Optimal coding of blended seismic sources for 2D full waveform inversion in time

Full Waveform Inversion (FWI) schemes are gradually becoming more common in the oil and gas industry, as a new tool for studying complex geological zones, based on their reliability for estimating velocity models. FWI is a non-linear inversion method that iteratively estimates subsurface characteristics such as seismic velocity, starting from an initial velocity model and the preconditioned data acquired. Blended sources have been used in marine seismic acquisitions to reduce acquisition costs, reducing the number of times that the vessel needs to cross the exploration delineation trajectory. When blended or simultaneous without previous de-blending or separation, stage data are used in the reconstruction of the velocity model with the FWI method, and the computational time is reduced. However, blended data implies overlapping single shot-gathers, producing interference that affects the result of seismic approaches, such as FWI or seismic image migration. In this document, an encoding strategy is developed, which reduces the overlap areas within the blended data to improve the final velocity model with the FWI method.

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Full-waveform inversion for full-wavefield imaging: Decades in the making

The Leading Edge ◽

10.1190/tle40050324.1 ◽

2021 ◽

Vol 40 (5) ◽

pp. 324-334

Author(s):

Rongxin Huang ◽

Zhigang Zhang ◽

Zedong Wu ◽

Zhiyuan Wei ◽

Jiawei Mei ◽

...

Keyword(s):

Model Building ◽

Seismic Imaging ◽

Structural Information ◽

Waveform Inversion ◽

Velocity Model ◽

Data Sets ◽

Full Waveform Inversion ◽

Data Types ◽

Velocity Models ◽

Full Waveform

Seismic imaging using full-wavefield data that includes primary reflections, transmitted waves, and their multiples has been the holy grail for generations of geophysicists. To be able to use the full-wavefield data effectively requires a forward-modeling process to generate full-wavefield data, an inversion scheme to minimize the difference between modeled and recorded data, and, more importantly, an accurate velocity model to correctly propagate and collapse energy of different wave modes. All of these elements have been embedded in the framework of full-waveform inversion (FWI) since it was proposed three decades ago. However, for a long time, the application of FWI did not find its way into the domain of full-wavefield imaging, mostly owing to the lack of data sets with good constraints to ensure the convergence of inversion, the required compute power to handle large data sets and extend the inversion frequency to the bandwidth needed for imaging, and, most significantly, stable FWI algorithms that could work with different data types in different geologic settings. Recently, with the advancement of high-performance computing and progress in FWI algorithms at tackling issues such as cycle skipping and amplitude mismatch, FWI has found success using different data types in a variety of geologic settings, providing some of the most accurate velocity models for generating significantly improved migration images. Here, we take a step further to modify the FWI workflow to output the subsurface image or reflectivity directly, potentially eliminating the need to go through the time-consuming conventional seismic imaging process that involves preprocessing, velocity model building, and migration. Compared with a conventional migration image, the reflectivity image directly output from FWI often provides additional structural information with better illumination and higher signal-to-noise ratio naturally as a result of many iterations of least-squares fitting of the full-wavefield data.

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High-resolution depth imaging of long-offset 2D data using full-waveform inversion: The Malvinas Basin, Argentina

The Leading Edge ◽

10.1190/tle38030220.1 ◽

2019 ◽

Vol 38 (3) ◽

pp. 220-225

Author(s):

Laurence Letki ◽

Mike Saunders ◽

Monica Hoppe ◽

Milos Cvetkovic ◽

Lewis Goss ◽

...

Keyword(s):

High Resolution ◽

Waveform Inversion ◽

Velocity Model ◽

Full Waveform Inversion ◽

Data Set ◽

Depth Imaging ◽

Additional Information ◽

Full Waveform ◽

Seismic Image ◽

2D Data

The Argentina Austral Malvinas survey comprises 13,784 km of 2D data extending from the shelf to the border with the Falkland Islands. The survey was acquired using a 12,000 m streamer and continuous recording technology and was processed through a comprehensive broadband prestack depth migration workflow focused on producing a high-resolution, high-fidelity data set. Source- and receiver-side deghosting to maximize the bandwidth of the data was an essential ingredient in the preprocessing. Following the broadband processing sequence, a depth-imaging workflow was implemented, with the initial model built using a time tomography approach. Several passes of anisotropic reflection tomography provided a significant improvement in the velocity model prior to full-waveform inversion (FWI). Using long offsets, FWI made use of additional information contained in the recorded wavefield, including the refracted and diving wave energy. FWI resolved more detailed velocity variations both in the shallow and deeper section and culminated in an improved seismic image.

