Time Frequency Wavenumber Analysis of Surface Waves and Signal Enhancement Using S-Transform

2015 ◽  
Vol 23 (04) ◽  
pp. 1540010
Author(s):  
Zhao Zhang ◽  
Yuefeng Sun ◽  
Karl Berteussen
Keyword(s):  
Surface Waves ◽  
Low Frequency ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Time Frequency ◽  
Reflection Data ◽  
Time Space ◽  
S Transform ◽  
Variable Factor ◽  
Over Time

Highly dispersive surface waves resulting from the combination of strong lateral seafloor heterogeneities, shallow water depths and hard sea bottom severely degrade seismic reflection data quality. Considering that seismic signals are nonstationary and surface waves have various spectra over time, we proposed S-transform-based time frequency wavenumber analysis technique which allows the dynamic analysis of spectrum over time. The data is first transformed from the time-space domain to the time-wavenumber domain through one-dimensional Fourier transform over the spatial variable, then the variable-factor S-transform is applied over time. Nonstationary filtering is then designed to identify and separate surface waves in complex wavefields, based on its low frequency and low velocity properties, in the time-frequency-wavenumber (TFK) domain. Application to field data illustrates that with this technique, not only is the surface wave effectively suppressed, but also the reflective signals are enhanced, which confirm the validity of the method.

Investigation of generalized S-transform analysis windows for time-frequency analysis of seismic reflection data

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. V235-V247 ◽  
Author(s):  
Duan Li ◽  
John Castagna ◽  
Gennady Goloshubin
Keyword(s):  
Temporal Resolution ◽  
Seismic Reflection ◽  
Frequency Resolution ◽  
Low Frequencies ◽  
Seismic Reflection Data ◽  
Time Frequency ◽  
Gaussian Window ◽  
Reflection Data ◽  
S Transform ◽  
Generalized S Transform

The frequency-dependent width of the Gaussian window function used in the S-transform may not be ideal for all applications. In particular, in seismic reflection prospecting, the temporal resolution of the resulting S-transform time-frequency spectrum at low frequencies may not be sufficient for certain seismic interpretation purposes. A simple parameterization of the generalized S-transform overcomes the drawback of poor temporal resolution at low frequencies inherent in the S-transform, at the necessary expense of reduced frequency resolution. This is accomplished by replacing the frequency variable in the Gaussian window with a linear function containing two coefficients that control resolution variation with frequency. The linear coefficients can be directly calculated by selecting desired temporal resolution at two frequencies. The resulting transform conserves energy and is readily invertible by an inverse Fourier transform. This modification of the S-transform, when applied to synthetic and real seismic data, exhibits improved temporal resolution relative to the S-transform and improved resolution control as compared with other generalized S-transform window functions.

scholarly journals Application of S-Transform in ISAR Imaging

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 676 ◽  
Author(s):  
Bo Zang ◽  
Mingzhe Zhu ◽  
Xianda Zhou ◽  
Lu Zhong
Keyword(s):  
Frequency Analysis ◽  
Low Frequency ◽  
Frequency Region ◽  
Echo Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Isar Imaging ◽  
Body Components ◽  
Over Time

In inverse synthetic aperture radar (ISAR) imaging, time-frequency analysis is the basic method for processing echo signals, which are reflected by the results of time-frequency analysis as each component changes over time. In the time-frequency map, a target’s rigid body components will appear as a series of single-frequency signals in the low-frequency region, and the micro-Doppler components generated by the target’s moving parts will be distributed in the high-frequency region with obvious frequency modulation. Among various time-frequency analysis methods, S-transform is especially suitable for analyzing these radar echo signals with micro-Doppler (m-D) components because of its multiresolution characteristics. In this paper, S-transform and the corresponding synchrosqueezing method are used to analyze the ISAR echo signal and perform imaging. Synchrosqueezing is a post-processing method for the time-frequency analysis result, which could retain most merits of S-transform while significantly improving the readability of the S-transformation result. The results of various simulations and actual data will show that S-transform is highly matched with the echo signal for ISAR imaging: the better frequency-domain resolution at low frequencies can concentrate the energy of the rigid body components in the low-frequency region, and better time resolution at high frequencies can better describe the transformation of the m-D component over time. The combination with synchrosqueezing also significantly improves the effect of time-frequency analysis and final imaging, and alleviates the shortcomings of the original S-transform. These results will be able to play a role in subsequent work like feature extraction and parameter estimation.

