Short-term
Earthquake Prediction
Based on Seismic Precursory Electric
Signals Recorded on Ground Surface.

“What
today seems impossible, is tomorrow’s reality”

Dinos

"STRANGE ATTRACTOR LIKE" ELECTRIC EARTHQUAKE
PRECURSORS

SYNTHETIC DATA

The application of the "strange attractor" EQ
electrical precursor has shown that it takes the form of a hyperbola,
ellipse or closely to circle, in cases when the signal to noise ratio is
quite large.

In this part of the presentation "clean" (completely
free of any noise) signals will be used in order to check the physical
validity of the specific EQ electric precursor. The used physical
set-up basically consists of two distant monitoring sites.

Generation of hyperbolas.

This is demonstrated in the following Fig. (8).

Fig.8. Monitoring sites M1,2 register signals
generated at different locations Sig-1,2

The generated signals are uncorrelated. The
application of the "strange attractor" method results are shown in
the following Fig. (9).

Fig. 9. Hyperbolas generated by the application
of the "strange attractor" method on uncorrelated noise electrical
signals.

Generation of circles.

In the physical model of Fig. (8) the two noise
generation sources Sig-1,2 are replaced by a single signal source (EQ
focal area). The generated signal is registered by the two
monitoring stations M1,2. The physical set-up of this version is shown
in the following Fig. (10).

Fig. (10). Monitoring sites M1,2 register signals
generated at the EQ focal area (EQ-Sig).

The generated signals are correlated. The application
of the "strange attractor" method results are shown in the
following Fig. (11).

Fig. (11). Circle created by the application of the
"strange attractor" EQ electric precursor, on the same monochromatic EM
signals induced by the EQ focal area at both monitoring sites M1,2.

Generation of an ellipse.

The next step in this analysis
is to transform a calculated circle into an actually observed ellipse.
At this point you have to remember that both signal and noise are of the
same frequency. Therefore, the monochromatic EQ EM signal is interfered
by a monochromatic noise of the same frequency as the EQ signal.

The application of the "strange attractor" method
simultaneously on both signals, results into the following Fig. (12).

Fig. (12). Ellipse created by the
application of "strange attractor" methodology on both
monochromatic EQ electric precursory signal and noise.

At this point one could ask: How do you know that
a circle is generated always from your model and no any other shape?

There are three (3) ways to prove it.

The first one is a tedious and lengthy
mathematical transformations of the physical model equations
aiming to show that each point (x,y) generated from the model is placed
at a fixed distance (radius) from a fixed point which is the center of
the circle. I advise you not to do it.

The second is to test it statistically. In this case
there is the problem of the "statistical significance" of the No of
examples used which in turn leads to an endless debate on the topic.

The third one, being the most elegant,
beautiful and simple too, is based on the physical processes which take
place in the model, and to give you a hint, some high-school geometry
(angles in circles properties) is required.

During this work we had an exceptional idea. A virtual
(hypothetical) network, composed of three monitoring sites and a
hypothetical EQ, to be considered and the
resulting circles to be placed in the same graph for comparison reasons.
The following virtual specific cases have been considered:

A. Monitoring sites M1, M2, M3 and
hypothetical EQ-1

B. Monitoring sites M1, M2, M3 and
hypothetical EQ-2

C. Monitoring sites M4, M5, M6 and
hypothetical EQ-2

In detail:

A. The monitoring sites M1, M2, M3 and the
hypothetical EQ-1
coordinates are:

COR

X

Y

M1 =

30

20

M2 =

60

50

M3 =

80

90

EQ-1

45

75

Circles are formed in pairs of monitoring sites
(M1-M2, M1-M-3, M2-M3). The corresponding results are shown in Fig. (13).

Fig. 13. Circles generated by the application the
"strange attractor" electric precursor on signals generated by EQ-1.
The
double circles intersections indicate the monitoring sites while the
triple one indicates
the hypothetical EQ-1 location (compare its theoretical
and resulted coordinates).

B. The monitoring sites M1, M2, M3 and the
hypothetical EQ-2
coordinates are:

COR

X

Y

M1 =

30

20

M2 =

60

50

M3 =

80

90

EQ-2

120

10

Circles are formed in pairs of monitoring sites
(M1-M2, M1-M-3, M2-M3). The corresponding results are shown in Fig. (14).

Fig. 14. Circles generated by the application of the
"strange attractor" electric precursor on the signals generated by EQ-2.
The
double circles intersections indicate the monitoring sites while the
triple one indicates the hypothetical EQ-2 location (compare its theoretical
and resulted coordinates).

C. The monitoring sites M4, M5, M6 and the
hypothetical EQ-2
coordinates are:

COR

X

Y

M4 =

10

90

M5 =

15

20

M6 =

110

100

EQ-2

120

10

Circles are formed in pairs of monitoring sites
(M4-M5, M4-M-6, M5-M6). The corresponding results are shown in Fig. (15).

Fig. 15. Circles generated by the application of the
"strange attractor" electric precursor on signals generated by EQ-2. The
double circles intersections indicate the monitoring sites while the
triple one indicates the hypothetical EQ-2 location (compare its theoretical
and resulted coordinates).

In all three cases A, B and C, the epicenter of the
hypothetical EQ is determined with high accuracy. The next
step is to check the validity of the mathematical analysis of the
virtual model by using Inversion
(Example-1) methodologies.