Reflection intensity waveform inversion

Yike Liu; Bin He; Zhendong Zhang; Yingcai Zheng; Peng Li

doi:10.1190/geo2019-0590.1

Reflection intensity waveform inversion

Geophysics ◽

10.1190/geo2019-0590.1 ◽

2020 ◽

Vol 85 (3) ◽

pp. R263-R273 ◽

Cited By ~ 1

Author(s):

Yike Liu ◽

Bin He ◽

Zhendong Zhang ◽

Yingcai Zheng ◽

Peng Li

Keyword(s):

Frequency Band ◽

Field Data ◽

Waveform Inversion ◽

Low Frequency ◽

Velocity Model ◽

Inversion Method ◽

Frequency Content ◽

Frequency Information ◽

Long Offset ◽

Reflection Intensity

Traditional iteration-based full-waveform inversion (FWI) methods encounter serious challenges if the initial velocity model is far from the true model or if the observed data are lacking low-frequency content. As such, the optimization algorithm may be trapped in local minima and fail to go to a global optimal model. In addition, the traditional FWI method requires long-offset data to update the deep structure of a velocity model with diving waves. To overcome the disadvantages of traditional FWI under these circumstances, we have developed a reflection intensity waveform inversion method. This method aims to minimize the seismic intensity differences between the modeled reflection data and field data. Our method is less dependent on the starting model, and long-offset data are no longer required. The wave intensity, proportional to the square of the original data amplitude, can have a low-frequency band and a higher frequency band, even for waveforms without initial low-frequency content. Our multiscale intensity inversion starts from the low-frequency information in the intensity data, and it can largely avoid the cycle-skipping problem. Synthetic and field data examples demonstrate that our method is able to overcome cycle skipping in handling data with no low-frequency information.

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Laplace-domain full-waveform inversion of seismic data lacking low-frequency information

Geophysics ◽

10.1190/geo2011-0411.1 ◽

2012 ◽

Vol 77 (5) ◽

pp. R199-R206 ◽

Cited By ~ 31

Author(s):

Wansoo Ha ◽

Changsoo Shin

Keyword(s):

Seismic Data ◽

Field Data ◽

Waveform Inversion ◽

Low Frequency ◽

Single Frequency ◽

Frequency Content ◽

Laplace Domain ◽

Frequency Information ◽

Velocity Models ◽

Gaussian Source

The lack of the low-frequency information in field data prohibits the time- or frequency-domain waveform inversions from recovering large-scale background velocity models. On the other hand, Laplace-domain waveform inversion is less sensitive to the lack of the low frequencies than conventional inversions. In theory, frequency filtering of the seismic signal in the time domain is equivalent to a constant multiplication of the wavefield in the Laplace domain. Because the constant can be retrieved using the source estimation process, the frequency content of the seismic data does not affect the gradient direction of the Laplace-domain waveform inversion. We obtained inversion results of the frequency-filtered field data acquired in the Gulf of Mexico and two synthetic data sets obtained using a first-derivative Gaussian source wavelet and a single-frequency causal sine function. They demonstrated that Laplace-domain inversion yielded consistent results regardless of the frequency content within the seismic data.

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Elastic envelope inversion using multicomponent seismic data without low frequency

Nonlinear Processes in Geophysics Discussions ◽

10.5194/npgd-1-1757-2014 ◽

2014 ◽

Vol 1 (2) ◽

pp. 1757-1802

Author(s):

C. Huang ◽

L. Dong ◽

Y. Liu ◽

B. Chi

Keyword(s):

Seismic Data ◽

Waveform Inversion ◽

Synthetic Data ◽

Low Frequency ◽

Velocity Model ◽

Inversion Method ◽

Full Waveform Inversion ◽

S Wave ◽

Full Waveform ◽

Multicomponent Data

Abstract. Low frequency is a key issue to reduce the nonlinearity of elastic full waveform inversion. Hence, the lack of low frequency in recorded seismic data is one of the most challenging problems in elastic full waveform inversion. Theoretical derivations and numerical analysis are presented in this paper to show that envelope operator can retrieve strong low frequency modulation signal demodulated in multicomponent data, no matter what the frequency bands of the data is. With the benefit of such low frequency information, we use elastic envelope of multicomponent data to construct the objective function and present an elastic envelope inversion method to recover the long-wavelength components of the subsurface model, especially for the S-wave velocity model. Numerical tests using synthetic data for the Marmousi-II model prove the effectiveness of the proposed elastic envelope inversion method, especially when low frequency is missing in multicomponent data and when initial model is far from the true model. The elastic envelope can reduce the nonlinearity of inversion and can provide an excellent starting model.

