Seismic imaging of fault damaged zone and its scaling relation with displacement

2017 ◽  
Vol 5 (4) ◽  
pp. SP83-SP93 ◽  
Author(s):  
Behzad Alaei ◽  
Anita Torabi
Keyword(s):  
Seismic Data ◽  
Process Zone ◽  
Damage Zone ◽  
Frequency Resolution ◽  
Segment Length ◽  
Time Frequency ◽  
Zone Width ◽  
Damaged Zone ◽  
Fault Bend ◽  
Different Depths

We have studied seismically resolved damaged zone of normal faults in siliciclastic rocks of the Norwegian continental shelf. The workflow we have developed reveals structural details of the fault damaged zone and in particular, the subsidiary synthetic faults, horsetail at the main lateral fault tips at different depths and fault bend. These subsidiary or small fault segments form an area that can be clearly followed laterally and vertically. We call this area fault damaged zone. The studied damaged zone on seismic data comprises the fault core and the fault damage zone, as defined in outcrop studies. Spectral decomposition (short-time Fourier transform for time-frequency resolution and continuous wavelet transform) was performed on the data centered around faulted intervals. The magnitude of higher frequencies was used to generate coherence attribute volumes. Coherence attributes were filtered to enhance fault images. This integrated workflow improves fault images on reflection seismic data. Our approach reveals details of damaged zone geometry and morphology, which are comparable with the outcrop studies of similar examples conducted by previous researchers or us. We have extracted the fault geometry data including the segment length, displacement, and damaged zone width at different depths. Our results show that subsidiary faults, fault bends, linkage of fault segments, and branching in the fault tip (horsetail structure or process zone) all affect the width of the damaged zone and the distribution of displacement. We have seen a distinct increase in the fault damaged zone width near the fault bend locations. The fault segment length decreases with depth toward the lower fault tip, which is below the base Cretaceous unconformity. In addition, the displacement increases below the unconformity. In general, there is a positive correlation between fault displacement and the corresponding damaged zone width measured in this study, which is in agreement with previous studies.

scholarly journals Time-Frequency Domain Deconvolution based on Synchrosqueezing Generalized S Transform

2021 ◽  
pp. 148-155
Author(s):  
Shulin Zheng ◽  
Zijun Shen
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Oil And Gas ◽  
Amplitude Spectrum ◽  
Frequency Resolution ◽  
Good Effect ◽  
Time Frequency ◽  
S Transform ◽  
Convolution Model ◽  
Generalized S Transform

Complex geological characteristics and deepening of the mining depth are the difficulties of oil and gas exploration at this stage, so high-resolution processing of seismic data is needed to obtain more effective information. Starting from the time-frequency analysis method, we propose a time-frequency domain dynamic deconvolution based on the Synchrosqueezing generalized S transform (SSGST). Combined with spectrum simulation to estimate the wavelet amplitude spectrum, the dynamic convolution model is used to eliminate the influence of dynamic wavelet on seismic records, and the seismic signal with higher time-frequency resolution can be obtained. Through the verification of synthetic signals and actual signals, it is concluded that the time-frequency domain dynamic deconvolution based on the SSGST algorithm has a good effect in improving the resolution and vertical resolution of the thin layer of seismic data.

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

2021 ◽  
pp. 1-81
Author(s):  
Xiaokai Wang ◽  
Zhizhou Huo ◽  
Dawei Liu ◽  
Weiwei Xu ◽  
Wenchao Chen
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Seismic Data ◽  
Random Noise ◽  
Principal Component ◽  
Frequency Resolution ◽  
Reflection Point ◽  
Transform Method ◽  
Time Frequency ◽  
The Time Domain

Common-reflection-point (CRP) gather is one extensive-used prestack seismic data type. However, CRP suffers more noise than poststack seismic dataset. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain but also in the time-frequency domain. Therefore, we firstly use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the time-frequency domain, as the SSWT has better time-frequency resolution and reconstruction properties. Then we propose to use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the time-frequency domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples show that our proposed method can effectively attenuate random noise with a better attenuation performance than the commonly-used principal component analysis, FX filter, and the continuous wavelet transform method.

