Title: Extracting preseismic electric signals from noisy Earths electric field data recordings. The ’noise injection’ method
ArXiv ID: 0807.4298
Date: 2008-07-29
Authors: Researchers from original ArXiv paper
📝 Abstract
An entirely different approach is used for the extraction of preseismic electric signals from highly contaminated by noise data series. The basic element of this method is the "Dirac Delta Function". Noise is injected, instead of applying any known method of filtering, in the data series at different amplitude levels (p value) and the generated "family" of filtered data series is compared to different convergence criteria. In the case of preseismic electric signals identification the most appropriate (p) value is selected by testing the convergence of generated intersections of more than three "families" of filtered data which were generated from more than three monitoring sites. The "noise injection" method was tested against real data recorded long before two large earthquakes in Greece and one in Turkey.
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Deep Dive into Extracting preseismic electric signals from noisy Earths electric field data recordings. The "noise injection" method.
An entirely different approach is used for the extraction of preseismic electric signals from highly contaminated by noise data series. The basic element of this method is the “Dirac Delta Function”. Noise is injected, instead of applying any known method of filtering, in the data series at different amplitude levels (p value) and the generated “family” of filtered data series is compared to different convergence criteria. In the case of preseismic electric signals identification the most appropriate (p) value is selected by testing the convergence of generated intersections of more than three “families” of filtered data which were generated from more than three monitoring sites. The “noise injection” method was tested against real data recorded long before two large earthquakes in Greece and one in Turkey.
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Extracting preseismic electric signals from noisy Earth’s electric field data recordings. The “noise
injection” method.
An entirely different approach is used for the extraction of preseismic electric signals from highly contaminated by noise data
series. The basic element of this method is the “Dirac Delta Function”. Instead of applying any known method of filtering, noise is
injected, in the data series at different amplitude levels (p value) and the generated “family” of filtered data series is compared to
different convergence criteria. In the case of preseismic electric signals identification the most appropriate (p) value is selected
by testing the convergence of generated intersections of more than three “families” of filtered data which were generated from
more than three monitoring sites. The “noise injection” method was tested against real data recorded long before two large
earthquakes in Greece and one in Turkey. The obtained results justify the validity of the postulated method.
Introduction.
Before any use is made, of the data series which result from the acquisition and registration of the Earth’s electric field, by any
monitoring site, two important operations are applied on them. The first one is the editing of the data and the second is the rejection of
the superimposed noise. The noise rejection (filtering) techniques and methodologies, which deal with time series of any data type, generally require,
as input data, files which are free from data gaps. If, accidentally, a “gap” is met during the processing of a data file, then in most
cases, the running procedure, either “crashes” or generates erroneous results. In both cases, it takes some time to be wasted, in
order to rectify this nasty situation. Therefore, as a preliminary step, before any methodology is applied on a specific data set, it
is a good policy to check against data gaps and to apply any suitable methodology to recover the data continuity.
In the particular case of the registration of the Earth’s electric field at the various monitoring sites which are in operation
(www.earthquakeprediction.gr
) to date (ATH, PYR, HIO), the following main causes have created data gaps:
a. Damage on the receiving dipole electrode lines. This is mostly a breakdown (due to various causes) of the wires
which connect the electrodes with the pre-processing unit, located at the housing of the monitoring site.
b. “Crashing” of the used computer system, due to power line voltage rapid and large amplitude changes, which the
used ordinary UPS, cannot accommodate.
In both cases, the result is the same. Data gaps are created, since the operator in charge of the monitoring site becomes
aware of the faulty situation, only after a few hours from the data gap occurrence. As long as such a data gap has been detected, the gap is replaced by linearly, interpolated data, taking into account the start
and the end data values, which preceded and followed the data gap. This procedure is presented in the following figures (1 – 3)
which present data gaps, met at recordings generated by PYR monitoring site.
Fig. 1. Presentation of “missing data” and their linearly,
interpolated values, used to bridge the corresponding data gap.
Date of gap recording is 14
th November 2006, at Pyrgos (PYR)
monitoring site.
The observed data gap of figure (1) lasted for six (6) hours during the actual recording of 14
th November, 2006 at Pyrgos
(PYR) monitoring site. A much longer data gap is presented in the following figure (2).
Fig. 2. “Missing data” and their linearly interpolated values are
presented, which are used to bridge the corresponding data gap.
Date of gap recording is 25
th - 26
th November 2006, at Pyrgos
(PYR) monitoring site.
1
The observed data gap of figure (2) lasted for more than a day (25
th – 26
th November 2006). Finally a third example is
presented in figure (3).
Fig. 3. “Missing data” and their linearly, interpolated values
are presented, which were used to bridge the corresponding
data gap. Date of gap recording, 16
th January 2007, at Pyrgos
(PYR) monitoring site.
The procedure, which is followed, in order to fill the data gap, does affect the “short wave length”, electrical signals,
recorded, with period of some hours at most. In practice, this data gap time period is replaced by a “noise free”, new data set.
However, it does not chang