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Low-frequency, long-offset elastic waveform inversion in the context of velocity model building

The Leading Edge ◽

10.1190/tle40050342.1 ◽

2021 ◽

Vol 40 (5) ◽

pp. 342-347

Author(s):

René-Édouard Plessix ◽

Tadas Krupovnickas

Keyword(s):

Model Building ◽

Waveform Inversion ◽

Low Frequency ◽

Real Data ◽

Velocity Model ◽

Acoustic Wave Propagation ◽

Compressional Waves ◽

Traveltime Tomography ◽

Velocity Model Building ◽

Long Offset

Classic imaging approaches consist of splitting the earth into background and reflectivity models. When justified, this separation of scale is quite powerful, although this approach relies on some smoothness and weak contrast assumptions. This approach allows for the imaging methods to be based on acoustic wave propagation after having identified the compressional waves through picking or signal processing. Over the past years, wave-equation tomography and waveform inversion approaches have become routine, complementing the classic approaches to derive background models. They do not rely on high-frequency picks, unlike ray-based traveltime tomography, but on low-frequency cross-correlation to define time shifts and on waveform matching. In the presence of large earth parameter contrasts, time shifts and waveforms of compressional waves may depend on elastic parameters when interferences occur within the Fresnel zones. This challenges the recovery of the background model under an acoustic assumption with low-frequency data. Accounting for an elastic propagation in waveform inversion, even in the context of model building, could help to reduce the artifacts seen in acoustic results. A synthetic and a real data example are presented to illustrate the potential benefit of using an elastic waveform inversion approach when inverting long-offset, low-frequency seismic data.

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Velocity-model building with enhanced shallow resolution using elastic waveform inversion — An example from onshore Oman

Geophysics ◽

10.1190/geo2018-0736.1 ◽

2019 ◽

Vol 84 (6) ◽

pp. R977-R988 ◽

Cited By ~ 1

Author(s):

Carlos Pérez Solano ◽

René-Édouard Plessix

Keyword(s):

Elastic Property ◽

Model Building ◽

Waveform Inversion ◽

Low Frequency ◽

Velocity Model ◽

Good Resolution ◽

Frequency Data ◽

Wide Aperture ◽

Velocity Model Building ◽

Long Offset

Full-waveform inversion is a powerful data-fitting technique that is used for velocity-model building in seismic exploration. The inversion approach exploits the sensitivity of long-offset, wide-aperture, low-frequency data to the P-wave velocity properties in the subsurface. In the geologically complex land context in which different lithologies interleave and create large elastic property contrasts, acoustic waveform inversion is challenged due to the elastic nature of the data. The large elastic property contrasts create mode conversions. At low-to-intermediate frequencies, due to tuning/interference effects, the changes in the amplitudes of the different events affect amplitude and phase of the waveforms. We found that elastic waveform inversion of the long-offset, wide-aperture, low-frequency data leads to better retrieval of the compressional velocity model than the acoustic inversion and it is more stable. To obtain a good resolution in the shallow part of the model in an efficient manner, we have developed a two-stage inversion workflow that combines offset and frequency continuation. We have evaluated the relevance of this workflow with a challenging data set from South Oman.

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Volume source-based extended waveform inversion

Geophysics ◽

10.1190/geo2017-0330.1 ◽

2018 ◽

Vol 83 (5) ◽

pp. R369-R387 ◽

Cited By ~ 9

Author(s):

Guanghui Huang ◽

Rami Nammour ◽

William W. Symes

Keyword(s):