Fault geometry and architecture, an integrated study

2021 ◽  
Author(s):  
Anita Torabi ◽  
Behzad Alaei ◽  
Audun Libak
Keyword(s):  
Seismic Data ◽  
Co2 Storage ◽  
Damage Zone ◽  
Petroleum Exploration ◽  
Fault Geometry ◽  
Deformation Bands ◽  
Research Areas ◽  
Slip Surfaces ◽  
Damaged Zone ◽  
Fault Damage Zone

<p>Understanding fault geometry and processes of faulting are important research areas for many applications such as petroleum exploration and production; geothermal energy managements; hydrogeology; waste disposal and CO2 storage underground; earthquake seismology and geological hazard studies. Faults can be described as comprising a core and an enveloping damage zone (e.g. Caine et al. 1996).  The fault core accommodates most of the displacement along multiple slip surfaces and may include fault rocks such as fault gouge, cataclasites, breccia, clay smear, fractures, diagenetic features, and lenses of deformed and undeformed rocks trapped between slip surfaces. Whereas, the deformation is less intense in the damage zone and may include fractures and/or deformation bands depending on the initial porosity of the host rock, minor faults, and folds (Torabi et al., 2020). Fault geometric attributes include fault shape, fault displacement, length, damage zone width and fault core thickness (Caine et al., 1996; Torabi and Berg, 2011). Currently, there are uncertainties in defining and understanding of fault 3D geometry. These uncertainties are to some extent related to the accessibility of the fault geometric attributes and the methodological constraints, utilizing biased data. Details of fault damage zone and fault core structures can be mapped at outcrop, however, their descriptions and statistical handling are usually constrained by their accessibility in the field and their definitions by individual researchers.</p><p>Reflection seismic data is used to study faults in the subsurface, although the interpretation of faults could be affected by the seismic resolution and the accuracy of interpretation (Marchal et al., 2003; Lohr et al., 2008; Iacopini et al., 2016; Torabi et al., 2016). Utilizing seismic attributes, we are able to directly images faults from seismic without a need for interpretation. Using this method, we extracted fault geometric attributes directly from fault images in the fault attribute volumes and studied the 3D shape and displacement distribution of faults (Torabi et al., 2019). By integrating spectral decomposition with seismic attribute workflows, we created enhanced fault attribute volumes with a high resolution, allowing us to detect, and map fault damaged zone (fault damage zone plus fault core in outcrop scale) in seismic data (Alaei and Torabi, 2017). Finally, we integrated the data from outcrop and seismic study in the scaling relations between the faults geometric attributes in order to predict the fault geometry in the subsurface.</p><p> </p><p> </p>

Q estimation and its application in the 3D shallow weathering zone

2017 ◽  
Vol 5 (1) ◽  
pp. SC29-SC38 ◽  
Author(s):  
Ying Liu ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Zhikai Wang ◽  
Yiran Xu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Empirical Formula ◽  
Synthetic Data ◽  
Spectral Ratio ◽  
Frequency Resolution ◽  
Deep Part ◽  
Time Frequency ◽  
Generalized S Transform ◽  
Velocity Information ◽  
Weathering Zone

Attenuation in the shallow weathering zone is relatively strong, causing severe energy loss during wave propagation. It is difficult to estimate accurate [Formula: see text] values in the shallow weathering zone, and the influence of shallow weathering zone is seldom considered into attenuation estimation and compensation in the deep part. We achieved [Formula: see text] value estimation where there exist microlog data in the shallow weathering zone using the generalized S transform (GST); then, we establish an empirical formula using the velocity and [Formula: see text] value estimated with microlog data; finally, the [Formula: see text] value in the 3D shallow weathering zone can be obtained using the established formula and the velocity information. During the first procedure, the GST is used to provide reasonable time-frequency resolution, and linear regression is used in the obtained logarithmic spectral ratio to get the estimated [Formula: see text] value. An empirical formula is established using the estimated [Formula: see text] value and the velocity where there exists microlog data in the second procedure. In the third step, [Formula: see text] estimation in the whole shallow weathering zone can be obtained using the established formula and the velocity information, which can overcome the inaccuracy of spatial interpolation with the estimated [Formula: see text] factors where there exist different twin-well microlog data. Attenuation compensation to seismic data obtained from the deep part is carried out to prove the effectiveness of the estimated [Formula: see text] in the shallow weathering zone. After compensation, the resolution of seismic data is effectively increased, which demonstrates the validity of the estimated [Formula: see text] values in the shallow weathering zone. Synthetic data and field data examples demonstrate the validity of our method.

scholarly journals Seismic Reflection Coefficient Inversion Using Basis Pursuit Denoising in the Joint Time-Frequency Domain

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5025
Author(s):  
Xuegong Zhao ◽  
Hao Wu ◽  
Xinyan Li ◽  
Zhenming Peng ◽  
Yalin Li
Keyword(s):  
High Resolution ◽  
Reflection Coefficient ◽  
Objective Function ◽  
Frequency Domain ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Seismic Inversion ◽  
Frequency Resolution ◽  
Basis Pursuit ◽  
Time Frequency

