scholarly journals Stable Q analysis on vertical seismic profiling data

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. D217-D225 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Frequency Domain ◽  
Text Analysis ◽  
Signal To Noise Ratio ◽  
Attenuation Measurement ◽  
Time Frequency ◽  
Seismic Profiling ◽  
Vertical Seismic Profiling ◽  
Seismic Waveforms ◽  
Vsp Data ◽  
Fitting In

Vertical seismic profiling (VSP) provides a direct observation of seismic waveforms propagating to various depths within the earth’s subsurface. The [Formula: see text] analysis or attenuation ([Formula: see text]) analysis based on direct comparison between individual waveforms at different depths, however, suffers from the problem of instability commonly due to fluctuations inherent in the frequency spectrum of each waveform. To improve the stability, we considered frequency and time variations and conducted [Formula: see text] analysis on an integrated observation. First, we transformed the time- (or depth-) frequency-domain spectrum to a 1D attenuation measurement with respect to a single variable, the product of time and frequency. Although this 1D measurement has a higher signal-to-noise ratio than the 2D spectrum in the time-frequency domain, it can also be used to further generate a stabilized compensation function. Then, we implemented two [Formula: see text]-analysis methods by data fitting (in a least-squares sense) to either the attenuation measurement or the data-driven gain function. These two methods are theoretically consistent and practically robust for conducting [Formula: see text] analysis on field VSP data.

scholarly journals An extraction method of dispersion curve from vertical seismic profiling data

2020 ◽  
Author(s):  
Zhi Hu ◽  
Jinghuai Gao ◽  
Yanbin He ◽  
Guowei Zhang
Keyword(s):  
Dispersion Curve ◽  
Group Velocity ◽  
Body Wave ◽  
Seismic Signal ◽  
Time Frequency ◽  
Seismic Profiling ◽  
Vertical Seismic Profiling ◽  
Wave Data ◽  
Vsp Data ◽  
The Relationship

Abstract The dispersion curve describes the relationship between velocities and frequencies. The group velocity is a kind of dispersion, which presents the velocities of the energy with different frequencies. Although many studies have shown methods for estimating group velocity from a surface wave, the estimation of group velocity from body-wave data is still hard. In this paper, we propose a method to calculate the group velocity from vertical seismic profiling (VSP) data that is a kind of body-wave data. The generalised S-transform (GST) is used to map the seismic signal to the time-frequency (TF) domain and then the group delay (GD) can be extracted from the TF domain. The GD shows the travelling time of different frequency components. The group velocity can be calculated by the GD and the distance between receivers. Unfortunately, the GD is hard to measure accurately because of the noise. Inaccurate GD introduces errors in estimating the velocity. To reduce the errors, we make use of the multiple traces and the iterative least-squares fitting to extract the relationship line between GD and depths. The slope of the line is the reciprocal of the group velocity. Two numerical examples prove the effectiveness of the method. We also derive the formula of group velocity in diffusive-viscous media. In the field data example, the dispersion intensity at different depths and the geological layers can be well matched. These examples illustrate the proposed method is an alternative method for dispersion estimation from VSP.

Calculate Formation Velocity from Vertical Seismic Profiling Data

2014 ◽  
Vol 599-601 ◽  
pp. 639-642
Author(s):  
Jun Zhou ◽  
Chun Hui Xie ◽  
Peng Yang
Keyword(s):  
Random Errors ◽  
Seismic Profiling ◽  
Interval Velocity ◽  
Vertical Seismic Profiling ◽  
Long Offset ◽  
Vsp Data ◽  
Velocity Information

Extracting interval velocity is one of important applications of VSP data. Also, imaging of VSP data requires accurate velocity information. Two kinds of algorithms on the assumption of straight-ray and curve-ray are employed to calculate interval velocity respectively. Comparison of the extracted velocity from the two methods above with real velocity shows that both methods are suitable for VSP data recorded in the vicinity of well, while the algorithm derived from straight-ray fails in the long-offset. Moreover, the curve-ray is more reliable when there are some random errors due to the first arrivals picking.

On: “Vertical seismic profiling: Separation of upgoing and downgoing acoustic waves in a stratified medium” by B. Seeman and L. Horowicz (GEOPHYSICS, 48, 555–568, May 1983)

Geophysics ◽  
1986 ◽  
Vol 51 (5) ◽  
pp. 1148-1149
Author(s):  
S. D. Stainsby ◽  
M. H. Worthington
Keyword(s):  
Acoustic Waves ◽  
Sampling Interval ◽  
Stratified Medium ◽  
Time Shift ◽  
Reference Level ◽  
Seismic Profiling ◽  
Vertical Seismic Profiling ◽  
Cutoff Frequencies ◽  
The Earth ◽  
Vsp Data

Seeman and Horowicz devised an elegant procedure for the separation of upgoing and downgoing waves in VSP data. Their method is based upon a least‐squares solution of the frequency‐domain equations which relate the upgoing and downgoing signals at a reference level to the observed signals at other levels in the Earth. The coefficients of these equations are time‐shift operations. Unfortunately, for frequencies [Formula: see text] where δt is the vertical time sampling interval, the denominator of the solution equations is zero. For this reason the authors only applied the method over a passband: [Formula: see text] where the cutoff frequencies [Formula: see text] and [Formula: see text] are chosen to reflect the useful frequency band of the signal.

