Time-variant wavelet extraction with a local-attribute-based time-frequency decomposition for seismic inversion

2017 ◽  
Vol 5 (1) ◽  
pp. SC9-SC16 ◽  
Author(s):  
Rui Zhang ◽  
Sergey Fomel
Keyword(s):  
Frequency Spectrum ◽  
Seismic Data ◽  
Field Data ◽  
Seismic Signal ◽  
Seismic Inversion ◽  
Kernel Matrix ◽  
Time Frequency ◽  
Seismic Wavelet ◽  
Frequency Decomposition ◽  
Noise Contamination

Seismic impedance inversion has been widely used to estimate subsurface properties. Conventional inversion assumes that seismic data are the convolution result of seismic wavelet and reflectivity, implying that seismic data are stationary when a constant wavelet is considered. However, seismic data are nonstationary because of noise contamination and attenuation during wave propagation, which means that the frequency spectrum of the seismic signal changes from shallow to deep formations. We have developed a time-variant wavelet extraction method by using a local-attribute-based spectral decomposition technique. Time-variant wavelets are generated according to the local frequency spectrum, which can be used to construct a time-variant wavelet kernel matrix. By using this time-variant kernel matrix, we can obtain a better correlation between synthetic and extracted seismograms than by using constant wavelet on a field data example. Using this example, we have also compared the time-variant and constant wavelets for inverting the field data to estimate subsurface acoustic impedance. Our results showed improved resolution and a better fit to well-log-measured impedance by using the time-variant wavelets.

Joint time-variant spectral analysis — Part 1: Forward modeling the effects of thin-bed layering

2018 ◽  
Vol 6 (4) ◽  
pp. T967-T983
Author(s):  
Ramses G. Meza ◽  
J. Antonio Sierra ◽  
John P. Castagna ◽  
Umberto Barbato
Keyword(s):  
Seismic Data ◽  
Seismic Isolation ◽  
Forward Modeling ◽  
Destructive Interference ◽  
Constructive Interference ◽  
Phase Component ◽  
Time Frequency ◽  
Seismic Wavelet ◽  
Specific Frequency ◽  
Net To Gross

Using time-frequency and time-phase analysis we found that for an isolated thin bed in a binary-impedance setting, there is no observable sensitivity in preferential illumination as layered net-to-gross (NTG) changes within the isolated thin bed, regardless of the way the internal layering is distributed — either uniformly or semirandomly. The NTG signature is observed on the amplitude (magnitude) responses, rather than any specific frequency or phase component. On the other hand, external mutual thin-bed interference can significantly change the preferred phase component for each participating target. This phenomenon is largely driven by the embedded seismic wavelet that determines the nominal seismic response of an isolated thin layer and what phase component would preferentially illuminate it. For vertical separations between mutually interfering and elastically comparable thin beds in which mutual constructive interference is achieved, the target bed will be preferentially illuminated at a phase component that is very close to that of a total seismic isolation, whereas the occurrence of mutual destructive interference will cause a significant departure on the phase preferential illumination from that of an isolated seismic thin bed. All these observations can provide an avenue to yield more robust stratigraphic interpretations of seismic data and enhance the confidence on subsurface description.

Seismic-to-well tie of a field of the Nigerian Delta

2021 ◽  
Vol 19 (3) ◽  
pp. 125-138
Author(s):  
S. Inichinbia ◽  
A.L. Ahmed
Keyword(s):  
Seismic Data ◽  
Processing Parameters ◽  
Seismic Inversion ◽  
Seismic Wavelet ◽  
Statistical Measures ◽  
Convolution Model ◽  
Data Driven Approach ◽  
Well Data ◽  
Seismic Reflectors ◽  
Matching Techniques

This paper presents a rigorous but pragmatic and data driven approach to the science of making seismic-to-well ties. This pragmatic  approach is consistent with the interpreter’s desire to correlate geology to seismic information by the use of the convolution model,  together with least squares matching techniques and statistical measures of fit and accuracy to match the seismic data to the well data. Three wells available on the field provided a chance to estimate the wavelet (both in terms of shape and timing) directly from the seismic and also to ascertain the level of confidence that should be placed in the wavelet. The reflections were interpreted clearly as hard sand at H1000 and soft sand at H4000. A synthetic seismogram was constructed and matched to a real seismic trace and features from the well are correlated to the seismic data. The prime concept in constructing the synthetic is the convolution model, which represents a seismic reflection signal as a sequence of interfering reflection pulses of different amplitudes and polarity but all of the same shape. This pulse shape is the seismic wavelet which is formally, the reflection waveform returned by an isolated reflector of unit strength at the target  depth. The wavelets are near zero phase. The goal and the idea behind these seismic-to-well ties was to obtain information on the sediments, calibration of seismic processing parameters, correlation of formation tops and seismic reflectors, and the derivation of a  wavelet for seismic inversion among others. Three seismic-to-well ties were done using three partial angle stacks and basically two formation tops were correlated. Keywords: seismic, well logs, tie, synthetics, angle stacks, correlation,

Nonstationary local time-frequency transform

Geophysics ◽  
2021 ◽  
Vol 86 (3) ◽  
pp. V245-V254
Author(s):  
Yangkang Chen
Keyword(s):  
Local Time ◽  
Seismic Data ◽  
Low Frequency ◽  
Data Sets ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Data Registration ◽  
Frequency Decomposition ◽  
Temporal Smoothing ◽  
The Time Domain

