.-Y. Wang, F. Brenguier, M. Campillo, A. Lecointre, T. Takeda, and Y. Aoki, “Seasonal crustal seismic velocity changes throughout japan,” J. Geophys. Res. Solid Earth, Vol.122, No.10, pp. 7987-8002, 2017.
  10. [10] U. Meier, N. M. Shapiro, and F. Brenguier, “Detecting seasonal variations in seismic velocities within Los Angeles basin from correlations of ambient seismic noise,” Geophys. J. Int., Vol.181, No.2, pp. 985-996, 2011.
  11. [11] G. Hillers, S. Husen, A. Obermann, T. Planes, E. Larose, and M. Campillo, “Noise-based monitoring and imaging of aseismic transient deformation induced by the 2006 Basel reservoir stimulation,” Geophysics, Vol.80, No.4, pp. KS51-KS68, 2015.
  12. [12] A. J. Hotovec-Ellis, J. Gomberg, J. E. Vidale, and K. C. Creager, “A continuous record of intereruption velocity change at Mount St. Helens from coda wave interferometry,” J. Geophys. Res. Solid Earth, Vol.119, No.3, pp. 2199-2214, 2014.
  13. [13] P. G. Silver, T. M. Daley, F. Niu, and E. L. Majer, “Active Source Monitoring of Cross-Well Seismic Travel Time for Stress-Induced Changes,” Bull. Seismol. Soc. Am., Vol.97, No.1B, pp. 281-293, 2007.
  14. [14] B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, “Estimating the support of a high-dimensional distribution,” Neural Comput., Vol.13, No.7, pp. 1443-1471, 2001.
  15. [15] C. M. Bishop, “Pattern Recognition and Machine Learning (Information Science and Statistics),” Springer, 2006.
  16. [16] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “LOF: identifying density-based local outliers,” ACM SIGMOD Rec., Vol.29, No.2, pp. 93-104, 2000.
  17. [17] H.-P. Kriegel, P. Kroger, E. Schubert, and A. Zimek, “LoOP: local outlier probabilities,” Proc. of the 18th ACM Conf. on Information and Knowledge Management (CIKM’09), pp. 1649-1652, 2009.
  18. [18] Q.-Y. Wang, “Monitoring of the mechanical properties of the crust beneath Japan from continuous data of the Hi-net network,” Applied Geology, Université Grenoble Alpes, 2018.
  19. [19] K. Ono, K. Watanabe, H. Hoshizumi, H. Takada, and S. Ikebe, “Ash eruption of Nakadake volcano, Aso caldera, and its products,” Bull. Volcanol. Soc. Japan, Vol.40, No.3, pp. 133-151, 1995 (in Japanese with English abstract).
  20. [20] H. Shinohara and K. Kazahaya, “Degassing processes related to magma-chamber crystallization,” J. F. H. Thompson (Ed.), “Magmas, Fluids, and Ore Deposits (Mineralogical Association of Canada Short Course Series Vol.23),” pp. 47-70, Mineralogical Association of Canada, 1995.
  21. [21] S. Nagaoka and M. Okuno, “Tephrochronology and eruptive history of Kirishima volcano in southern Japan,” Quatern. Int., Vol.246, Nos.1-2, pp. 260-269, 2011.
  22. [22] G. D. Bensen, M. H. Ritzwoller, M. P. Barmin, A. L. Levshin, F. Lin, M. P. Moschetti, N. M. Shapiro, and Y. Yang, “Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements,” Geophys. J. Int., Vol.169, No.3, pp. 1239-1260, 2007.
  23. [23] O. I. Lobkis and R. L. Weaver, “Coda-wave interferometry in finite solids: recovery of P-to-S conversion rates in an elastodynamic billiard,” Phys. Rev. Lett., Vol.90, No.25, Article No.254302, 2003.
  24. [24] R. L. Weaver, C. Hadziioannou, E. Larose, and M. Campillo, “On the precision of noise correlation interferometry,” Geophys. J. Int., Vol.185, No.3, pp. 1384-1392, 2011.
  25. [25] U. S. Hashmi, A. Darbandi, and A. Imran, “Enabling proactive self-healing by data mining network failure logs,” Proc. of 2017 Int. Conf. on Computing, Networking and Communications (ICNC), pp. 511-517, doi: 10.1109/ICCNC.2017.7876181, 2017.
  26. [26] Geospatial Information Authority of Japan, “Asosan,” Paper presented at 137th meeting of Coordinating Committee for Prediction of Volcanic Eruptions, Japan Meteorological Agency, Tokyo, 2017, https://www.data.jma.go.jp/svd/vois/data/tokyo/STOCK/kaisetsu/CCPVE/shiryo/137/137_01-2.pdf (in Japanese) [accessed December 20, 2021]
  27. [27] Geospatial Information Authority of Japan, “Kirishimayama,” Paper presented at meeting of Coordinating Committee for Prediction of Volcanic Eruptions, Japan Meteorological Agency, Tokyo, 2017, https://www.data.jma.go.jp/svd/vois/data/tokyo/STOCK/kaisetsu/CCPVE/shiryo/kakudai171019/2_jma.pdf (in Japanese) [accessed December 20, 2021]
  28. [28] Geospatial Information Authority of Japan, “Kirishimayama,” Paper presented at 141th meeting of Coordinating Committee for Prediction of Volcanic Eruptions, Japan Meteorological Agency, Tokyo, 2018, https://www.data.jma.go.jp/svd/vois/data/tokyo/STOCK/kaisetsu/CCPVE/shiryo/141/141_01-1.pdf (in Japanese) [accessed December 20, 2021]
  29. [29] M. Breunig, H.-P. Kriegel, R.-T. Ng, and J. Sander, “LOF: Identifying Density-Based Local Outliers,” ACM SIGMOD Rec., Vol.29, No.2, pp. 93-104, doi: 10.1145/342009.335388, 2000.
  30. [30] C. Sens-Schönfelder, E. Pomponi, and A. Peltier, “Dynamics of Piton de la Fournaise volcano observed by passive image interferometry with multiple references,” J. Volcanol. Geotherm. Res., Vol.276, pp. 32-45, 2014.
  31. [31] Y.-C. Huang, T. Ohkura, T. Kagiyama, S. Yoshikawa, and H. Inoue, “Shallow volcanic reservoirs and pathways beneath Aso caldera revealed using ambient seismic noise tomography,” Earth, Planets and Space, Vol.70, Article No.169, 2018.
  32. [32] Y. Nagaoka, “Study on seismic velocity structure beneath active volcanoes by seismic interferometry,” Ph.D. thesis, The University of Tokyo, 2020.

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