Seismic reflection coefficient inversion in the joint time-frequency domain is a method for inverting reflection coefficients using time domain and frequency domain information simultaneously. It can effectively improve the time-frequency resolution of seismic data. However, existing research lacks an analysis of the factors that affect the resolution of inversion results. In this paper, we analyze the influence of parameters, such as the length of the time window, the size of the sliding step, the dominant frequency band, and the regularization factor of the objective function on inversion results. The SPGL1 algorithm for basis pursuit denoising was used to solve our proposed objective function. The applied geological model and experimental field results show that our method can obtain a high-resolution seismic reflection coefficient section, thus providing a potential avenue for high-resolution seismic data processing and seismic inversion, especially for thin reservoir inversion and prediction.

scholarly journals Frequency bandwidth enhancement using continuous wavelet transform and random noise attenuation by f-xy filtering technique – a case study from Badin oilfield, Indus basin, Pakistan

2019 ◽  
Vol 133 ◽  
pp. 01007
Author(s):  
Asad Taimur ◽  
Akinniyi Akinsunmade ◽  
Sylwia Tomecka-Suchon ◽  
Fahad Mehmood
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Random Noise ◽  
Resolving Power ◽  
Indus Basin ◽  
Frequency Resolution ◽  
Continuous Wavelet ◽  
Bandwidth Extension ◽  
Time Frequency

Routine seismic data processing does not always meet the quantitative interpreters’ expectations especially in areas like Badin, where prospective thin bed B – sand interval is ambiguous throughout the seismic volume. Continuous Wavelet Transform (CWT) provides detailed description of seismic signal in both time and frequency without compromising on window length and a fixed time-frequency resolution over time-frequency spectrum. We present enhancement of seismic data for effective interpretation using the bandwidth extension technique. Implementing bandwidth extension, the dominant frequency increases from 18 Hz to 30 Hz and the frequency content boosted from 40 Hz to 60 Hz. Noise inclusion by the technique was suppressed by F-XY predictive filter and F-XY deconvolution with edge preserve smoothing. Phase and spectral balancing were applied to partial angle stacks to stabilize the phase rotation across the 3D survey, particularly for far offset stack. Frequency was balanced using surface consistent spectrum balancing, and subjected to trace scaling for amplitudes balance and preservation. Results of the techniques yielded unique improvement on the data resolution and subtle information about the thin sand beds were better delineated. Tuning thickness analysis reveals the usefulness of bandwidth extension, with an increase of 30% in the resolving power of thin beds.

High-resolution characterization of geologic structures using the synchrosqueezing transform

2017 ◽  
Vol 5 (1) ◽  
pp. T75-T85 ◽  
Author(s):  
Naihao Liu ◽  
Jinghuai Gao ◽  
Zhuosheng Zhang ◽  
Xiudi Jiang ◽  
Qi Lv
Keyword(s):  
High Resolution ◽  
Frequency Analysis ◽  
Seismic Data ◽  
Proper Time ◽  
Frequency Resolution ◽  
Heisenberg Uncertainty Principle ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Basic Wavelet ◽  
Synchrosqueezing Transform

The main factors responsible for the nonstationarity of seismic signals are the nonstationarity of the geologic structural sequences and the complex pore structure. Time-frequency analysis can identify various frequency components of seismic data and reveal their time-variant features. Choosing a proper time-frequency decomposition algorithm is the key to analyze these nonstationarity signals and reveal the geologic information contained in the seismic data. According to the Heisenberg uncertainty principle, we cannot obtain the finest time location and the best frequency resolution at the same time, which results in the trade-off between the time resolution and the frequency resolution. For instance, the most commonly used approach is the short-time Fourier transform, in which the predefined window length limits the flexibility to adjust the temporal and spectral resolution at the same time. The continuous wavelet transform (CWT) produces an “adjustable” resolution of time-frequency map using dilation and translation of a basic wavelet. However, the CWT has limitations in dealing with fast varying instantaneous frequencies. The synchrosqueezing transform (SST) can improve the quality and readability of the time-frequency representation. We have developed a high-resolution and effective time-frequency analysis method to characterize geologic bodies contained in the seismic data. We named this method the SST, and the basic wavelet is the three-parameter wavelet (SST-TPW). The TPW is superior in time-frequency resolution than those of the Morlet and Ricker wavelets. Experiments on synthetic and field data determined its validity and effectiveness, which can be used in assisting in oil/gas reservoir identification.

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

scholarly journals Vehicle speed measurement by on-board acoustic signal processing

2018 ◽  
Vol 51 (5-6) ◽  
pp. 138-149 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Relative Motion ◽  
Wavelet Packet ◽  
Absolute Error ◽  
Frequency Resolution ◽  
Vehicle Speed ◽  
Air Conditioner ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Speed Sensor ◽  
Frequency Domain Methods

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

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