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Pure and Applied Geophysics

, Volume 171, Issue 3–5, pp 549–559 | Cite as

Visual Event Screening of Continuous Seismic Data by Supersonograms

  • Benjamin SickEmail author
  • Marco Walter
  • Manfred Joswig
Article

Abstract

We present a new visualization method for human inspection of seismic data called supersonograms, which maximizes the amount of time and stations visible on screen while retaining the possibility to detect short and low-signal to noise ratio (SNR) signals. This visualization approach is integrated into a seismological software suite used in the seismic aftershock monitoring system (SAMS) of Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) on-site inspections (OSI) to detect suspicious events eventually representing aftershocks from an underground nuclear explosion (UNE). During an OSI, huge amounts of continuous waveform data accumulate from up to 50 six-channel mini-arrays covering an inspection area of 1,000 square kilometers. Sought-after events can have magnitude as low as \(\hbox{M}_{\rm L}\,{-2.0}\) and duration of just a few seconds, which makes it particularly hard to discover them in large, noisy datasets. Therefore, the data visualization is based on nonlinearly scaled, noise-adapted spectrograms, i.e., sonograms, which help to distinguish weak signal energy from stationary background noise. Four single-trace sonograms per mini-array can be combined into supersonograms, since the array aperture is small and sonograms suppress differences of local site noise, allowing an analyst to check array-wide signal coherence quickly. In this paper, we present the supersonograms and the software on the basis of a dataset from a creeping, inhabited landslide in Austria where the same station layout is used as in an OSI. Detected signals are fracture processes in the sedimentary landslide, i.e., slidequakes, with \(\hbox{M}_{\rm L}\,{-0.5} \,\hbox{\,to}\,{-2.5}\) between July 2009 and July 2011. These signals are comparable in magnitude and duration to expected weak UNE aftershocks.

Keywords

OSI SAMS passive method signal processing seismic 

Notes

Acknowledgements

Early software development of the NanoseismicSuite was done mainly by Andreas Poszlovszki, who created the modular object-oriented application basis which allowed the further extensive development by the main author into an application which is used now in many areas. Matthias Guggenmos and Andreas Eisermann also contributed code. The authors thank Patrick Blascheck and Eberhard Claar for their support in developing and installing the permanent seismic network at Heumoes slope, which is funded by DFG (German Research Foundation). Funding of the software development is provided by the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO Prep Com), where Peter Labak provides the integration of the software into the OSI SAMS environment.

