- D. Torkar, B. Zmazek, J. Vaupotič, I. Kobal
- Chemical geology, 2010, Vol. 270, No. 1-2
pages: 1-8
- abstract:
Anomalies have been observed in radon content in soil gas from three boreholes at the Orlica fault in
the Krško basin, Slovenia. To distinguish the anomalies caused by environmental parameters (air and soil
temperature, barometric and soil air pressure, rainfall) from those resulting solely from seismic activity, the
following approaches have been used. First, the seismic activity data were eliminated from the dataset
and then an artificial neural network (ANN) with 5 inputs for environmental parameters and a single output
(radon concentration) was trained with the standard backpropagation learning rule. Then the predictions of
Rn concentrations (Cp) generated with this ANN for the whole dataset were compared to measurements
(Cm) and three types of anomalies (CA — correct anomaly, FA — false anomaly and NA — no anomaly) have
been detected in the signal |Cm/Cp−1| by varying five parameters describing an anomaly within predefined
intervals. An exhaustive search among results was made to find the best ones and thus identifying the best
set of parameters. Finally, an attempt was made to shorten the search procedure by training another ANN
with numbers of anomalies of each type in the input and five anomaly detection parameters in the output.
With these procedures we were able to correctly predict 10 seismic events out of 13 within the 2-year
period.
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