Time-frequency analysis is a fundamental approach to many seismic problems. Time-frequency decomposition transforms input seismic data from the time domain to the time-frequency domain, offering a new dimension to probe the hidden information inside the data. Considering the nonstationary nature of seismic data, time-frequency spectra can be obtained by applying a local time-frequency transform (LTFT) method that matches the input data by fitting the Fourier basis with nonstationary Fourier coefficients in the shaping regularization framework. The key part of LTFT is the temporal smoother with a fixed smoothing radius that guarantees the stability of the nonstationary least-squares fitting. We have developed a new LTFT method to handle the nonstationarity in all time, frequency, and space ( x and y) directions of the input seismic data by extending fixed-radius temporal smoothing to nonstationary smoothing with a variable radius in all physical dimensions. The resulting time-frequency transform is referred to as the nonstationary LTFT method, which could significantly increase the resolution and antinoise ability of time-frequency transformation. There are two meanings of nonstationarity, i.e., coping with the nonstationarity in the data by LTFT and dealing with the nonstationarity in the model by nonstationary smoothing. We evaluate the performance of our nonstationary LTFT method in several standard seismic applications via synthetic and field data sets, e.g., arrival picking, quality factor estimation, low-frequency shadow detection, channel detection, and multicomponent data registration, and we benchmark the results with the traditional stationary LTFT method.

Adaptive surface‐related multiple elimination

Geophysics ◽  
1992 ◽  
Vol 57 (9) ◽  
pp. 1166-1177 ◽  
Author(s):  
D. J. Verschuur ◽  
A. J. Berkhout ◽  
C. P. A. Wapenaar
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Simulated Data ◽  
Seismic Inversion ◽  
The Other ◽  
Inversion Process ◽  
Elimination Process ◽  
Free Data ◽  
Surface Reflectivity ◽  
Multiple Elimination

The major amount of multiple energy in seismic data is related to the large reflectivity of the surface. A method is proposed for the elimination of all surface‐related multiples by means of a process that removes the influence of the surface reflectivity from the data. An important property of the proposed multiple elimination process is that no knowledge of the subsurface is required. On the other hand, the source signature and the surface reflectivity do need to be provided. As a consequence, the proposed process has been implemented adaptively, meaning that multiple elimination is designed as an inversion process where the source and surface reflectivity properties are estimated and where the multiple‐free data equals the inversion residue. Results on simulated data and field data show that the proposed multiple elimination process should be considered as one of the key inversion steps in stepwise seismic inversion.

Value of nonstationary wavelet spectral balancing in mapping a faulted fluvial system, Bohai Gulf, China

2015 ◽  
Vol 3 (3) ◽  
pp. SS1-SS13 ◽  
Author(s):  
Huailai Zhou ◽  
Yuanjun Wang ◽  
Tengfei Lin ◽  
Fangyu Li ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Spectral Bandwidth ◽  
Deconvolution Method ◽  
Fluvial System ◽  
Time Frequency ◽  
Seismic Wavelet ◽  
Enhanced Resolution ◽  
Coherence Energy ◽  
Text Filtering

Seismic data with enhanced resolution allow interpreters to effectively delineate and interpret architectural components of stratigraphically thin geologic features. We used a recently developed time-frequency domain deconvolution method to spectrally balance nonstationary seismic data. The method was based on polynomial fitting of seismic wavelet magnitude spectra. The deconvolution increased the spectral bandwidth but did not amplify random noise. We compared our new spectral modeling algorithm with existing time-variant spectral-whitening and inverse [Formula: see text]-filtering algorithms using a 3D offshore survey acquired over Bohai Gulf, China. We mapped these improvements spatially using a suite of 3D volumetric coherence, energy, curvature, and frequency attributes. The resulting images displayed improved lateral resolution of channel edges and fault edges with few, if any artifacts associated with amplification of random noise.

Probing the subsurface karst features using time-frequency decomposition

2016 ◽  
Vol 4 (4) ◽  
pp. T533-T542 ◽  
Author(s):  
Yangkang Chen
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Single Frequency ◽  
Frequency Interval ◽  
Data Set ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Inherent Frequency ◽  
Resolution Mapping ◽  
Resource Exploration

The high-resolution mapping of karst features is of great importance to hydrocarbon discovery and recovery in the resource exploration field. Currently, however, there are few effective methods specifically tailored for such a task. The 3D seismic data can reveal the existence of karsts to some extent, but a precise characterization cannot be obtained. I have developed an effective framework for accurately probing the subsurface karst features using a well-developed time-frequency decomposition algorithm. More specifically, I have introduced a frequency interval analysis approach for obtaining the best karsts detection result using an optimal frequency interval. A high-resolution time-frequency transform was preferred in the proposed framework to capture the inherent frequency components hidden behind the amplitude map. Although the single-frequency slice could not provide a reliable karst depiction result, the summation over the selected frequency interval could obtain a high-resolution and high-fidelity delineation of subsurface karsts. I used a publicly available 3D field seismic data set as an example to indicate the performance of the proposed method.

Application of sparse time-frequency decomposition to seismic data

2014 ◽  
Vol 11 (4) ◽  
pp. 447-458 ◽  
Author(s):  
Xiong-Wen Wang ◽  
Hua-Zhong Wang
Keyword(s):  
Seismic Data ◽  
Time Frequency ◽  
Frequency Decomposition
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