Nonhyperbolic converted wave velocity analysis and normal moveout

1996 ◽  
Author(s):  
Yaohui Zhang
Keyword(s):  
Wave Velocity ◽  
Velocity Analysis ◽  
Converted Wave

Determining optimum wave velocity from sparse CMP automatically by coupling POCS interpolation and NMO correction: application to array antenna GPR data collected during in-situ infiltration test

2021 ◽  
Author(s):  
Koki Oikawa ◽  
Hirotaka Saito ◽  
Seiichiro Kuroda ◽  
Kazunori Takahashi
Keyword(s):  
Wave Velocity ◽  
Convex Sets ◽  
Numerical Study ◽  
Sand Dunes ◽  
Time Lapse ◽  
Array Antenna ◽  
Velocity Analysis ◽  
Nmo Correction ◽  
Infiltration Experiment

<p>As an array antenna ground penetrating radar (GPR) system electronically switches any antenna combinations sequentially in milliseconds, multi-offset gather data, such as common mid-point (CMP) data, can be acquired almost seamlessly. However, due to the inflexibility of changing the antenna offset, only a limited number of scans can be obtained. The array GPR system has been used to collect time-lapse GPR data, including CMP data during the field infiltration experiment (Iwasaki et al., 2016). CMP data obtained by the array GPR are, however, too sparse to obtain reliable velocity using a standard velocity analysis, such as semblance analysis. We attempted to interpolate the sparse CMP data based on projection onto convex sets (POCS) algorithm (Yi et al., 2016) coupled with NMO correction to automatically determine optimum EM wave velocity. Our previous numerical study showed that the proposed method allows us to determine the EM wave velocity during the infiltration experiment.</p><p>The main objective of this study was to evaluate the performance of the proposed method to interpolate sparse array antenna GPR CMP data collected during the in-situ infiltration experiment at Tottori sand dunes. The interpolated CMP data were then used in the semblance analysis to determine the EM wave velocity, which was further used to compute the infiltration front depth. The estimated infiltration depths agreed well with independently obtained depths. This study demonstrated the possibility of developing an automatic velocity analysis based on POCS interpolation coupled with NMO correction for sparse CMP collected with array antenna GPR.</p>

Multi-parameter model building for the Qiuyue structure using four-component ocean-bottom seismometer data

Geophysics ◽  
2021 ◽  
pp. 1-52
Author(s):  
Yuzhu Liu ◽  
Xinquan Huang ◽  
Jizhong Yang ◽  
Xueyi Liu ◽  
Bin Li ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Velocity Analysis ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Full Waveform ◽  
P Wave Velocity

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multi-channel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom seismometer (OBS) acquisition program acquired a four-component dataset in East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we applied a full-waveform inversion (FWI) workflow to this dense four-component OBS dataset. After preprocessing, including receiver geometry correction, moveout correction, component rotation, and energy transformation from 3D to 2D, a preconditioned first-arrival traveltime tomography based on an improved scattering integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavefield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at 2.0 km and 4.7 km depth. Initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic full-waveform inversion to refine both velocity models. Compared to a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single-component, the workflow presented in this study represents a good approach for inverting the four-component OBS dataset to characterize sub-seafloor velocity structures.

Iterative P-wave velocity inversion using Markov chain Monte Carlo method

2020 ◽  
Author(s):  
Hyunggu Jun ◽  
Hyeong-Tae Jou ◽  
Han-Joon Kim ◽  
Sang Hoon Lee
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Velocity Structure ◽  
Low Frequency ◽  
P Wave ◽  
Velocity Analysis ◽  
Seismic Section ◽  
Traveltime Tomography ◽  
P Wave Velocity ◽  
Frequency Components

<p>Imaging the subsurface structure through seismic data needs various information and one of the most important information is the subsurface P-wave velocity. The P-wave velocity structure mainly influences on the location of the reflectors during the subsurface imaging, thus many algorithms has been developed to invert the accurate P-wave velocity such as conventional velocity analysis, traveltime tomography, migration velocity analysis (MVA) and full waveform inversion (FWI). Among those methods, conventional velocity analysis and MVA can be widely applied to the seismic data but generate the velocity with low resolution. On the other hands, the traveltime tomography and FWI can invert relatively accurate velocity structure, but they essentially need long offset seismic data containing sufficiently low frequency components. Recently, the stochastic method such as Markov chain Monte Carlo (McMC) inversion was applied to invert the accurate P-wave velocity with the seismic data without long offset or low frequency components. This method uses global optimization instead of local optimization and poststack seismic data instead of prestack seismic data. Therefore, it can avoid the problem of the local minima and limitation of the offset. However, the accuracy of the poststack seismic section directly affects the McMC inversion result. In this study, we tried to overcome the dependency of the McMC inversion on the poststack seismic section and iterative workflow was applied to the McMC inversion to invert the accurate P-wave velocity from the simple background velocity and inaccurate poststack seismic section. The numerical test showed that the suggested method could successfully invert the subsurface P-wave velocity.</p>

Velocity‐depth ambiguity in the dipping reflector case

Geophysics ◽  
1997 ◽  
Vol 62 (5) ◽  
pp. 1583-1585 ◽  
Author(s):  
Brijpal S. Rathor
Keyword(s):  
Acoustic Wave ◽  
Wave Velocity ◽  
Seismic Data ◽  
Seismic Events ◽  
Velocity Analysis ◽  
Vital Parameter ◽  
Acoustic Wave Velocity ◽  
Analysis Methods

Seismic or acoustic wave velocity is a vital parameter for processing and interpretation of seismic data. Various velocity analysis methods, using traveltime moveout of seismic events, have been described in literature. In certain cases, these methods provide ambiguous results. Hence, there is a need to investigate velocity‐depth ambiguity in such cases.

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