Deconvolutive Radon transform

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. V117-V125 ◽  
Author(s):  
Ali Gholami
Keyword(s):  
Data Processing ◽  
Temporal Resolution ◽  
Radon Transform ◽  
Seismic Data ◽  
Model Building ◽  
Random Noise ◽  
Velocity Model ◽  
Seismic Events ◽  
Seismic Data Processing ◽  
Band Limited

The Radon transform (RT) plays an important role in seismic data processing for its ability to focus seismic events in the transform domain. The band-limited nature of seismic events due to the blurring effects of the source wavelet, however, causes a decrease in the temporal resolution of the transform. We have developed the deconvolutive RT (DecRT) as a generalization of conventional RT and to increase the temporal resolution. Unlike the conventional counterpart, the new basis functions can take an arbitrary shape in the time direction. This method is thus proposed to adaptively infer the temporal wave shape from the input data while finding a sparse representation of it. The new transform significantly improves the sparsity and thus the temporal resolution of the resulting seismic data. The applicability of the hyperbolic DecRT in seismic data processing is demonstrated for random noise attenuation, primary and multiple separation, high-quality stacking, and automatic velocity model building. The results obtained on synthetic and field data sets confirm the effectiveness of the method in improving the time and slowness/curvature resolutions compared with conventional transforms, which leads to improved seismic processing results in the deconvolutive Radon domains.

Applying the Horizon Based Tomography Method to Update Interval Velocity Model, Identify The Structure of Pre-Stack Depth Migration 3D and Estimate The Hydrocarbon Reserve In SBI Field of North West Java Basin

2014 ◽  
Vol 69 (6) ◽  
Author(s):  
Sudra Irawan ◽  
Sismanto Sismanto ◽  
Adang Sukmatiawan
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Velocity Model ◽  
Research Area ◽  
Seismic Data Processing ◽  
West Java ◽  
North West ◽  
Depth Migration ◽  
Interval Velocity ◽  
Tomography Method

Seismic data processing is one of the three stages in the seismic method that has an important role in the exploration of oil and gas. Without good data processing, it is impossible to get seismic image cross section for good interpretation. A research using seismic data processing was done to update the velocity model by horizon based tomography method in SBI Field, North West Java Basin. This method reduces error of seismic wave travel time through the analyzed horizon because the existence velocity of high lateral variation in research area. There are three parameters used to determine the accuracy of the resulting interval velocity model, namely, flat depth gathers, semblance residual moveout that coincides with the axis zero residual moveout, and the correspondence between image depth (horizon) with wells marker  (well seismic tie). Pre Stack Depth Migration (PSDM) form interval velocity model and updating using horizon-based tomography method gives better imaging of under-surfaced structure results than PSDM before using tomography. There are three faults found in the research area, two normal faults have southwest-northeast strike and the other has northwest-southeast strike. The thickness of reservoir in SBI field, North West Java Basin, is predicted between 71 to 175 meters and the hydrocarbon (oil) reserve is predicted about  with 22.6% porosity and 70.7% water saturation. 

ADVANCES IN 3-D SEISMIC DATA PROCESSING TECHNIQUES

1992 ◽  
Vol 32 (1) ◽  
pp. 276
Author(s):  
T.J. Allen ◽  
P. Whiting
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Three Dimensional ◽  
Velocity Model ◽  
Migration Velocity ◽  
Seismic Data Processing ◽  
Data Set ◽  
Migration Velocity Analysis ◽  
Processing Techniques ◽  
Made In

Several recent advances made in 3-D seismic data processing are discussed in this paper.Development of a time-variant FK dip-moveout algorithm allows application of the correct three-dimensional operator. Coupled with a high-dip one-pass 3-D migration algorithm, this provides improved resolution and response at all azimuths. The use of dilation operators extends the capability of the process to include an economical and accurate (within well-defined limits) 3-D depth migration.Accuracy of the migration velocity model may be improved by the use of migration velocity analysis: of the two approaches considered, the data-subsetting technique gives more reliable and interpretable results.Conflicts in recording azimuth and bin dimensions of overlapping 3-D surveys may be resolved by the use of a 3-D interpolation algorithm applied post 3-D stack and which allows the combined surveys to be 3-D migrated as one data set.

EMD-seislet transform

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. A27-A32 ◽  
Author(s):  
Yangkang Chen ◽  
Sergey Fomel
Keyword(s):  
Data Processing ◽  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Seismic Events ◽  
Variable Frequency ◽  
Seismic Data Processing ◽  
Local Slope ◽  
Mode Decomposition ◽  
Frequency Components

The seislet transform uses a prediction operator that is connected to the local slope or frequency of seismic events. We have combined the 1D nonstationary seislet transform with empirical-mode decomposition (EMD) in the [Formula: see text]-[Formula: see text] domain. We used EMD to decompose data into smoothly variable frequency components for the following 1D seislet transform. The resultant representation showed remarkable sparsity. We developed a detailed algorithm and used a field example to demonstrate the application of the new seislet transform for sparsity-promoting seismic data processing.

