A union of spectrum and cepstrum: Recipe for thin-bed delineation

2019 ◽  
Vol 38 (4) ◽  
pp. 298-305
Author(s):  
Prashant Kumar Mishra ◽  
Sanjai Kumar Singh ◽  
Pradip Kumar Chaudhuri
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Dominant Frequency ◽  
Resolution Limit ◽  
Bandwidth Extension ◽  
Data Set ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Cepstral Analysis ◽  
Resultant Data

The resolution limit of seismic data is an intricate issue that depends not only on frequency and data quality (signal-to-noise ratio) but also on the tools and technology used to analyze seismic response. In this context, the subject of thin-bed delineation is extremely significant for coal-laminated (causing large acoustic impedance contrasts) clastic sequences of the Western Onshore Basin, India. Most of the clastic reservoirs in the area are of subseismic resolution (below 10 m in thickness) due to the low dominant frequency available in seismic data (19–35 Hz). This is where improving seismic resolution is essential for a detailed structural and stratigraphic interpretation. We have implemented a modified workflow with which, by using state-of-the-art techniques of time-frequency decomposition and cepstral analysis, significant seismic bandwidth extension has been achieved. This in turn yields improved vertical resolution of the seismic data with better geologic interpretability. Our approach is named the “syn-cepstral method” after its two integral constituents — synchrosqueezing transform and cepstral analysis. Applying the syn-cepstral method produces better well-to-seismic ties and resolves additional events in comparison to the original seismic data. The validity of syn-cepstral methodology has been demonstrated by 1D and 2D modeling studies followed by application to a 3D seismic data set from the Western Onshore Basin of India. The improvement in thin-bed delineation arising from the increased bandwidth of the resultant data has been validated by well-to-seismic ties and amplitude map interpretation. Thus, while thin clastic reservoir beds in the logs show no discernible presence in the original seismic data, upon application of the syn-cepstral method, the resultant seismic data show improved interpretability of these units.

Sparse spectral attributes to detect hydrocarbons: A case study from deep dolomite reservoirs

2019 ◽  
Vol 7 (3) ◽  
pp. T701-T711
Author(s):  
Jianhu Gao ◽  
Bingyang Liu ◽  
Shengjun Li ◽  
Hongqiu Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Spectral Amplitude ◽  
Data Set ◽  
Frequency Spectra ◽  
Hydrocarbon Production ◽  
Hydrocarbon Detection ◽  
Time Frequency ◽  
Gas Bearing

Hydrocarbon detection is always one of the most critical sections in geophysical exploration, which plays an important role in subsequent hydrocarbon production. However, due to the low signal-to-noise ratio and weak reflection amplitude of deep seismic data, some conventional methods do not always provide favorable hydrocarbon prediction results. The interesting dolomite reservoirs in Central Sichuan are buried over an average depth of 4500 m, and the dolomite rocks have a low porosity below approximately 4%, which is measured by well-logging data. Furthermore, the dominant system of pores and fractures as well as strong heterogeneity along the lateral and vertical directions lead to some difficulties in describing the reservoir distribution. Spectral decomposition (SD) has become successful in illuminating subsurface features and can also be used to identify potential hydrocarbon reservoirs by detecting low-frequency shadows. However, the current applications for hydrocarbon detection always suffer from low resolution for thin reservoirs, probably due to the influence of the window function and without a prior constraint. To address this issue, we developed sparse inverse SD (SISD) based on the wavelet transform, which involves a sparse constraint of time-frequency spectra. We focus on investigating the applications of sparse spectral attributes derived from SISD to deep marine dolomite hydrocarbon detection from a 3D real seismic data set with an area of approximately [Formula: see text]. We predict and evaluate gas-bearing zones in two target reservoir segments by analyzing and comparing the spectral amplitude responses of relatively high- and low-frequency components. The predicted results indicate that most favorable gas-bearing areas are located near the northeast fault zone in the upper reservoir segment and at the relatively high structural positions in the lower reservoir segment, which are in good agreement with the gas-testing results of three wells in the study area.

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.

Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. V229-V237 ◽  
Author(s):  
Hongbo Lin ◽  
Yue Li ◽  
Baojun Yang ◽  
Haitao Ma
Keyword(s):  
Weight Function ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Signal To Noise ◽  
Transform Method ◽  
Time Frequency ◽  
Frequency Peak ◽  
Noise Ratio

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

Application of sparse-layer inversion and harmonic bandwidth extension for a channel system in Southern Alberta, Canada

2020 ◽  
Vol 8 (2) ◽  
pp. T217-T229
Author(s):  
Yang Mu ◽  
John Castagna ◽  
Gabriel Gil
Keyword(s):  
Seismic Data ◽  
Reflection Coefficients ◽  
3D Seismic ◽  
Bandwidth Extension ◽  
Data Set ◽  
Frequency Spectra ◽  
Channel System ◽  
Southern Alberta ◽  
Reflectivity Inversion ◽  
3D Seismic Data

