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Characterization of Site Conditions for Selected Seismic Stations in Eastern Part of Romania

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Grecu B. , Zaharia B. , Diaconescu M. , Bala A. , Nastase E. , Constantinescu E. , Tataru D.

Acta Geophysica

2018

tom Vol. 66, no. 2

153--165

Strong motion data are essential for seismic hazard assessment. To correctly understand and use this kind of data is necessary to have a good knowledge of local site conditions. Romania has one of the largest strong motion networks in Europe with 134 real-time stations. In this work, we aim to do a comprehensive site characterization for eight of these stations located in the eastern part of Romania. We make use of a various seismological dataset and we perform ambient noise and earthquake-based investigations to estimate the background noise level, the resonance frequencies and amplification of each site. We also derive the Vs30 parameter from the surface shear-wave velocity profiles obtained through the inversion of the Rayleigh waves recorded in active seismic measurements. Our analyses indicate similar results for seven stations: high noise levels for frequencies larger than 1 Hz, well defined fundamental resonance at low frequencies (0.15-0.29 Hz), moderate amplification levels (up to 4 units) for frequencies between 0.15 and 5-7 Hz and same soil class (type C) according to the estimated Vs30 and Eurocode 8. In contrast, the eighth station for which the soil class is evaluated of type B exhibits a very good noise level for a wide range of frequencies (0.0120 Hz), a broader fundamental resonance at high frequencies (~ 8 Hz) and a flat amplification curve between 0.1 and 3-4 Hz.

Travel time picking of ambient noise cross-correlation using a deep neural network combining convolutional neural networks and Transformer

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Jin C. , Ye F. , Zhang H. , Bao X.

tom Vol. 72, no. 1

97--114

The travel time of ambient noise cross-correlation is widely used in geophysics, but traditional methods for picking the travel time of correlation are either difficult to be applied to data with low signal-to-noise ratio (SNR), or make some assumptions which fail to be achieved in many realistic situations, or require a lot of complex calculations. Here, we present a neural network based on convolutional neural networks (CNN) and Transformer for the travel time picking of ambient noise crosscorrelation. CNNs expand the dimension of the vector of each time step for the input of Transformer. Transformer focuses the model’s attention on the key parts of the sequence. Model derives the travel time according to the attention. 102,000 cross-correlations are used to train the network. Compared with traditional methods, the approach is easy to use and has a better performance, especially for the low SNR data. Then, we test our model on another ambient noise cross-correlation dataset, which contains cross-correlations from different regions and at different scales. The model has good performance on the test dataset. It can be seen from the experiment that the travel time of the cross-correlation function of ambient noise with an average SNR as low as 9.3 can be picked. 97.2% of the picked travel times are accurate, and the positive and negative travel time of most cross-correlations are identical (90.2%). Our method can be applied to seismic instrument performance verification, seismic velocity imaging, source location and other applications for its good ability to pick travel time accurately.

An inversion of Rayleigh waves dispersion curves as a tool to recognize the bedrock depth in Chorzów Stary, Poland

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Mendecki M. J. , Zuberek W. M. , Hrehorowicz P. , Jarek S.

Contemporary Trends in Geoscience

2012

tom Vol. 1, iss. 1

39--44

Identification of a bedrock beneath soft cover is one of the most important task in engineering geology. The location of boundary-overburden information may be used by investors, builders and municipal authorities to design an infrastructure or land-use plans. In such issues the application of appropriate geophysical methods is useful. However, in urban zones and areas characterized by subsurface soft layer the usage of certain methods (eg.: seismic refraction) is not advisable. The passive method of Refraction Microtremor (ReMi) can fulfill its tasks in the relatively difficult urban environment. The vertical S-wave velocity profiles were carried out as a result of inversion of Rayleigh wave dispersion curves obtained from ReMi method. The change of S-wave velocities allowed to distinguish shallow geological layers in the area of Chorzów Stary. Preliminary measurements allowed to identify the Carboniferous bedrock at a depth of 14-18 m what has been confirmed by resistivity imaging. Furthermore, unconsolidated deposits are also recognized and the seismic results show a good correlation with the available geological information and resistivity imaging data.

Rozpoznanie zalegania sztywnego podłoża pod warstwą luźnych osadów jest jednym z ważniejszych zagadnień w geologii inżynierskiej. Informacja o położeniu granicy podłożenadkład wykorzystana może zostać przez inwestorów, inżynierów budowlanych lub władze gmin do projektowania właściwych konstrukcji lub planowania zagospodarowania przestrzennego. W takich sytuacjach przydatne stają się rozwiązania jakie proponują właściwe metody geofizyczne. Jednakże w strefach zurbanizowanych oraz charakteryzujących się luźnymi warstwami przypowierzchniowymi zastosowanie niektórych metod (np.: sejsmiki refrakcyjnej) nie jest wskazane. Metoda refrakcji mikrodrgań ReMi (pasywna) spełnia swoje zadania w tych względnie trudnych warunkach. W wyniku inwersji krzywych dyspersyjnych fal Rayleigha otrzymano pionowe zmiany prędkości fali S, co pozwoliło na rozróżnienie warstw geologicznych. W rejonie Chorzowa Starego przeprowadzono wstępne pomiary, które pozwoliły na rozpoznanie zalegania podłoża karbońskiego na głębokości ok. 15 m. Wydzielone warstwy w strefie osadów luźnych wykazały także dobrą korelację z dostępną informacją geologiczną oraz pomiarami inwersyjnego obrazowania oporności.