References

  1. Chiu, J. M., Steiner, G., Smalley Jr., R., and Johnston, A. C. (1991), PANDA: A simple, portable seismic array for local- to regional-scale seismic experiments, Bull. Seism. Soc. Am. 81, 1000–1014.Google Scholar
  2. CTBTO, Comprehensive Nuclear-Test-Ban Treaty (Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization, 1996).Google Scholar
  3. Douglas, A. (2007), Forensic seismology revisited, Surv Geophys. 28, 1–31.Google Scholar
  4. Ford, S. R., and Walter, W. R. (2010), Aftershock Characteristics as a Means of Discriminating Explosions from Earthquakes, Bull. Seism. Soc. Am. 100, 364–376.Google Scholar
  5. Gomberg, J., Schulz, W., Bodin, P., and Kean, J. (2011), Seismic and geodetic signatures of fault slip at the Slumgullion Landslide Natural Laboratory, J. Geophys. Res. 116, 20 pp.Google Scholar
  6. Häge, M., and Joswig, M. (2009), Spatiotemporal characterization of interswarm period seismicity in the focal area Nový Kostel (West Bohemia/Vogtland) by a short-term microseismic study, Geophys. J. Int. 179, 1071–1079.Google Scholar
  7. Häge, M., Blascheck, P., and Joswig, M. (2012), EGS hydraulic stimulation monitoring by surface arrays - location accuracy and completeness magnitude: the Basel Deep Heat Mining Project case study, J. Seismol.Google Scholar
  8. Harjes, H.-P. (1990), Design and siting of a new regional array in Central Europe, Bull. Seism. Soc. Am. 80, 1801–1817.Google Scholar
  9. Haubrich, R. A. (1968), Array design, Bull. Seism. Soc. Am. 58, 977–991.Google Scholar
  10. Jarpe, S., Goldstein, P., and Zucca, J. J., Comparison of the non-proliferation event aftershocks with other Nevada Test Site events, UCRL-JC-117754. In Non-proliferation Experiment Symposium, Rockville, Maryland, 19-21 April 1994.Google Scholar
  11. Joswig, M. (1990), Pattern recognition for earthquake detection, Bull. Seism. Soc. Am. 80, 170–186.Google Scholar
  12. Joswig, M. (1993), Single-trace detection and array-wide coincidence association of local earthquakes and explosions, Comput. Geosci. 19, 207–221.Google Scholar
  13. Joswig, M. (1993), Automated seismogram analysis for the tripartite BUG array: an introduction, Comput. Geosci. 19, 203–206.Google Scholar
  14. Joswig, M. (1995), Automated classification of local earthquake data in the BUG small array, Geophys. J. Int. 120, 262–286.Google Scholar
  15. Joswig, M. (1996), Pattern recognition techniques in seismic signal processing, In Proceedings of the 2nd Workshop on Application of Artificial Intelligence Techniques in Seismology and Engineering Seismology 12, 1996, 37–56.Google Scholar
  16. Joswig, M. (2008), Nanoseismic monitoring fills the gap between microseismic networks and passive seismic, First Break 26, 117–124.Google Scholar
  17. Kennett, B. L. N., Brown, D. J., Sambridge, M., and Tarlowski C. (2003), Signal Parameter Estimation for Sparse Arrays, Bull. Seism. Soc. Am. 93, 1765–1772.Google Scholar
  18. Kohonen, T. (2001), Self-organizing maps, Springer Ser. Inf. Sci. 30, 501 pp.Google Scholar
  19. Kvaerna, T., and Ringdal, F. (1992), Integrated array and three-component processing using a seismic microarray, Bull. Seism. Soc. Am. 82, 870–882.Google Scholar
  20. Lindenmaier, F., Zehe, E., Dittfurth, A., and Ihringer, J. (2005), Process identification at a slow-moving landslide in the Vorarlberg Alps, Hydrological Processes 19, 1635–1651.Google Scholar
  21. Mykkeltveit, S., Åstebøl, K., Doornbos, D. J., and Husebye, E. S. (1983), Seismic array configuration optimization, Bull. Seism. Soc. Am. 73, 173–186.Google Scholar
  22. Ringdal, F., and Husebye, E. S. (1982), Application of arrays in the detection, location, and identification of seismic events, Bull. Seism. Soc. Am. 72, 201–224.Google Scholar
  23. Ringdal, F. (1990), Introduction to the special issue on regional seismic arrays and nuclear test ban verification, Bull. Seism. Soc. Am. 80, 1775–1776.Google Scholar
  24. Sokolowski, T. J., and Miller, G. R. (1967), Automated epicenter locations from a quadripartite array, Bull. Seism. Soc. Am. 57, 269–275.Google Scholar
  25. Spillmann, T., Maurer, H., Green, A. G., Heincke, B., Willenberg, H., and Husen, S. (2007), Microseismic investigation of an unstable mountain slope in the Swiss Alps, J. Geophys. Res. 112, 25 pp.Google Scholar
  26. Suyehiro, S. (1967), A search for small, deep earthquakes using quadripartite stations in the Andes, Bull. Seism. Soc. Am. 57, 447–461.Google Scholar
  27. Walter, M., and Joswig, M. (2008), Seismic monitoring of fracture processes generated by a creeping landslide in the Vorarlberg alps, First Break 26, 131–136.Google Scholar
  28. Walter, M., and Joswig, M., Seismic characterization of slope dynamics caused by softrock-landslides: The Super-Sauze case study. In Malet, J.-P., Remaître, A., Boogard, T. (Eds), Proceedings of the International Conference on Landslide Processes: from geomorphologic mapping to dynamic modelling, Strasbourg (CERG Editions, 2009), 215–220.Google Scholar
  29. Walter, M., Niethammer, U., Rothmund, S., and Joswig, M. (2009), Joint analysis of the Super-Sauze (French Alps) mudslide by nanoseismic monitoring and UAV-based remote sensing, First Break 27, 75–82.Google Scholar
  30. Walter, M., Walser, M., and Joswig, M. (2011), Mapping rainfall-triggered fracture processes, and seismic determination of landslide volume at the creeping Heumoes slope, Vadose Zone J. 10, 487–495.Google Scholar
  31. Walter, M., Arnhardt, C., and Joswig, M. (2012), Seismic monitoring of rockfalls, slide quakes, and fissure development at the Super-Sauze mudslide, French Alps, Eng. Geol. 128, 12–22.Google Scholar
  32. Ward, P. L., and Gregersen, S. (1973), Comparison of earthquake locations determined with data from a network of stations and small tripartite arrays on Kilauea Volcano, Hawaii, Bull. Seism. Soc. Am. 63, 679–711.Google Scholar
  33. Wust-Bloch, G. H., and Joswig, M. (2006), Pre-collapse identification of sinkholes in unconsolidated media at Dead Sea area by ’nanoseismic monitoring’ (graphical jackknife location of weak sources by few, low-SNR records), Geophys. J. Int. 167, 1220–1232.Google Scholar
  34. Zucca, J. J. (1998), Forensic seismology supports the Comprehensive Test Ban Treaty, Science & Technology Rev., LLNL, CA., Sept. 1998, 4–11.Google Scholar

Copyright information

© Springer Basel 2012

Authors and Affiliations

  1. 1.University of Stuttgart, Institute for GeophysicsStuttgartGermany

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