Sparse-layer reflectivity inversion decomposes a seismic trace into a limited number of simple layer responses and their corresponding reflection coefficients for top and base reflections. In contrast to sparse-spike inversion, the applied sparsity constraint is less biased against layer thickness and can thus better resolve thin subtuning layers. Application to a 3D seismic data set in Southern Alberta produces inverted impedances that have better temporal resolution and lateral stability and a less blocky appearance than sparse-spike inversion. Bandwidth extension harmonically extrapolated the frequency spectra of the inverted layers and nearly doubled the usable bandwidth. Although the prospective glauconitic sand tunes at approximately 37 m, bandwidth extension reduced the tuning thickness to 22 m. Bandwidth-extended data indicate a higher correlation with synthetic traces than the original seismic data and reveal features below the original tuning thickness. After bandwidth extension, the channel top and base are more evident on inline and crossline profiles. Lateral facies changes interpreted from the inverted acoustic impedance of the bandwidth-extended data are consistent with observations in wells.

Suppression of high‐energy noise using an alternative stacking procedure

Geophysics ◽  
1989 ◽  
Vol 54 (2) ◽  
pp. 181-190 ◽  
Author(s):  
Jakob B. U. Haldorsen ◽  
Paul A. Farmer
Keyword(s):  
Seismic Data ◽  
Traditional Approach ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
High Amplitude ◽  
High Energy ◽  
Data Set ◽  
Trimmed Mean ◽  
Fold Reduction ◽  
Processing Techniques

Occasionally, seismic data contain transient noise that can range from being a nuisance to becoming intolerable when several seismic vessels try simultaneously to collect data in an area. The traditional approach to solving this problem has been to allocate time slots to the different acquisition crews; the procedure, although effective, is very expensive. In this paper a statistical method called “trimmed mean stack” is evaluated as a tool for reducing the detrimental effects of noise from interfering seismic crews. Synthetic data, as well as field data, are used to illustrate the efficacy of the technique. Although a conventional stack gives a marginally better signal‐to‐noise ratio (S/N) for data without interference noise, typical usage of the trimmed mean stack gives a reduced S/N equivalent to a fold reduction of about 1 or 2 percent. On the other hand, for a data set containing high‐energy transient noise, trimming produces stacked sections without visible high‐amplitude contaminating energy. Equivalent sections produced with conventional processing techniques would be totally unacceptable. The application of a trimming procedure could mean a significant reduction in the costs of data acquisition by allowing several seismic crews to work simultaneously.

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.

Extended stable factor method for the inverse Q-filter

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. T155-T163
Author(s):  
Yong Li ◽  
Gulan Zhang ◽  
Jing Duan ◽  
Chengjie He ◽  
Hao Du ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Time Frequency ◽  
Stable Factor ◽  
Factor Method ◽  
Data Application ◽  
Two Parameters ◽  
Limit Assumption

The commonly used stable factor methods for the inverse [Formula: see text]-filter achieve good performance in seismic data processing; however, the constant gain-limit assumption in these methods is not associated with the effective frequency band of seismic data and cannot obtain desirable results with high resolution and high signal-to-noise ratio (S/N). Our extended stable factor method for the inverse [Formula: see text]-filter extends these methods by introducing two parameters and constant or self-adaptive gain limit to achieve the desirable high-resolution and high-S/N result. The extended stable factor method for the inverse [Formula: see text]-filter can be implemented in the frequency or time-frequency domain; the latter implementation achieves a higher S/N. Analysis of synthetic signals and field seismic data application illustrate that our method can produce a desirable high-resolution and high-S/N result.

Vertical resolution of a seismic survey in stratigraphic sequences less than 100 m deep in southeastern Kansas

Geophysics ◽  
1995 ◽  
Vol 60 (2) ◽  
pp. 423-430 ◽  
Author(s):  
Richard D. Miller ◽  
Neil L. Anderson ◽  
Howard R. Feldman ◽  
Evan K. Franseen
Keyword(s):  
Seismic Data ◽  
Fresnel Zone ◽  
Drill Hole ◽  
Seismic Profile ◽  
Dominant Frequency ◽  
Seismic Survey ◽  
Vertical Resolution ◽  
Resolution Limit ◽  
Reflection Seismic ◽  
Quarter Wavelength

A 400-m long, 12‐fold high‐resolution common depth point (CDP) reflection seismic profile was acquired across shallow converging Pennsylvanian strata in the Independence area of southeastern Kansas. One of the principal objectives was to determine practical vertical resolution limits in an excellent shallow seismic‐data area with borehole control. The dominant frequency of the CDP stacked data is in excess of 150 Hz based on peak‐to‐peak measurements. Interference phenomena observed on stacked seismic data incorporated with models derived from log and drill‐hole information suggest a practical vertical resolution limit of about 7 m, or one‐third of the dominant wavelength. This practical resolution is slightly less than the predicted (theoretical) resolution limit of 5 m based on the generally accepted one‐quarter wavelength axiom. These data suggest conventional rules of thumb describing resolution potential are not accurate when reflectors on shallow, narrow bandwidth data converge rapidly across horizontal distances less than the Fresnel Zone.

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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