Use of the slant stack for geologic or geophysical map lineament analysis

Geophysics ◽  
1997 ◽  
Vol 62 (6) ◽  
pp. 1774-1778 ◽  
Author(s):  
Robert S. Pawlowski
Keyword(s):  
Plane Wave ◽  
Radon Transform ◽  
Seismic Data ◽  
Seismic Events ◽  
Seismic Data Processing ◽  
Coherent Noise ◽  
Plane Wave Decomposition ◽  
Lineament Analysis ◽  
The Individual ◽  
Wave Decomposition

The slant‐stack technique (also known as Radon transform, τ-p transform, and plane‐wave decomposition) used in seismic data processing for discriminating between and separating seismic events of differing dips (or moveout) is applied here to the problem of geologic or geophysical map lineament analysis. The latter problem is analogous to the seismic coherent noise problem in the sense that lineaments associated with one geologic event or episode are often underprinted by the lineaments of preceding geologic disturbances and overprinted by the lineaments of subsequent disturbances. Consequently, it can be difficult to distinguish between the individual lineament sets.

Nonstatinary deconvolutive Radon transform

Geophysics ◽  
2021 ◽  
pp. 1-35
Author(s):  
Hojjat Haghshenas Lari ◽  
Ali Gholami
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
Estimation Method ◽  
Original Data ◽  
Seismic Events ◽  
Seismic Data Processing ◽  
Convolution Operation ◽  
Inverse Transform ◽  
Time Space ◽  
Overlap Method

Different versions of the Radon transform (RT) are widely used in seismic data processing tofocus the recorded seismic events. Multiple separation, data interpolation, and noise attenuationare some of RT applications in seismic processing work-flows. Unfortunately, the conventional RTmethods cannot focus the events perfectly in the RT domain. This problem arises due to theblurring effects of the source wavelet and the nonstationary nature of the seismic data. Sometimes,the distortion results in a big difference between the original data and its inverse transform. Wepropose a nonstationary deconvolutive RT to handle these two issues. Our proposed algorithm takesadvantage of a nonstationary convolution technique. that builds on the concept of block convolutionand the overlap method, where the convolution operation is defined separately for overlapping blocks.Therefore, it allows the Radon basis function to take arbitrary shapes in time and space directions. Inaddition, we introduce a nonstationary wavelet estimation method to determine time-space-varyingwavelets. The wavelets and the Radon panel are estimated simultaneously and in an alternative way.Numerical examples demonstrate that our nonstationary deconvolutive RT method can significantlyimprove the sparsity of Radon panels. Hence, the inverse RT does not suffer from the distortioncaused by the unfocused seismic events.

DEVELOPMENTS IN SEISMIC DATA PROCESSING AND ANALYSIS (1968–1970)

Geophysics ◽  
1971 ◽  
Vol 36 (6) ◽  
pp. 1043-1073 ◽  
Author(s):  
William A. Schneider
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Model Building ◽  
Processing Parameters ◽  
Subject Area ◽  
Two Dimensions ◽  
Seismic Data Processing ◽  
Seismic Processing ◽  
Reflection Seismic ◽  
Stratigraphic Model

The subject matter of this review paper pertains to developments during the past several years in the area of reflection seismic data processing and analysis. While this subject area is extensive in both its breadth and scope, one indisputable fact emerges: the computer is now more pervasive than ever. Processing areas which were computer intensive, such as signal enhancement, are now even more so; and those formerly exclusive domains of man, such as seismic interpretation, are beginning to feel the encroachment of the large number crunchers. What the future holds is anyone’s guess, but it is quite probable that man and computer will share the throne if the interactive seismic processing systems on the drawing boards come to pass. For the present and recent past, however, the most intensively developed areas of seismic data processing and analysis include 1) computer extraction of processing parameters such as stacking velocity and statics, 2) automated detection and tracking of reflections in multidimensional parameter space to provide continuous estimates of traveltime, amplitude, moveout (velocity), dip, etc., 3) direct digital migration in two dimensions, giving improved subsurface “pictures” and utilizing diffraction energy normally lost by specular processing techniques, and 4) development of quantitative understanding of the limitations imposed by current seismic processing practice and assumptions with regard to structural and stratigraphic model building, and recognition of the ultimate need for an iterative signal processing—information extraction—model building closed loop system.

scholarly journals Elastic Full-Waveform Inversion Using Migration-Based Depth Reflector Representation in the Data Domain

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 76
Author(s):  
Vladimir Tcheverda ◽  
Kirill Gadylshin
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Critical Element ◽  
Full Waveform Inversion ◽  
Seismic Data Processing ◽  
Velocity Models ◽  
Data Space ◽  
Full Waveform

The depth velocity model is a critical element for providing seismic data processing success, as it is responsible for the times of waves’ propagation and, therefore, prescribes the location of geological objects in the resulting seismic images. Constructing a deep velocity model is the most time-consuming part of the entire seismic data processing, which usually requires interactive human intervention. This article introduces the consistently numerical method for reconstructing a depth velocity model based on the modified version of the elastic Full Waveform Inversion (FWI). The specific feature of this approach to FWI is the decomposition of the space of admissible velocity models into subspaces of propagator (macro velocity) and reflector components. In turn, the latter transforms to the data space reflectivity on the base of migration transformation. Finally, we perform minimisation in two different spaces: (1) Macro velocity as a smooth spatial function; (2) Migration transforms data space reflectivity to the spatial reflectivity. We present numerical experiments confirming less sensitiveness of the modified version of FWI to the lack of the low time frequencies in the data acquired. In our computations, we use synthetic data with valuable time frequencies from 5 Hz.

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