Modeling and processing of scattered waves in seismic reflection surveys

Geophysics ◽  
1988 ◽  
Vol 53 (4) ◽  
pp. 466-478 ◽  
Author(s):  
Bruce S. Gibson ◽  
Alan R. Levander
Keyword(s):  
Surface Waves ◽  
Seismic Reflection ◽  
Wave Scattering ◽  
Body Wave ◽  
Simple Relation ◽  
Velocity Variation ◽  
Low Velocity ◽  
Spectral Character ◽  
Seismic Reflection Data ◽  
Reflection Data

Various wave‐scattering mechanisms are known to degrade reflection signals by producing noise in seismic reflection data. Synthetic 2-D acoustic‐wave finite‐ difference data sets illustrate the effects of two such mechanisms. Twenty‐five shot gathers were generated for each of two models and the data were processed as standard CMP surveys. In one model, an irregular low‐ velocity surface layer produced multiply scattered surface waves that appear as linear noise trains in common‐shot gathers and stacked sections. The scattering of upcoming reflections at the lower interface of the layer also produced a significant amount of noise. When predictive deconvolution was applied before stack to reduce reverberations, the spectral character of the scattered surface waves seriously inhibited the action of that process. In the second model, a zone of smooth, random velocity variation was imposed between two reflectors deeper in the model. The heterogeneous zone (±5 percent rms velocity variation) substantially degraded the signal reflected from below it; events produced by body‐wave scattering are characterized by higher phase velocities than those seen in the first model. Conventional CMP stacking produced discontinuous subhorizontal events from the disturbed zone. The limited bandwidth of the propagating signal and spatial filtering attributable to CMP stacking cause these events to bear no simple relation to the velocity anomalies of the model, even after migration.

An improved method to estimate Q based on the logarithmic spectrum of moving peak points

2019 ◽  
Vol 7 (2) ◽  
pp. T255-T263 ◽  
Author(s):  
Yanli Liu ◽  
Zhenchun Li ◽  
Guoquan Yang ◽  
Qiang Liu
Keyword(s):  
Seismic Waves ◽  
Wave Attenuation ◽  
Real Data ◽  
Peak Frequency ◽  
Seismic Reflection Data ◽  
The Real ◽  
Time Frequency ◽  
Reflection Data ◽  
Text Filtering ◽  
The Impact

The quality factor ([Formula: see text]) is an important parameter for measuring the attenuation of seismic waves. Reliable [Formula: see text] estimation and stable inverse [Formula: see text] filtering are expected to improve the resolution of seismic data and deep-layer energy. Many methods of estimating [Formula: see text] are based on an individual wavelet. However, it is difficult to extract the individual wavelet precisely from seismic reflection data. To avoid this problem, we have developed a method of directly estimating [Formula: see text] from reflection data. The core of the methodology is selecting the peak-frequency points to linear fit their logarithmic spectrum and time-frequency product. Then, we calculated [Formula: see text] according to the relationship between [Formula: see text] and the optimized slope. First, to get the peak frequency points at different times, we use the generalized S transform to produce the 2D high-precision time-frequency spectrum. According to the seismic wave attenuation mechanism, the logarithmic spectrum attenuates linearly with the product of frequency and time. Thus, the second step of the method is transforming a 2D spectrum into 1D by variable substitution. In the process of transformation, we only selected the peak frequency points to participate in the fitting process, which can reduce the impact of the interference on the spectrum. Third, we obtain the optimized slope by least-squares fitting. To demonstrate the reliability of our method, we applied it to a constant [Formula: see text] model and the real data of a work area. For the real data, we calculated the [Formula: see text] curve of the seismic trace near a well and we get the high-resolution section by using stable inverse [Formula: see text] filtering. The model and real data indicate that our method is effective and reliable for estimating the [Formula: see text] value.

