Geogiga Seismic Pro ✊🏿
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Geogiga Seismic Pro
the last picking fundamental mode was also inverted by means of a genetic algorithm. the results of this inversion were compared with previously obtained results based on the analysis of the rayleigh wave dispersion curves. the second picking fundamental mode was interpreted in the same way. it was also inverted to produce a 2d shear wave velocity section. figure 10 shows an example of an inversion result of the second picking fundamental mode for a velocity interval of 0.045-0.056 km/s. the rayleigh wave dispersion curves and the first picking fundamental mode dispersion curve are shown in the upper figure. the shear wave velocity model along the same line, obtained from the first picking fundamental mode dispersion curve, is shown in the lower figure. the data-processing and interpretation were carried out using the geogiga seismic pro software.
fig. 1. map of the study area.the green colour shows the areas where the seismic noise was recorded. this noise is assumed to be generated by vehicle traffic. the blue colour shows the area where the geological engineering survey was carried out, which is assumed to be generated by surface-related activities. the yellow colour shows the area where the rayleigh wave inversion was carried out.
fig. 5. power spectrum of the seismic noise shown in fig. 4. a) frequency range of 5-22hz, where the strongest seismic energy was observed. b) energy distribution in the frequency range between 5 and 22hz. the peaks are in accordance with the strongest seismic energy observed in the seismic noise.
seismic interferometry requires that the seismic signals, recorded at the receivers, be cross-correlated. in the cross-correlation, a signal received at a certain receiver is cross-correlated with the signal received at another receiver, the time lag being set by the selected processing interval. the cross-correlation technique is widely used in data processing and imaging (e.g., xia et al. 1999 ). the cross-correlation of seismic signals recorded at the two receivers is a linear operation, which is a cross-product of the signals recorded at the receivers. in the case of a spatially distributed signal, the cross-correlation calculation is a convolution of the signal with the impulse response of the medium. the convolution requires an additional assumption on the shape of the impulse response of the medium. two cases are commonly used: the exponential and the gaussian response (e. in the case of the exponential response, only the time lag of the pulse at the receivers will be taken into account. the gaussian response takes into account the spatial distribution of the impulse response of the medium and the shape of the distribution function of the medium response at the receiver locations. in the case of the gaussian response, the correlation at a receiver can be obtained as the product of the impulse response of the medium and the product of the impulse response at the receiver and the signal. in the correlation method, the product of the impulse responses and the signal is called a cross-spectrum. the cross-spectrum is the fourier transform of the cross-correlation function. the time lags in the cross-correlation can be set by selecting a certain time shift between two signals and applying the fourier transform to the cross-correlation function. then, the frequency spectrum of the cross-correlation function is calculated. in this method, the time lags between the signals are determined and a cross-spectrum is obtained. the fourier transform of the cross-spectrum is the so-called convolution spectrum, which allows to obtain information on the impulse response of the medium from the cross-spectrum. the influence of the time lag on the results of the cross-spectrum is much lower than in the case of the cross-correlation and, as a result, the time lag can be selected as a larger value. this is the main advantage of the cross-correlation method.
3d information on s-wave velocity is obtained by means of cross-correlation, which is a method used to evaluate the velocity change, particularly in the case of shallow-buried geological mediums. the resulting model can be used to create the structures of geological mediums, i.e., to obtain the occurrence of geological faults or to determine the position of the boundaries of geological mediums. the method can be applied to the study of the groundwater circulation and to the study of conditions on its pipelines. moreover, seismic data are often contaminated with stationary noise. the method of automatic filtering of noise, which is widely used in the geophysics field, can be applied to obtain the original signal in the case of an ambient noise with the characteristics of a stationary signal. if the signal is sufficiently clean, the method of ambient noise cross-correlation will be more effective in the analysis of the structure of mediums and of the nature of the underground, and it can be used as a tool in hydrogeology.
the method of ambient noise cross-correlation used in the present study is based on the premise that the noise is stationary. however, in practice, the ambient noise may change with time, which causes the distortion of the results and makes the interpretation of the obtained structure of the medium more difficult. to prevent this, the method should be preceded by the filtering of noise, which requires a preliminary analysis of the signal. the method of ambient noise cross-correlation is based on the following assumptions: the noise is not correlated to the true signal, and the noise is stationary. the number of measurements used for cross-correlation is determined by the signal-to-noise ratio (snr), which is the ratio between the signal power and the noise power. to process the seismic data, the method of ambient noise cross-correlation is used. to automatically filter the noise, a special algorithm based on the threshold of the detected signal is used. the value of the threshold is selected so as to optimize the processing of the seismic data and the output images. the application of the method of ambient noise cross-correlation in the study of the fracture network of the quarry of the origin of the ambient noise was identified by determining the velocity of the ambient noise at the frequency of the signals of the registered seismic data. the ambient noise was selected for the purpose of processing the seismic data, which was assumed to be stationary. the ambient noise that was registered in the current study had a frequency of approximately 4hz. however, since the ambient noise registered in the current study had a low frequency, the approximation of the ambient noise frequency by the frequency of the ambient noise registered in the current study was sufficient. the ambient noise was selected as the reference signal for processing the seismic data.