VAN method

The VAN method – named after P. Varotsos, K. Alexopoulos and K. Nomicos, authors of the 1981 papers describing it[1][2] – measures low frequency electric signals, termed "seismic electric signals" (SES), by which Varotsos and several colleagues claimed to have successfully predicted earthquakes in Greece.[3][4] Both the method itself and the manner by which successful predictions were claimed have been severely criticized[5][6][7] and defended.[8][9] The VAN method was advanced in 2001 by improving the accuracy of the estimation of the time window of the forthcoming earthquake through a new analysis, termed "natural time", and this new method has been independently reproduced.[10] Research related to the VAN method is currently carried out at the Solid Earth Physics Institute, University of Athens, Greece.

Description of the VAN method

Prediction of earthquakes with this method is based on the detection, recording and evaluation of seismic electric signals or SES. These electrical signals have a fundamental frequency component of 1 Hz or less and an amplitude the logarithm of which scales with the magnitude of the earthquake.[11] According to VAN proponents, SES are emitted by rocks under stresses caused by plate-tectonic forces. There are three types of reported electric signal:[12]

Several hypotheses have been proposed to explain SES:

While the electrokinetic effect may be consistent with signal detection tens or hundreds of kilometers away, the other mechanisms require a second mechanism to account for propagation:

Seismic electric signals are detected at stations which consist of pairs of electrodes (oriented NS and EW) inserted into the ground, with amplifiers and filters. The signals are then transmitted to the VAN scientists in Athens where they are recorded and evaluated. Currently the VAN team operates 9 stations, while in the past (1989) they could afford up to 17.[23]

The VAN team claimed that they were able to predict earthquakes of magnitude larger than 5, with an uncertainty of 0.7 units of magnitude, within a radius of 100 km, and in time window ranging from several hours to a few weeks. Several papers confirmed this success rate, leading to statistically significant conclusion.[24] For example, there were eight M ≥ 5.5 earthquakes in Greece from January 1, 1984 through September 10, 1995, and the VAN network forecast six of these.[25]

The VAN method has also been used in Japan,[10] but in early attempts success comparable to that achieved in Greece was "difficult" to attain.[26] A preliminary investigation of seismic electric signals in France led to encouraging results.[27]

Earthquake prediction using "natural time" analysis

Since 2001 the VAN team has tried to improve the accuracy of the estimation of the time of the forthcoming earthquake. They introduced the concept of natural time, a time series analysis technique which puts weight on a process based on the ordering of events.[28] They define a variance term κ in the time series analysis. Their current method detects SES as valid when κ = 0.070. That is the first part of their protocol. Once the SES are deemed valid, a second analysis is started. All the subsequent seismic (not electric) events are now noted, and the region is divided up as a Venn diagram with at least two seismic events per overlapping rectangle. When the distribution of κ for the rectangular regions has its maximum at κ = 0.070, the critical seismic event is imminent, i.e. it will occur in a few days to one week or so, and a prediction is issued.[29]

Here is a brief introduction to the analysis. Two terms, χ and Q, characterize each event. χ is defined as k/N, where k is an integer (the k-th event) and N is the total number of events in the time sequence of data. Q is the energy released for each event. A related term, p, is the ratio Q/Q(total), and describes the fractional energy released. They introduce a critical term κ, defined as κ = [ Σ p(k) (k/N)^2 ] − [ Σ (k/N) p(k) ]^2, where κ is the variance in natural time, k is the event index, N is the number of events, Σ signifies summation, for k = 1 to N, and p(k) is the fractional quantity of energy for the k-th event. This variance puts extra weight on the energy term, p(k).

The VAN team published to have successfully predicted twenty five of the 28 major earthquakes from 2001 through 2010 in the region of latitude N 36° to N 41° and longitude E 19° to E 27° with this new analysis.[30] Predictions are part of papers housed in arXiv, and new predictions are currently being made and uploaded there.[31] A description of the updated VAN method was collected in a book published by Springer in 2011, titled "Natural Time Analysis: The New View of Time."[32]

Criticisms of VAN

Currently, the major criticism of the VAN method is that results have not yet been replicated by scientists outside specific research groups in Greece and Japan. Testing of the method needs to be done by scientists unrelated to these two groups. Independent verification is a standard protocol in science.

Historically, the usefulness of the VAN method for prediction of earthquakes had been a matter of debate. Both positive and negative criticism on an older conception of the VAN method is summarized in the 1996 book "A Critical Review of VAN", edited by Sir James Lighthill.[33] A critical review of the statistical methodology was published by Y. Y. Kagan of UCLA in 1997.[34] Note that these criticisms predate the time series analysis methods introduced by the VAN group in 2001. The main points of the criticism were:

See also

Notes

  1. Varotsos, Alexopoulos & Nomicos 1981a, 1981b
  2. 1 2 Varotsos & Alexopoulos 1984
  3. Varotsos & Kuhlanek 1993 (preface to a special edition about VAN)
  4. Varotsos, Alexopoulos & Lazaridou 1993
  5. Mulargia & Gasperini 1992
  6. Geller 1997, §4.5
  7. ICEF 2011, p. 335
  8. Lighthill 1996 (proceedings of a conference that reviewed VAN)
  9. twenty articles in a special issue of Geophysical Research Letters (table of contents)
  10. 1 2 Uyeda, Kamogawa & Tanaka 2009
  11. Varotsos, Alexopoulos & Nomicos 1981a; Varotsos et al. 1981; Varotsos, Alexopoulos & Nomicos 1982.
  12. Varotsos, Alexopoulos & Lazaridou 1993.
  13. Matsumoto, Ikeya & Yamanaka 1998.
  14. Varotsos et al. 1986, p. 120.
  15. Hadjicontis et al. 2007
  16. Shen et al. 2011.
  17. Gershenzon, Gokhberg & Yunga 1993.
  18. Honkura et al. 2009.
  19. Pulinets 2007.
  20. ICEF 2011, p. 334.
  21. Varotsos et al. 1998.
  22. Freund 1998.
  23. Varotsos & Lazaridou 1991
  24. Hamada 1993
  25. Uyeda 1996
  26. Utada 1993, p. 153
  27. Maron et al. 1993
  28. Varotsos, Sarlis & Skordas 2002; Varotsos 2006.
  29. Varotsos, Sarlis & Skordas 2011, Chapter 7.
  30. Varotsos, Sarlis & Skordas 2011, p. 326
  31. http://arxiv.org/, search for "Varotsos"
  32. Varotsos, Sarlis & Skordas 2011.
  33. Lighthill 1996.
  34. Kagan 1997, p. 512.
  35. Varotsos, Alexopoulos & Nomicos 1981b.
  36. Wyss 1996a.
  37. Varotsos & Lazaridou 1991.
  38. Wyss & Allmann 1996.
  39. Bernard 1992; Bernard & LeMouel 1996.
  40. Pham et al. 1998.
  41. Mulargia & Gasperini 1992; Mulargia & Gasperini 1996; Wyss 1996b.
  42. Hamada 1993.
  43. Mulargia & Geller 2003, p. 318.

References

External links

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