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Edge-preserving smoothing for simultaneous-source full-waveform inversion model updates in high-contrast velocity models

Geophysics ◽

10.1190/geo2017-0563.1 ◽

2018 ◽

Vol 83 (2) ◽

pp. A33-A37 ◽

Cited By ~ 5

Author(s):

Amsalu Y. Anagaw ◽

Mauricio D. Sacchi

Keyword(s):

Waveform Inversion ◽

Velocity Model ◽

Model Parameters ◽

Full Waveform Inversion ◽

High Contrast ◽

Edge Preserving ◽

Velocity Models ◽

Smoothing Filter ◽

Full Waveform ◽

Simultaneous Source

Full-waveform inversion (FWI) can provide accurate estimates of subsurface model parameters. In spite of its success, the application of FWI in areas with high-velocity contrast remains a challenging problem. Quadratic regularization methods are often adopted to stabilize inverse problems. Unfortunately, edges and sharp discontinuities are not adequately preserved by quadratic regularization techniques. Throughout the iterative FWI method, an edge-preserving filter, however, can gently incorporate sharpness into velocity models. For every point in the velocity model, edge-preserving smoothing assigns the average value of the most uniform window neighboring the point. Edge-preserving smoothing generates piecewise-homogeneous images with enhanced contrast at boundaries. We adopt a simultaneous-source frequency-domain FWI, based on quasi-Newton optimization, in conjunction with an edge-preserving smoothing filter to retrieve high-contrast velocity models. The edge-preserving smoothing filter gradually removes the artifacts created by simultaneous-source encoding. We also have developed a simple model update to prevent disrupting the convergence of the optimization algorithm. Finally, we perform tests to examine our algorithm.

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From tomography to full-waveform inversion with a single objective function

Geophysics ◽

10.1190/geo2013-0291.1 ◽

2014 ◽

Vol 79 (2) ◽

pp. R55-R61 ◽

Cited By ~ 25

Author(s):

Tariq Alkhalifah ◽

Yunseok Choi

Keyword(s):

Objective Function ◽

Initial Velocity ◽

Waveform Inversion ◽

Synthetic Data ◽

Velocity Model ◽

Full Waveform Inversion ◽

Half Cycle ◽

Velocity Models ◽

Full Waveform ◽

Single Objective

In full-waveform inversion (FWI), a gradient-based update of the velocity model requires an initial velocity that produces synthetic data that are within a half-cycle, everywhere, from the field data. Such initial velocity models are usually extracted from migration velocity analysis or traveltime tomography, among other means, and are not guaranteed to adhere to the FWI requirements for an initial velocity model. As such, we evaluated an objective function based on the misfit in the instantaneous traveltime between the observed and modeled data. This phase-based attribute of the wavefield, along with its phase unwrapping characteristics, provided a frequency-dependent traveltime function that was easy to use and quantify, especially compared to conventional phase representation. With a strong Laplace damping of the modeled, potentially low-frequency, data along the time axis, this attribute admitted a first-arrival traveltime that could be compared with picked ones from the observed data, such as in wave equation tomography (WET). As we relax the damping on the synthetic and observed data, the objective function measures the misfit in the phase, however unwrapped. It, thus, provided a single objective function for a natural transition from WET to FWI. A Marmousi example demonstrated the effectiveness of the approach.

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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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Reconstruction of the Near-Surface Velocities with Trap Bodies by the Full Waveform Inversion

10.2118/206594-ms ◽

2021 ◽

Author(s):

Kirill Gennadievich Gadylshin ◽

Vladimir Albertovich Cheverda ◽

Danila Nikolaevich Tverdokhlebov

Keyword(s):

Free Surface ◽

Surface Area ◽

Waveform Inversion ◽

Full Waveform Inversion ◽

Near Surface ◽

Velocity Models ◽

Full Waveform ◽

Seismic Image ◽

Geological Conditions ◽

Velocity Anomalies

Abstract Seismic surveys in the vast territory of Eastern Siberia are carried out in seismic and geological conditions of varying complexity. Obtaining a high-quality dynamic seismic image for the work area is a priority task in the states of contrasting heterogeneities of the near-surface. For this, it is necessary to restore an effective depth-velocity model that provides compensation for velocity anomalies and calculates static corrections. However, for the most complex near-surface structure, for example, the presence of trap intrusions and tuffaceous formations, the information content of the velocity models of the near-surface area obtained based on tomographic refinement turns out to be insufficient, and a search for another solution is required. The paper considers an approach based on Full Waveform Inversion (FWI). As the authors showed earlier, multiples associated with the free surface reduce the resolution of this approach. But their use increases the stability of the solution in the presence of uncorrelated noise. Therefore, at the first stage of FWI, the full wavefield is used, including free surface-related multiples, but they are suppressed in the next steps of the data processing. The results obtained demonstrate the ability of the FWI to restore complex geological structures of the near-surface area, even in the presence of high-velocity anomalies (trap intrusions).

