Oriented pre-stack inverse Q filtering for resolution enhancements of seismic data

2020 ◽  
Vol 223 (1) ◽  
pp. 488-501
Author(s):  
Guochang Liu ◽  
Chao Li ◽  
Ying Rao ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Seismic Waves ◽  
Attenuation Factor ◽  
Real Data ◽  
Velocity Model ◽  
Seismic Attenuation ◽  
Propagation Path ◽  
Local Slope ◽  
Filtering Method ◽  
Inverse Q Filtering

SUMMARY Seismic attenuation is one of the main factors responsible for degradation of the resolution of seismic data. During seismic wave propagation in attenuation medium, the energy of signal components seriously decreases, especially those with higher frequencies. The seismic attenuation and resolution reduction are generally compensated for with inverse Q filtering in the frequency or time domain. However, the implementation of pre-stack inverse Q filtering is challenging because the traveltime in each layer is not easy to obtain for the pre-stack seismic gather, unless the accurate velocity model is known. In this study, we propose an inverse Q filtering method for the pre-stack seismic gather that uses the local slope and warped mapping to determine the propagation path, and Taylor-expansion-based division is used to stabilize the inversion. The local slope can determine the reflection events with the same ray path, and the inverse warped mapping can transform the attenuation factor from the ${t_0} - p$ (zero-offset traveltime to ray parameter) domain to the $t - x$ (traveltime and offset) domain. The attenuation factor in the ${t_0} - p$ domain is easy to calculate because the traveltimes and Q values in each layer are known. The proposed oriented pre-stack inverse Q filtering method is velocity-independent and suitable for a depth varying Q model. The synthetic and real data examples demonstrated that the method can effectively correct the attenuation and dispersion of seismic waves, and can obtain pre-stack seismic gathers with high resolution.

2-D continuation operators and their applications

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1846-1858 ◽  
Author(s):  
Claudio Bagaini ◽  
Umberto Spagnolini
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Integral Form ◽  
Amplitude Distribution ◽  
Velocity Model ◽  
Velocity Analysis ◽  
Seismic Data Processing ◽  
Analysis Method ◽  
Standard Tool ◽  
Zero Offset

Continuation to zero offset [better known as dip moveout (DMO)] is a standard tool for seismic data processing. In this paper, the concept of DMO is extended by introducing a set of operators: the continuation operators. These operators, which are implemented in integral form with a defined amplitude distribution, perform the mapping between common shot or common offset gathers for a given velocity model. The application of the shot continuation operator for dip‐independent velocity analysis allows a direct implementation in the acquisition domain by exploiting the comparison between real data and data continued in the shot domain. Shot and offset continuation allow the restoration of missing shot or missing offset by using a velocity model provided by common shot velocity analysis or another dip‐independent velocity analysis method.

Relative acoustic impedance from wavelet transform

2014 ◽  
Vol 2 (1) ◽  
pp. SA107-SA118 ◽  
Author(s):  
Marcílio Castro de Matos ◽  
Rodrigo Penna ◽  
Paulo Johann ◽  
Kurt Marfurt
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Real Data ◽  
Velocity Model ◽  
Continuous Wavelet ◽  
Reconstruction Process ◽  
Seismic Wavelet ◽  
Morlet Continuous Wavelet Transform

Most deconvolution algorithms try to transform the seismic wavelet into spikes by designing inverse filters that remove an estimated seismic wavelet from seismic data. We assume that seismic trace subtle discontinuities are associated with acoustic impedance contrasts and can be characterized by wavelet transform spectral ridges, also called modulus maxima lines (WTMML), allowing us to improve seismic resolution by using the wavelet transform. Specifically, we apply the complex Morlet continuous wavelet transform (CWT) to each seismic trace and compute the WTMMLs. Then, we reconstruct the seismic trace with the inverse continuous wavelet transform from the computed WTMMLs with a broader band complex Morlet wavelet than that used in the forward CWT. Because the reconstruction process preserves amplitude and phase along different scales, or frequencies, the result looks like a deconvolution method. Considering this high-resolution seismic representation as a reflectivity approximation, we estimate the relative acoustic impedance (RAI) by filtering and trace integrating it. Conventional deconvolution algorithms assume the seismic wavelet to be stochastic, while the CWT is implicitly time varying such that it can be applied to both depth and time-domain data. Using synthetic and real seismic data, we evaluated the effectiveness of the methodology on detecting seismic events associated with acoustic impedance changes. In the real data examples, time and in-depth RAI results, show good correlation with real P-impedance band-pass data computed using more rigorous commercial inversion software packages that require well logs and low-frequency velocity model information.