STACKING OF REFLECTIONS FROM COMPLEX STRUCTURES

Geophysics ◽  
1974 ◽  
Vol 39 (4) ◽  
pp. 427-440 ◽  
Author(s):  
Max K. Miller
Keyword(s):  
Deep Structure ◽  
Control Model ◽  
Layer Model ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Near Surface ◽  
Static Correction ◽  
Reflection Data ◽  
Actual Field ◽  
Interval Velocities

Common‐depth‐point seismic reflection data were generated on a computer using simple ray tracing and analyzed with processing techniques currently used on actual field recordings. Constant velocity layers with curved interfaces were used to simulate complex geologic shapes. Two models were chosen to illustrate problems caused by curved geologic interfaces, i.e., interfaces at depths which vary laterally in a nonlinear fashion and produce large spatial variations in the apparent stacking velocity. A three‐layer model with a deep structure and no weathering was used as a control model. For comparison, a low velocity weathering layer also of variable thickness was inserted near the surface of the control model. The low velocity layer was thicker than the ordinary thin weathering layers where state‐of‐the‐art static correction methods work well. Traveltime, moveout, apparent rms velocities, and interval velocities were calculated for both models. The weathering introduces errors into the rms velocities and traveltimes. A method is described to compensate for these errors. A static correction applied to the traveltimes reduced the fluctuation of apparent rms velocities. Values for the thick weathering layer model were “over corrected” so that synclines (anticlines) replaced false anticlines (synclines) for both near‐surface and deep zones. It is concluded that computer modeling is a useful tool for analyzing specific problems of processing CDP seismic data such as errors in velocity estimates produced by large lateral variations in overburden.

Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study

2021 ◽  
Author(s):  
Siddharth Garia ◽  
Arnab Kumar Pal ◽  
Karangat Ravi ◽  
Archana M Nair
Keyword(s):  
Neural Network ◽  
Bulk Density ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Petrophysical Properties ◽  
Seismic Section ◽  
Frequency Model ◽  
Seismic Reflection Data ◽  
Reflection Data

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

Shallow seismic detection of the fault zone associated with a high scarp in southwestern Montana

2015 ◽  
Vol 3 (1) ◽  
pp. T25-T41
Author(s):  
Jose Pujol ◽  
Mervin J. Bartholomew ◽  
Andrew Mickelson ◽  
Michael Bone
Keyword(s):  
Surface Waves ◽  
Fault Zone ◽  
Synthetic Data ◽  
Low Frequency ◽  
Normal Fault ◽  
Actual Data ◽  
Southwestern Montana ◽  
Velocity Models ◽  
Reflection Data ◽  
High Pass

We collected shallow reflection data in southwestern Montana, USA, across a 5.4-m-high tectonic scarp. The goal was to image the normal fault associated with the scarp, observed in an adjacent trench. Processing of the data was challenging because the height of the scarp was comparable to the depths of the reflectors of interest. To find out how to proceed, we processed synthetic data generated using velocity models derived in part from actual shot gathers. The actual data are dominated by large-amplitude low-frequency surface waves, but clear high-frequency reflections are seen in the more distant geophones. Common-offset gathers for the raw and high-pass filtered data reveal sharp discontinuities in arrival times and a strong decrease in amplitudes, respectively, under the scarp. These changes in the wavefield are indicative of lateral variations in elastic properties and are consistent with the presence of a fault zone seen in the trench. The actual data were stacked after the surface waves were removed with a narrow f-k filter. Severe muting was applied to isolate the reflections seen in the high-pass filtered data. The stacked data reveal a clear and fairly continuous horizontal reflector on the downthrown side of the fault and more disrupted reflectors on the upthrown side, with truncated reflections and changes in amplitude roughly across the projection of the fault mapped in the trench. These observations are consistent with faulting and would be difficult to explain if the scarp were an erosional feature.