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Full-waveform inversion for imaging and geologic interpretation: A deepwater Gulf of Mexico case study

Interpretation ◽

10.1190/int-2018-0163.1 ◽

2019 ◽

Vol 7 (2) ◽

pp. SB11-SB21

Author(s):

Yannick Cobo ◽

Carlos Calderón-Macías ◽

Shihong Chi

Keyword(s):

Gulf Of Mexico ◽

Waveform Inversion ◽

Velocity Model ◽

Full Waveform Inversion ◽

Depth Range ◽

Frequency Model ◽

Amplitude Inversion ◽

Velocity Models ◽

Full Waveform ◽

Wavefield Inversion

Full-waveform inversion (FWI) is commonly used in model-building workflows to improve the resolution of the shallow velocity model and thus has a potentially positive impact on the imaging of deeper targets. This type of inversion commonly makes use of first arrivals from the longest offsets. However, signal from smaller offsets and later times can extend the depth range of the FWI-derived velocity model. Waveform inversion methods that use reflections have been shown to provide greater details and accuracy when deriving velocity models for deepwater exploration and production. The derived velocity sometimes provides an improved migrated image useful for interpretation in complex geology and enhances geologic features such as subsalt sediments, faults, and channels. We have used combination of FWI and a wavefield inversion approach known as reconstructed wavefield inversion (RWI) that makes use of diving waves and reflections to derive a velocity model for a deepwater survey off the coast of Veracruz in the Gulf of Mexico. The velocity model we derived from this approach produces an improved image of the target reservoir, and furthermore contains enough geologic details for direct interpretation. We enhanced the resolution of the velocity model further by performing a poststack amplitude inversion with the FWI + RWI derived velocity used as the input low-frequency model. The resulting high-resolution velocity provides an excellent product for detecting shallow gas anomalies, delineating a gas reservoir in an anticline structure as well as a system of deep, sand-filled channels. The inverted velocity also indicates a better correlation with sonic velocity measured from two blind wells than the initial tomography velocity, indicating the benefits of FWI approaches for quantitative reservoir characterization in deepwater environments.

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Estimation of acoustic macro models using a genetic full-waveform inversion: Applications to the Marmousi model

Geophysics ◽

10.1190/geo2015-0198.1 ◽

2016 ◽

Vol 81 (4) ◽

pp. R173-R184 ◽

Cited By ~ 32

Author(s):

Angelo Sajeva ◽

Mattia Aleardi ◽

Eusebio Stucchi ◽

Nicola Bienati ◽

Alfredo Mazzotti

Keyword(s):

Prior Information ◽

Waveform Inversion ◽

Velocity Model ◽

Forward Modeling ◽

Coarse Grid ◽

P Wave ◽

Stochastic Methods ◽

Full Waveform Inversion ◽

Velocity Models ◽

Full Waveform

We have developed a stochastic full-waveform inversion that uses genetic algorithms (GA FWI) to estimate acoustic macro models of the P-wave velocity field. Stochastic methods such as GA severely suffer the curse of dimensionality, meaning that they require unaffordable computer resources for inverse problems with many unknowns and expensive forward modeling. To mitigate this issue, we have proposed a two-grid technique with a coarse grid to represent the subsurface for the GA inversion and a finer grid for the forward modeling. We have applied this procedure to invert synthetic acoustic data of the Marmousi model, and we have developed three different tests. The first two tests use a velocity model derived from standard stacking velocity analysis as prior information and differ only for the parameterization of the coarse grid. Their comparison indicates that a smart parameterization of the coarse grid may significantly improve the final result. The third test uses a linearly increasing 1D velocity model as prior information, a layer-stripping procedure, and a large number of model evaluations. All three tests return velocity models that fairly reproduce the long-wavelength structures of the Marmousi. First-break cycle skipping related to the seismograms of the final GA-FWI models is significantly reduced compared with that computed on the models used as prior information. Descent-based FWIs starting from final GA-FWI models yield velocity models with low and comparable model misfits and with an improved reconstruction of the structural details. The quality of the models obtained by GA FWI plus descent-based FWI is benchmarked against the models obtained by descent-based FWI started from a smoothed version of the Marmousi and started directly from the prior information models. Our results are promising and demonstrate the ability of the two-grid GA FWI to yield velocity models suitable as input to descent-based FWI.