MAZ depth-velocity modelling and imaging with azimuthal anisotropy

2010 ◽  
Vol 50 (2) ◽  
pp. 723
Author(s):  
Sergey Birdus ◽  
Erika Angerer ◽  
Iftikhar Abassi
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Azimuthal Velocity ◽  
Velocity Model ◽  
Azimuthal Anisotropy ◽  
Lessons Learned ◽  
Seismic Survey ◽  
Depth Migration ◽  
Velocity Anisotropy ◽  
Interval Velocity

Processing of multi and wide-azimuth seismic data faces some new challenges, and one of them is depth-velocity modelling and imaging with azimuthal velocity anisotropy. Analysis of multi-azimuth data very often reveals noticeable fluctuations in moveout between different acquisition directions. They can be caused by several factors: real azimuthal interval velocity anisotropy associated with quasi-vertical fractures or present day stress field within the sediments; short-wavelength velocity heterogeneities in the overburden; TTI (or VTI) anisotropy in the overburden; or, random distortions due to noise, multiples, irregularities in the acquisition geometry, etcetera. In order to build a velocity model for multi-azimuth pre-stack depth migration (MAZ PSDM) taking into account observed azimuthal anisotropy, we need to recognise, separate and estimate all the effects listed above during iterative depth-velocity modelling. Analysis of seismic data from a full azimuth 3D seismic land survey revealed the presence of strong spatially variable azimuthal velocity anisotropy that had to be taken into consideration. Using real data examples we discuss major steps in depth processing workflow that took such anisotropy into account: residual moveout estimation in azimuth sectors; separation of different effects causing apparent azimuthal anisotropy (see A–D above); iterative depth-velocity modelling with azimuthal anisotropy; and, subsequent MAZ anisotropic PSDM. The presented workflow solved problems with azimuthal anisotropy in our multi-azimuth dataset. Some of the lessons learned during this MAZ project are relevant to every standard narrow azimuth seismic survey recorded in complex geological settings.

Seismic modeling of gas chimneys

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. SM251-SM259 ◽  
Author(s):  
Børge Arntsen ◽  
Lars Wensaas ◽  
Helge Løseth ◽  
Christian Hermanrud
Keyword(s):  
Seismic Waves ◽  
Real Data ◽  
Velocity Model ◽  
Fracture Network ◽  
Gas Diffusion ◽  
Elastic Model ◽  
Geological Time ◽  
Data Set ◽  
Gas Saturation ◽  
Gas Chimneys

We propose a simple acoustic model explaining the main features of gas chimneys. The main elements of the model consist of gas diffusing from a connected fracture network and into the surrounding shale creating an inhomogeneous gas saturation. The gas saturation results in an inhomogeneous fluctuating compressional velocity field that distorts seismic waves. We model the fracture network by a random-walk process constrained by maximum fracture length and angle of the fracture with respect to the vertical. The gas saturation is computed from a simple analytical solution of the diffusion equation, and pressure-wave velocities are locally obtained assuming that mixing of shale and gas occurs on a scale much smaller than seismic wavelengths. Synthetic seismic sections are then computed using the resulting inhomogeneous velocity model and shown to give rise to similar deterioration in data quality as that found in data from real gas chimneys. Also, synthetic common-midpoint (CMP) gathers show the same distorted and attenuated traveltime curves as those obtained from a real data set. The model shows clearly that the features of gas chimneys change with geological time (a model parameter in our approach), the deterioration of seismic waves being smallest just after the creation of the gas chimney. It seems likely that at least some of the features of gas chimneys can be explained by a simple elastic model in combination with gas diffusion from a fracture network.

Deblending of simultaneous source data using a structure-oriented space-varying median filter

2020 ◽  
Vol 222 (3) ◽  
pp. 1805-1823 ◽  
Author(s):  
Yangkang Chen ◽  
Shaohuan Zu ◽  
Yufeng Wang ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Median Filter ◽  
Noisy Data ◽  
Real Data ◽  
Filter Method ◽  
Local Slope ◽  
Median Filters ◽  
Call Structure ◽  
Slope Estimation ◽  
Simultaneous Source

