The Effect Ricker Wavelet of Duty Cycle Adjustment on GPR Detection Result

Ground Penetrating Radar (GPR) employs an ultra-wideband (UWB) signal for detecting objects under the ground surface. In a certain GPR application, a proper UWB signal is needed to obtain a good detection result. Ricker wavelet is one type of UWB signal that can be used in GPR operation. The effect of adjusting the Ricker wavelet duty cycle on the B-scan result was investigated and the result is discussed in this paper. Laboratory experiments were performed by modelling the GPR system using Vector Network Analyzer (VNA). The result shows that selecting a Ricker wavelet’s duty cycle is successful to show the target clearly.

Revisit Seismic Attenuation Attributes: Influences of the Spectral Balancing Operation on Seismic Attenuation Analysis

Seismic attenuation analysis is important for seismic processing and quantitative interpretation. Nevertheless, the classic quality factor estimation methods make certain assumptions that may be invalid for a given geologic target and seismic volume. For this reason, seismic attenuation attribute analysis, which reduces some of the theoretical assumptions, can serve as a practical alternative in apparent attenuation characterization. Unfortunately, most of the published literature defines seismic attenuation attributes based on a specific source wavelet assumption, such as the Ricker wavelet, rather than wavelets that exhibit a relatively flat spectrum produced by modern data processing workflows. One of the most common processing steps is to spectrally balance the data either explicitly in the frequency domain, or implicitly through wavelet shaping deconvolution. If the post-stack seismic data have gone through the spectral balancing/whitening to improve the seismic resolution, the wavelet would exhibit a flat spectrum instead of a Ricker or Gaussian shape. We address the influence of the spectral balancing on seismic attenuation analysis. Our mathematical analysis shows that attenuation attributes are still effective for the post-stack seismic data after certain types of spectral balancing. More importantly, this analysis explains why seismic attenuation attributes work for real seismic applications with common seismic processing procedures. Synthetic and field data examples validate our conclusions.

Filtering of a Ricker wavelet induced by anelastic seismic wave propagation and reflection

A varying Q factor with depth induces modifications of seismic wave features due to anelastic propagation but also reflections at the discontinuities. Standard signal analysis methods often neglect the reflection contribution when assessing Q values from seismic data. We have developed an analytical quantification of the cumulative effects of both the propagation and reflection contributions by considering Kjartansson's model and a seismic plane wave at normal incidence on a step-like discontinuity. We show that the cumulative effects are equivalent to a frequency filter characterised by a bandform and phase that both depend on the ratio between the elastic and anelastic contrasts. When considering this filter applied to a Ricker wavelet, we establish an analytical expression of the peak-frequency attribute as a function of propagation and reflection properties. We demonstrate that this seismic attribute depends on the anelastic contrast, which cannot be neglected when assessing Q factors: the error in Q estimate is not linearly dependent on the anelastic contrast and we establish an analytical expression for the case where this contrast is weak. An unexpected phenomenon for a step-like interface is an increase in the peak frequency that is observed when the anelastic and elastic contrasts have opposite signs, with a constraint on the anelastic propagation properties. This behaviour allows for assessment of the elastic and anelastic parameters.

Effects of non-reflection events and stationery source locations on virtual seismic reflection images

<p>To notice key obstacles and suggest effective processing methods for virtual reflection images, numerical modeling was performed by the 2-D finite difference method with time and space intervals of 0.2 ms and 1.25 m, respectively. Vertical sources of the Ricker wavelet with a main frequency of 20 Hz were assumed to be detonated independently at five buried locations with intervals of 500 m. Vertical components of the particle velocity were computed at 99 receivers at 10 m depth with intervals of 20 m. Synthetic data show that maximum amplitudes of reflection signals are less than 2% of those of direct Rayleigh waves on an average. This indicates that the non-reflection events should be attenuated as much as possible before correlating traces to compute virtual seismic data. For attenuating both direct and diffracted Rayleigh waves in the synthetic data, a median filter with a time window of a 0.1-s length was effective. Because stationery-phase source locations for virtual reflections concentrate near receiver locations, only common midpoint gathers close to the sources should be used for good virtual stack images.</p>

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

Microseismic signals are generally considered to follow the Gauss distribution. A comparison of the dynamic characteristics of sample variance and the symmetry of microseismic signals with the signals which follow α-stable distribution reveals that the microseismic signals have obvious pulse characteristics and that the probability density curve of the microseismic signal is approximately symmetric. Thus, the hypothesis that microseismic signals follow the symmetric α-stable distribution is proposed. On the premise of this hypothesis, the characteristic exponent α of the microseismic signals is obtained by utilizing the fractional low-order statistics, and then a new method of time difference of arrival (TDOA) estimation of microseismic signals based on fractional low-order covariance (FLOC) is proposed. Upon applying this method to the TDOA estimation of Ricker wavelet simulation signals and real microseismic signals, experimental results show that the FLOC method, which is based on the assumption of the symmetric α-stable distribution, leads to enhanced spatial resolution of the TDOA estimation relative to the generalized cross correlation (GCC) method, which is based on the assumption of the Gaussian distribution.

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

Microseismic signals are generally considered to follow the Gauss distribution. A comparison of the dynamic characteristics of sample variance and the symmetry of microseismic signals with the signals which follows α-stable distribution, reveals that the pulse characteristics of the microseismic signal is outstanding and that the probability density curve of the microseismic signal is approximately symmetric. Thus, the hypothesis that microseismic signals follow the symmetric α-stable distribution is proposed. On the premise of this hypothesis, the characteristic exponent α of the seismic signals is obtained by utilizing the fractional low-order statistics, and then a new method of time difference of arrival (TDOA) estimation of microseismic signals based on fractional low-order covariance (FLOC) is proposed. Upon applying this method to the TDOA estimation of Ricker wavelet simulation signals and real microseismic signals, experimental results show that the FLOC method, which is based on the assumption of the symmetric α-stable distribution, leads to enhanced spatial resolution of the TDOA estimation relative to the generalized cross correlation (GCC) method, which is based on the assumption of the Gaussian distribution.