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1

Earthquakes recorded in Poland along the Pieniny Klippen Belt, Western Carpathians

Guterch B., Lewandowska-Marciniak H., Niewiadomski J.

Acta Geophysica Polonica

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2005

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Vol. 53, nr 1

27-45

EN

Instrumental and macroseismic data of a sequence of local earthquakes recorded in 1995 in the Western Carpathians (the region of Podhale, northernmost part of the Pieniny Klippen Belt) have been analysed. The earthquakes occurring in the area of the Orawa-Nowy Targ depression are the evidence that tectonic faults in this area are still seismically active. An earthquake of M = 4.1 was re-corded there in November 2004.

2

Extraction of information from seismograms by neural networks

Niewiadomski J.

Acta Geophysica Polonica

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2004

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Vol. 52, nr 2

143-153

EN

In this study we concentrate on neural networks in the form of algorithms, and on their property to learn by examples. The possibility of learning by examples in-cludes also an enormous ability of information extraction. As the input data we have used the regional seismograms which record the ground particle velocities. Application of the networks for evaluation of the two seismic parameters has been demonstrated: first to calculate the magnitude (or seismic moment) of the seismic event directly from the velocity seismograms, without prior converting them to the displacement seismograms, and second, to locate seismic source by using only a single seismogram. Both applications are very convenient due to their speed of cal-culation and the lack of spectral methods - in the first case, and the good precision despite the unusual approach in the second case. To prepare the input data for the neural networks a preprocessing has been applied, which can be described as filter-ing of a seismogram by the filters bank.

3

Temporal distribution, source parameters, and depth discrimination of earthquakes from the Abu Dabbab region, Egypt

Hassoup A., Niewiadomski J., Gibowicz S.

Acta Geophysica Polonica

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2003

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Vol. 51, nr 2

135-146

EN

Records of earthquakes from the Abu Dabbab region in Egypt, situated about 25 km west of the Red Sea coast, were collected from the Aswan Seismograph Network (ASN). The temporal distribution of these events shows several sequences of the foreshock-main shock-aftershock type. Four such sequence occurred in 1984 and 1985. the slope of the frequency-amplitude relation (b-value) for the four sequences ranges form 1.8 to 2.4, reflecting co-seismic deformation with time in the vicinity of the source area. Source parameters were estimated for ten events from the same area, which occurred between 1998 and 2001 and had magnitude ranging from 3.0 to 4.2.the spectral plateau, corner frequency, seismic moment, source dimension, and stress drop were calculated. A good correlation is found between the logarithm of seismic moment and the local duration magnitude determined by the ASN. The stress drop is not uniform and ranges between 0.1 and 6.8 MPa. In addition, the relative decay of the amplitude of S waves from the Abu Dabbab earthquakes with the epicentral distance is examined from the records of various stations of the ASN. It was found the rater of decay can be divided into two distinct types, related to different paths between the hypocenters and the stations, which in turn indicate different depths of the studied events.

4

Detection of seismic signals on noisy seismograms

Niewiadomski J.

Acta Geophysica Polonica

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2002

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Vol. 50, nr 2

159-166

EN

In seismologic observational practice seismic signals are very often obscured by a strong seismic noise. Sometimes one needs to have only a very limited knowledge on them, and sole information on the presence of a seismic signal is quite important. Some of such situations include, e.g., seismic source location, where only an onset time is needed, or the situation of monitoring the seismic activity. Below we will concentrate on the really difficult cases, when a visual inspection of a trained seismologist fails to find any hint of the presence of seismic signal on the noisy seismograms. The difficult task of signal detection can be done by a neural network. As the input data we will use the autoregressive parameters which model the seismogram.