V. Hadjicontis

Transient electric signals prior to rock failure under uniaxial compression

Transient variations of the electric field are detected, prior to the failure of a rock sample which is subjected to uniaxial compression at a variable rate. These precursory electric signals are attributed to the stress induced polarization of the sample and seem to have a form similar to the so called Seismic Electric Signals (SES), which are detected in Greece by the VAN network, prior to earthquakes. The emitted electric signals seem to follow in form the variations of the first time derivative of the externally applied stress. A tentative model for the origin of these signals is also discussed.

Signature of pending earthquake from electromagnetic anomalies

Two electromagnetic (EM) anomalies have been detected in the VLF frequency band before the Athens earthquake (EQ) (Mw=5.9, Sept. 7, 1999) with the following characteristics: (i) The first and second anomaly lasted for 12 and 17 hours respectively with a cessation of 12 hours; (ii) The second anomaly ceased at about 9 hours before the EQ; (iii) The larger anomaly, the second one, contains approximately 80% of the total EM energy received; (iv) No EM disturbance has been recorded in the VHF frequency band unlike with other cases, e.g., the Kozani Grevena and Egion-Eratini earthquakes. The fault modeling of the Athens EQ, based on information obtained by radar interferometry, predicts two faults. The main fault segment is responsible for 80% of the total energy released, while the secondary fault segment for the remaining 20%. Moreover, a recent seismic data analysis supports the hypothesis that a two-event solution for the Athens EQ, is more likely than a single event solution. In addition, the absence of surface rupture explains the absence of EM detection in the VHF frequency band. The present analysis reveals that the properties of the preseismic electromagnetic anomalies might be considered as signatures of a pending earthquake.

Temperature and pressure variation of self-diffusion of Ge in relation to the bulk properties

Abstract A model has been suggested by Varotsos and Alexopoulos that connects the point defect parameters with bulk properties. The validity of this model is examined for the case of Ge. We find that it reproduces the temperature variation of the self-diffusion coefficient D in the temperature range 850–1176 K. The same model is found to give a correct estimation of the activation volume that results from the pressure variation of the self-diffusion coefficient.

A simple model applied to the elastic properties of the lead-thallium and magnesium-lithium alloys

Abstract The adiabatic bulk modulus of single crystals of lead containing thallium decreases with increasing thallium content in spite of the fact that the interatomic distance decreases with thallium additions. This unexpected experimental fact is explained in terms of a simple model suggested recently. The same explanation is found to hold for the case of dilute alloys of magnesium and lithium.

Interconnection of individual vacancy formation and pinning thermodynamic parameters in KCl

Abstract Tallon et al. have recently measured the individual thermodynamic parameters for cation vacancy and anion vacancy formation in KCl. Furthermore Tanibayashi and Tallon have just reported thermodynamic parameters of pinning in KCl. We show that the parameters of the above three processes are interconnected through well known properties of the bulk crystal.

Calculation of diffusion coefficients of nitrogen in vanadium

Abstract In the 573–2098 K temperature region the diffusion coefficient of N in V varies by 10 orders of magnitude. In the present paper we show that this variation can be accounted for by the anharmonic properties of the bulk material, i.e. the temperature variations of the isothermal bulk modulus and the lattice parameter.

Interconnection of the diffusion coefficients of various elements in aluminum

Abstract In the present paper we draw attention to the fact that the diffusion coefficients of eight elements diffusing in aluminum are interconnected through well-known properties of the bulk material.

Reply to “Rebuttal to Replies I and II by Varotsos et al.” by F. Mulargia, W. Marzocchi and P. Gasperini

Mulargia et al. [1996] claim that earthquakes (EQs) can be “predicted” (in retrospective) “much more efficiently than VAN” using a “rule”, they obtained from PDE catalogue. We show that this claim is undoubtedly wrong. Their “rule” issues a great number of false alarms, which exceeds that of the “predicted” EQs (mainly aftershocks) by a factor larger than 10. The errors diagram recommended by Keilis-Borok [1996], reveals that Mulargia et al.s [1996] “rule” corresponds to a non-meaningful algorithm; on the other hand, this diagram reflects that VAN is meaningful.

A review on the statistical significance of VAN predictions

Abstract A Special Issue (Geophys. Res. Lett. 23, 1996) was focused on the question whether the VAN predictions outperform random chance. The majority of the participants of this Debate was selected to be critics against VAN, but Varotsos and co-workers accepted to participate and responded to the critical comments. VAN critics, including Geller (1996), share the same “ requirements ”, which decrease the success rate by 50% and the alarm rate by a factor 4–5. Furthermore, these “ requirements ”, when applied to an ideally perfect earthquake prediction method (which successfully predicts all earthquakes above a certain threshold, and does not issue any false alarm), lead to the following paradoxes: (a) the success rate is below 100%, (b) the alarm rate is 23% only , and (c) the ideal precursors are “postseismic” signals. Recent statistical treatments by Hamada (1996) and by Aceves et al. (1996) coincide to the conclusion that VAN predictions cannot be ascribed to chance.

Reply to “Re‐Rebuttal to the Reply of Varotsos et al.”, by F. Mulargia, W. Marzocchi, and P. Gasperini

In our preceding Reply, we indicated that Mulargia et al. [1996] made (beyond their obvious error that they checked their predictive “rule” only for its “learning period”) a number of mistakes; we also showed that their “rule” does not correspond to a meaningful algorithm. Mulargia et al.s Re-Rebuttal admits that Mulargia et al. [1996] actually made a number of mistakes due to a “bug in the [Mulargia et al s 1996] code”, which not only omitted from their list two (non-“predicted” by their “rule”) “large” earthquakes (EQs), but also scored two missed (“large”) EQs as successfully “predicted”. Furthermore, they now admit that Mulargia et al.s [1996] rule “is certainly not an efficient predictor”, in contrast to their earlier claims. The main issue of our present Reply is to point out that Mulargia et al., in their Re-Rebuttal, now make a very serious error, when constructing the errors diagram: they confuse predictions of main shocks with those of the aftershocks, and hence incorrectly conclude that one can “build very simple, zero-cost predictive tools superior to VAN”. We show that their erroneous procedure leads to the following paradox: when a “rule” (which fails to predict all main shocks) correctly “predicts” a number of aftershocks, one can (incorrectly) claim that he found a predictive “rule” superior to the ideal prediction method; the latter (i.e., the ideal one), in spite of the fact that it predicts all main shocks, is (incorrectly) obtained to correspond to “random predictions”.