Penalty Function ◽

Source Parameters ◽

Waveform Inversion ◽

Low Frequency ◽

Model Space ◽

Velocity Model ◽

Inversion Method ◽

Inversion Algorithm ◽

Full Waveform ◽

Data Frequency

Full-waveform inversion (FWI) faces the persistent challenge of cycle skipping, which can result in stagnation of the iterative methods at uninformative models with poor data fit. Extended reformulations of FWI avoid cycle skipping through adding auxiliary parameters to the model so that a good data fit can be maintained throughout the inversion process. The volume-based matched source waveform inversion algorithm introduces source parameters by relaxing the location constraint of source energy: It is permitted to spread in space, while being strictly localized at time [Formula: see text]. The extent of source energy spread is penalized by weighting the source energy with distance from the survey source location. For transmission data geometry (crosswell, diving wave, etc.) and transparent (nonreflecting) acoustic models, this penalty function is stable with respect to the data-frequency content, unlike the standard FWI objective. We conjecture that the penalty function is actually convex over much larger region in model space than is the FWI objective. Several synthetic examples support this conjecture and suggest that the theoretical limitation to pure transmission is not necessary: The inversion method can converge to a solution of the inverse problem in the absence of low-frequency data from an inaccurate initial velocity model even when reflections and refractions are present in the data along with transmitted energy.

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Application of full waveform inversion algorithms to seismic data lacking low-frequency information from a simple starting model

Exploration Geophysics ◽

10.1071/eg17007 ◽

2018 ◽

Vol 49 (4) ◽

pp. 434-449 ◽

Cited By ~ 3

Author(s):

Hyunggu Jun ◽

Jungkyun Shin ◽

Changsoo Shin

Keyword(s):

Seismic Data ◽

Waveform Inversion ◽

Low Frequency ◽

Full Waveform Inversion ◽

Frequency Information ◽

Full Waveform ◽

Starting Model

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Traveltime information-based wave-equation inversion

Geophysics ◽

10.1190/1.3243073 ◽

2009 ◽

Vol 74 (6) ◽

pp. WCC27-WCC36 ◽

Cited By ~ 30

Author(s):

Yu Zhang ◽

Daoliu Wang

Keyword(s):

Wave Equation ◽

Conventional Method ◽

Seismic Data ◽

Waveform Inversion ◽

Back Propagation ◽

Velocity Model ◽

Data Fitting ◽

Inversion Method ◽

New Wave ◽

Seismic Record

We propose a new wave-equation inversion method that mainly depends on the traveltime information of the recorded seismic data. Unlike the conventional method, we first apply a [Formula: see text] transform to the seismic data to form the delayed-shot seismic record, back propagate the transformed data, and then invert the velocity model by maximizing the wavefield energy around the shooting time at the source locations. Data fitting is not enforced during the inversion, so the optimized velocity model is obtained by best focusing the source energy after a back propagation. Therefore, inversion accuracy depends only on the traveltime information embedded in the seismic data. This method may overcome some practical issues of waveform inversion; in particular, it relaxes the dependency of the seismic data amplitudes and the source wavelet.

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Source-independent extended waveform inversion based on space-time source extension: Frequency-domain implementation

Geophysics ◽

10.1190/geo2017-0333.1 ◽

2018 ◽

Vol 83 (5) ◽

pp. R449-R461 ◽

Cited By ~ 5

Author(s):

Guanghui Huang ◽

Rami Nammour ◽

William W. Symes

Keyword(s):

Source Function ◽

Random Noise ◽

A Priori ◽

Waveform Inversion ◽

Low Frequency ◽

Inversion Method ◽

Target Velocity ◽

Extended Source ◽

Full Waveform ◽

Time Variables

Source signature estimation from seismic data is a crucial ingredient for successful application of seismic migration and full-waveform inversion (FWI). If the starting velocity deviates from the target velocity, FWI method with on-the-fly source estimation may fail due to the cycle-skipping problem. We have developed a source-based extended waveform inversion method, by introducing additional parameters in the source function, to solve the FWI problem without the source signature as a priori. Specifically, we allow the point source function to be dependent on spatial and time variables. In this way, we can easily construct an extended source function to fit the recorded data by solving a source matching subproblem; hence, it is less prone to cycle skipping. A novel source focusing annihilator, defined as the distance function from the real source position, is used for penalizing the defocused energy in the extended source function. A close data fit avoiding the cycle-skipping problem effectively makes the new method less likely to suffer from local minima, which does not require extreme low-frequency signals in the data. Numerical experiments confirm that our method can mitigate cycle skipping in FWI and is robust against random noise.

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