TIME-VARYING SEISMIC WAVELET ESTIMATION FROM NONSTATIONARY SEISMIC DATA

Depth-domain seismic wavelet estimation is the essential foundation for depth-imaged data inversion, which is increasingly used for hydrocarbon reservoir characterization in geophysical prospecting. The seismic wavelet in the depth domain stretches with the medium velocity increase and compresses with the medium velocity decrease. The commonly used convolution model cannot be directly used to estimate depth-domain seismic wavelets due to velocity-dependent wavelet variations. We develop a separate parameter estimation method for estimating depth-domain seismic wavelets from poststack depth-domain seismic and well log data. This method is based on the velocity substitution and depth-domain generalized seismic wavelet model defined by the fractional derivative and reference wavenumber. Velocity substitution allows wavelet estimation with the convolution model in the constant-velocity depth domain. The depth-domain generalized seismic wavelet model allows for a simple workflow that estimates the depth-domain wavelet by estimating two wavelet model parameters. Additionally, this simple workflow does not need to perform searches for the optimal regularization parameter and wavelet length, which are time-consuming in least-squares-based methods. The limited numerical search ranges of the two wavelet model parameters can easily be calculated using the constant phase and peak wavenumber of the depth-domain seismic data. Our method is verified using synthetic and real seismic data and further compared with least-squares-based methods. The results indicate that the proposed method is effective and stable even for data with a low S/N.

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A nonstationary sparse spike deconvolution with anelastic attenuation

Geophysics ◽

10.1190/geo2017-0846.1 ◽

2019 ◽

Vol 84 (2) ◽

pp. R221-R234 ◽

Cited By ~ 6

Author(s):

Yuhan Sui ◽

Jianwei Ma

Keyword(s):

Seismic Data ◽

Sparse Matrix ◽

Estimation Error ◽

Parameter Setting ◽

Wavelet Estimation ◽

Seismic Wavelet ◽

Anelastic Attenuation ◽

Extended Approach ◽

Nonstationary Deconvolution ◽

Sparse Matrix Factorization

Seismic wavelet estimation and deconvolution are essential for high-resolution seismic processing. Because of the influence of absorption and scattering, the frequency and phase of the seismic wavelet change with time during wave propagation, leading to a time-varying seismic wavelet. To obtain reflectivity coefficients with more accurate relative amplitudes, we should compute a nonstationary deconvolution of this seismogram, which might be difficult to solve. We have extended sparse spike deconvolution via Toeplitz-sparse matrix factorization to a nonstationary sparse spike deconvolution approach with anelastic attenuation. We do this by separating our model into subproblems in each of which the wavelet estimation problem is solved by the classic sparse optimization algorithms. We find numerical examples that illustrate the parameter setting, noisy seismogram, and the estimation error of the [Formula: see text] value to validate the effectiveness of our extended approach. More importantly, taking advantage of the high accuracy of the estimated [Formula: see text] value, we obtain better performance than with the stationary Toeplitz-sparse spike deconvolution approach in real seismic data.

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Methods of spectral analysis of seismic data

Bulletin of the Seismological Society of America ◽

10.1785/bssa0540041213 ◽

1964 ◽

Vol 54 (4) ◽

pp. 1213-1232

Author(s):

I. K. McIvor

Keyword(s):

Spectral Analysis ◽

Fourier Analysis ◽

Seismic Data ◽

Seismic Event ◽

Time Varying

Abstract Three different methods of spectral analysis are compared on the basis of a common interpretation in terms of time-varying Fourier analysis. The spectra obtained by these methods for a particular seismic event are given and differences in the results are resolved.

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

Interpretation ◽

10.1190/int-2018-0048.1 ◽

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.

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Machine Learning analysis of seismic signals recorded at Stromboli Volcano

10.5194/egusphere-egu21-13166 ◽

2021 ◽

Author(s):

Darius Fenner ◽

Georg Rümpker ◽

Horst Stöcker ◽

Megha Chakraborty ◽

Wei Li ◽

...

Keyword(s):

Machine Learning ◽

Seismic Data ◽

Noise Level ◽

Volcanic Eruptions ◽

Time Varying ◽

Seismic Signals ◽

Machine Learning Model ◽

Automatic Adaptation ◽

Consistent Manner ◽

Recent Developments

At Stromboli, minor volcanic eruptions occur at time intervals of approximately five minutes on average, making it one of the most active volcanoes worldwide. In addition to these mostly harmless events, there are also stronger eruptions and paroxysms which pose a serious threat to residents and tourists. In light of recent developments in Machine Learning, this study attempts to apply these new tools for the analysis of the time-varying volcanic eruptions at Stromboli. As input for the Machine-Learning approach, we use continuous recordings of seismic signals from two seismometers on the island. The data is available from IRIS &#160;and includes records starting in 2012 up to the present.&#160;One primary challenge is to label and classify the data, i.e., to discriminate events of interest from noise. The variety of signal-appearance in the recorded data is wide, in some periods the events are clearly distinguishable from noise whereas, in other cases relevant events are obscured by the high noise level. To enable the event-detection in all cases, we developed the following algorithm: in the first step, the seismic data is pre-processed with an STA/LTA-Filter, which allows detection of events based on a prominence threshold. However, due to the diversity of signal patterns, a fixed set of hyperparameters (STA- and LTA-window length, prominence threshold, correlation coefficient) fails to reliably extract the relevant events in a consistent manner. Therefore, the (time-varying) noise level of the recordings is used as an additional key indicator. After this, the hyperparameters are optimized. The automatic adaptation is then used for labeling the continuous seismic data.After extracting the events based on this approach, a machine learning model is trained to analyze the recordings for possible patterns in the interval times and the event amplitudes. This study is expected to provide constraints on the possibility to detect complex time-dependent patterns of the eruption history at Stromboli.

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

Scientia Africana ◽

10.4314/sa.v19i3.9 ◽

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,

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