SUMMARY In seismic data processing, the median filter is usually applied along the structural direction of seismic data in order to attenuate erratic or spike-like noise. The performance of a structure-oriented median filter highly depends on the accuracy of the estimated local slope from the noisy data. When local slope contains significant error, which is usually the case for noisy data, the structure-oriented median filter will still cause severe damages to useful energy. We propose a type of structure-oriented median filter that can effectively attenuate spike-like noise even when the local slope is not accurately estimated, which we call structure-oriented space-varying median filter. A structure-oriented space-varying median filter can adaptively squeeze and stretch the window length of the median filter when applied in the locally flattened dimension of an input seismic data in order to deal with the dipping events caused by inaccurate slope estimation. We show the key difference among different types of median filters in detail and demonstrate the principle of the structure-oriented space-varying median filter method. We apply the structure-oriented space-varying median filter method to remove the spike-like blending noise arising from the simultaneous source acquisition. Synthetic and real data examples show that structure-oriented space-varying median filter can significantly improve the signal preserving performance for curving events in the seismic data. The structure-oriented space-varying median filter can also be easily embedded into an iterative deblending procedure based on the shaping regularization framework and can help obtain much improved deblending performance.

Qualitative seismic attenuation parameter estimation based on prestack data in the continuous wavelet domain

2017 ◽  
Vol 5 (2) ◽  
pp. T199-T207 ◽  
Author(s):  
Wenchao Chen ◽  
Xiaokai Wang ◽  
Dan Wu ◽  
Lei Gao ◽  
Jinghuai Gao
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Attenuation Factor ◽  
Low Frequency ◽  
Frequency Component ◽  
Seismic Attenuation ◽  
High Frequency Component ◽  
Continuous Wavelet ◽  
Anelastic Attenuation ◽  
Seismic Quality Factor

Because the seismic wave propagates through the subsurface, part of the elastic energy eventually ends up as heat energy. This phenomenon is known as absorption (or anelastic attenuation). The factors causing anelastic attenuation include fluid movement and grain boundary friction. The seismic quality factor ([Formula: see text]) quantifies the anelastic attenuation and is commonly used in assisting reservoir characterization. However, current [Formula: see text]-estimation approaches are mainly implemented on a poststack seismic volume. The [Formula: see text]-estimation approaches applied to poststack seismic data assume that the seismic data are normal incident reflections, and they do not consider the effect of the travel path on seismic attenuation. In theory, the attenuation degree of the low-frequency component should differ from the attenuation degree of the high-frequency component for large-offset seismic data. We have developed a method to qualitatively estimate seismic attenuation in the prestack seismic domain. A continuous wavelet transform is used to extract the low- and high-frequency components for the common-reflection point gathers. The difference between the amplitude of the low-frequency component and the amplitude of the high-frequency component is used to measure the seismic attenuation factor. We have determined the effectiveness of our method by applying it to synthetic and real seismic data.

A depth variant seismic wavelet extraction method for inversion of poststack depth-domain seismic data

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R569-R579 ◽  
Author(s):  
Rui Zhang ◽  
Zhiwen Deng
Keyword(s):  
Seismic Data ◽  
Extraction Method ◽  
Reservoir Characterization ◽  
Real Data ◽  
Velocity Model ◽  
Seismic Inversion ◽  
Data Depth ◽  
Spectral Variation ◽  
Seismic Wavelet ◽  
The Time Domain

Prestack depth seismic imaging is increasingly being used in industry, which has also led to an increasing need for its inversion results, such as acoustic impedance (AI), for reservoir characterization. Conventional seismic inversion methods for reservoir characterization are usually implemented in the time domain. A depth-time conversion would be required before inversion of depth-domain seismic data, which would depend on an accurate velocity model and a fine time-depth conversion algorithm. Thus, it could be beneficial that we can directly invert the depth migrated seismic data. Depth-domain seismic data could indicate a strong nonstationarity, such as spectral variation, which makes it difficult to use a constant wavelet for direct inversion in depth. To address this issue, we have developed a new wavelet extraction method by using a depth-wavenumber decomposition technique, which can generate depth variant wavelets to accommodate the nonstationarity of the depth-domain seismic data. The synthetic and real data applications have been used to test the effectiveness of our method. The directly inverted depth-domain AI indicates a good correlation with well-log data and a strong potential for reservoir characterization.

Accurate imaging of surface seismic data without a velocity model

2007 ◽  
Author(s):  
Sverre Brandsberg-Dahl ◽  
Brian E. Hornby ◽  
Xiang Xiao
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
Accurate Imaging

scholarly journals Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1090
Author(s):  
Wenxu Wang ◽  
Damián Marelli ◽  
Minyue Fu
Keyword(s):  
Maximum Likelihood ◽  
Particle Filtering ◽  
Indoor Localization ◽  
Real Data ◽  
High Accuracy ◽  
Classical Particle ◽  
Popular Approach ◽  
Filtering Method ◽  
Essential Idea ◽  
Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

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