Analysis and removal of multiply scattered tube waves

Geophysics ◽  
2000 ◽  
Vol 65 (3) ◽  
pp. 745-754 ◽  
Author(s):  
Gérard C. Herman ◽  
Paul A. Milligan ◽  
Qicheng Dong ◽  
James W. Rector
Keyword(s):  
Wave Field ◽  
Large Amplitude ◽  
Data Set ◽  
Impedance Function ◽  
Seismic Profiling ◽  
Vertical Seismic Profiling ◽  
Total Data ◽  
Vsp Data ◽  
Tube Wave

Because of irregularities in or near the borehole, vertical seismic profiling (VSP) or crosswell data can be contaminated with scattered tube waves. These can have a large amplitude and can interfere with weaker upcoming reflections, destroying their continuity. This type of organized noise cannot always be removed with filtering methods currently in use. We propose a method based on modeling the scattered tube‐wave field and then subtracting it from the total data set. We assume that the scattering occurs close to the borehole axis and therefore use a 1-D impedance function to characterize borehole irregularities. Estimation of this impedance function is one of the first steps. Our method also accounts for multiply scattered tube waves. We apply the method to an actual VSP data set and conclude that the continuity of reflected, upcoming events improves significantly in a washout zone.

Correlation between seismic diffractions extracted from vertical seismic profiling data and borehole logging in a carbonate environment

2015 ◽  
Vol 3 (2) ◽  
pp. T121-T129 ◽  
Author(s):  
Alexander Klokov ◽  
Damir Irkabaev ◽  
Osareni C. Ogiesoba ◽  
Nail Munasypov
Keyword(s):  
Joint Analysis ◽  
Seismic Attribute ◽  
Data Set ◽  
Borehole Logging ◽  
Seismic Profiling ◽  
Vertical Seismic Profiling ◽  
Fluid Saturation ◽  
Attribute Analysis ◽  
Vsp Data ◽  
Tectonic Dislocations

Seismic diffractions may play an important role in seismic interpretation because they characterize geologic objects that might not be visible for conventional seismic attribute analysis. Diffractivity may be caused by, and consequently may define, tectonic dislocations (faults and fractures), lithologic variations, and fluid saturation within rocks. We have tied seismic diffractions extracted from vertical seismic profiling (VSP) data and borehole logging, from which we recognized the reasons that were responsible for diffractivity of the strata. First, we processed a multisource multicomponent VSP data set to extract seismic diffractions and constructed diffraction images of the strata for all three of the VSP data components. Then, we performed joint analysis of well logs and diffractions to obtain petrophysical attributes associated with diffraction images. We divided the rock succession into several units, which have different diffraction properties. We identified compacted rock, alternating intervals, isolated fractured zones, and fluid-saturated layers.

scholarly journals An eLoran Signal Cycle Identification Method Based on Joint Time–Frequency Domain

2022 ◽  
Vol 14 (2) ◽  
pp. 250
Author(s):  
Wenhe Yan ◽  
Ming Dong ◽  
Shifeng Li ◽  
Chaozhong Yang ◽  
Jiangbin Yuan ◽  
...  
Keyword(s):  
Success Rate ◽  
Frequency Domain ◽  
Measurement Errors ◽  
Signal To Noise Ratio ◽  
Global Navigation Satellite Systems ◽  
Time Of Arrival ◽  
Satellite Systems ◽  
Time Frequency ◽  
Identification Method ◽  
The Time Domain

The eLoran system is an international standardized positioning, navigation, and timing service system, which can complement global navigation satellite systems to cope with navigation and timing warfare. The eLoran receiver measures time-of-arrival (TOA) through cycle identification, which is key in determining timing and positioning accuracy. However, noise and skywave interference can cause cycle identification errors, resulting in TOA-measurement errors that are integral multiples of 10 μs. Therefore, this article proposes a cycle identification method in the joint time–frequency domain. Based on the spectrum-division method to determine the cycle identification range, the time–domain peak-to-peak ratio and waveform matching are used for accurate cycle identification. The performance of the method is analyzed via simulation. When the signal-to-noise ratio (SNR) ≥ 0 dB and skywave-to-groundwave ratio (SGR) ≤ 23 dB, the success rate of cycle identification is 100%; when SNR ≥ −13 dB and SGR ≤ 23 dB, the success rate exceeds 75%. To verify its practicability, the method was implemented in the eLoran receiver and tested at three test sites within 1000 km using actual signals emitted by an eLoran system. The results show that the method has a high identification probability and can be used in modern eLoran receivers to improve TOA-measurement accuracy.

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