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3D high-resolution seismic imaging of the iron-oxide deposits in Ludvika (Sweden) using full-waveform inversion and reverse-time migration

10.5194/se-2021-122 ◽

2021 ◽

Author(s):

Brij Singh ◽

Michał Malinowski ◽

Andrzej Górszczyk ◽

Alireza Malehmir ◽

Stefan Buske ◽

...

Keyword(s):

Waveform Inversion ◽

Velocity Model ◽

Mine Planning ◽

Full Waveform Inversion ◽

Reverse Time ◽

Reverse Time Migration ◽

Depth Imaging ◽

Velocity Models ◽

Full Waveform ◽

Time Migration

Abstract. A sparse 3D seismic survey was acquired over the Blötberget iron-oxide deposits of the Ludvika Mines in south-central Sweden. The main aim of the survey was to delineate the deeper extension of the mineralisation and to better understand its 3D nature and associated fault systems for mine planning purposes. To obtain a high-quality seismic image in depth, we applied time-domain 3D acoustic full-waveform inversion (FWI) to build a high-resolution P-wave velocity model. This model was subsequently used for pre-stack depth imaging with reverse time migration (RTM) to produce the complementary reflectivity section. We developed a data preprocessing workflow and inversion strategy for the successful implementation of FWI in the hardrock environment. We obtained a high-fidelity velocity model using FWI and assessed its robustness. We extensively tested and optimised the parameters associated with the RTM method for subsequent depth imaging using different velocity models: a constant velocity model, a model built using first-arrival traveltime tomography and a velocity model derived by FWI. We compare our RTM results with a priori data available in the area. We conclude that, from all tested velocity models, the FWI velocity model in combination with the subsequent RTM step, provided the most focussed image of the mineralisation and we successfully mapped its 3D geometrical nature. In particular, a major reflector interpreted as a cross-cutting fault, which is restricting the deeper extension of the mineralisation with depth, and several other fault structures which were earlier not imaged were also delineated. We believe that a thorough analysis of the depth images derived with the combined FWIRTM approach that we presented here can provide more details which will help with better estimation of areas with high mineralization, better mine planning and safety measures.

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Unlocking the potential of conventional narrow azimuth data by full-waveform inversion: a deep carbonate imaging case study, offshore Indonesia

10.29118/ipa21-g-163 ◽

2021 ◽

Author(s):

J. Wang

Keyword(s):

Complex Structure ◽

Time Lag ◽

Waveform Inversion ◽

Low Frequency ◽

Time Shift ◽

Full Waveform Inversion ◽

Velocity Models ◽

Full Waveform ◽

Seismic Image ◽

Refraction Tomography

Full-waveform inversion (FWI) has evolved to be the contemporary solution to resolve velocity models in areas of complex structure. Further, wide azimuth, long offset and rich low-frequency seismic data, resulting from broadband seismic acquisition, helps FWI update deeper with better convergence and stability. In this study from the South Mahakam area in offshore Indonesia, multiple layers of carbonate exist from shallow to deep with sharp velocity contrast. The target reservoir is down to 3.5 kilometers. However, for the acquired data with narrow azimuths (NAZ), short offsets (3 kilometers) and low signal to noise in the low frequencies, FWI encounters challenges of cycle skipping and unstable updates in the deeper targets that are beyond the diving-wave penetration depth. Time-lag FWI (TLFWI) (Zhang et al., 2018) uses time-shift differences between observed and modeled data as the cost function, and also makes better use of the low-frequency refraction and reflection energy. TLFWI gave good velocity updates in both the shallow and deep regions and, hence, gave an improved deep carbonate image. The anisotropic model is an important factor for the success of any FWI due to the coupling between velocity and anisotropy. In this paper, joint reflection and refraction tomography (Allemand et al., 2017) were applied in order to obtain stable anisotropy models for TLFWI. Following that, TLFWI with both refraction and reflection energy gives sensible velocity updates down to 3.5 kilometers. These updates to the model improve the seismic image and, importantly, reduce the depth uncertainties in this complex geological setting. The cumulative improvements increase interpretation confidence and can reduce future drilling risks. For the seismic processing community, the reprocessing of narrow azimuth, short-offset data with TLFWI, and associated technologies, offers great potential for generating improved and more reliable images from legacy, conventional, acquisition scenarios.

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