Low velocity layer characterization in the Niger Delta: Implications for seismic reflection data quality

2017 ◽  
Vol 90 (2) ◽  
pp. 187-195
Author(s):  
A. I. Opara ◽  
C. C. Agoha ◽  
C. N. Okereke ◽  
U. P. Adiela ◽  
C. N. Onwubuariri ◽  
...  
Keyword(s):  
Data Quality ◽  
Niger Delta ◽  
Seismic Reflection ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Low Velocity Layer ◽  
Velocity Layer

An example of extreme near-surface variability in shallow seismic reflection data

2016 ◽  
Vol 4 (3) ◽  
pp. SH1-SH9
Author(s):  
Steven D. Sloan ◽  
J. Tyler Schwenk ◽  
Robert H. Stevens
Keyword(s):  
Seismic Reflection ◽  
Field Data ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Near Surface ◽  
Shallow Subsurface ◽  
Reflection Data ◽  
Shallow Seismic ◽  
Low Velocity Layer ◽  
Velocity Layer

Variability of material properties in the shallow subsurface presents challenges for near-surface geophysical methods and exploration-scale applications. As the depth of investigation decreases, denser sampling is required, especially of the near offsets, to accurately characterize the shallow subsurface. We have developed a field data example using high-resolution shallow seismic reflection data to demonstrate how quickly near-surface properties can change over short distances and the effects on field data and processed sections. The addition of a relatively thin, 20 cm thick, low-velocity layer can lead to masked reflections and an inability to map shallow reflectors. Short receiver intervals, on the order of 10 cm, were necessary to identify the cause of the diminished data quality and would have gone unknown using larger, more conventional station spacing. Combined analysis of first arrivals, surface waves, and reflections aided in determining the effects and extent of a low-velocity layer that inhibited the identification and constructive stacking of the reflection from a shallow water table using normal-moveout-based processing methods. Our results also highlight the benefits of using unprocessed gathers to pragmatically guide processing and interpretation of seismic data.

scholarly journals Interpretation of 3D Seismic Reflection Data to Reveal Stratigraphic Setting of the Reservoir of Mishrif Formation in Dujaila Oil Field, Southeast of Iraq

2021 ◽  
pp. 2250-2261
Author(s):  
Ahmed Muslim Khawaja ◽  
Jassim Muhammad Thabit
Keyword(s):  
Seismic Reflection ◽  
Seismic Analysis ◽  
Carbonate Platform ◽  
Low Frequency ◽  
Oil Field ◽  
3D Seismic ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Carbonate Buildups ◽  
Depositional Models

     This research is an attempt to solve the ambiguity associated with the stratigraphic setting of the main reservoir (late Cretaceous) of Mishrif Formation in Dujaila oil field. This was achieved by studying a 3D seismic reflection post-stack data for an area of ​​602.62 Km2 in Maysan Governorate, southeast of Iraq. Seismic analysis of the true amplitude reflections, time maps, and 3D depositional models showed a sufficient seismic evidence that the Mishrif Formation produces oil from a stratigraphic trap of isolated reef carbonate buildups that were grown on the shelf edge of the carbonate platform, located in the area around the productive well Dujaila-1. The low-frequency attribute illustrated that it is restricted in the area around the productive well Dujaila-1, which confirmed the existence of reef porous carbonate buildups and hydrocarbon accumulation in this region. The pay zone of the reef mound trap extends for about 7 km from the well Dujaila-1 toward the southwest side and 4 km toward the well Dujaila-2, without reaching it, which is explaining why it was dry. Therefore, this area to the south of the productive well Dujaila-1 represents a good area for low-risk drilling. Consequently, the hydrocarbon system observed in the Dujaila oil field provides a new opportunity to explore and produce oil in Mishrif Formation in other areas on the flank of the productive structures and in flat areas situated on the belt of the carbonate platform edge.

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