scholarly journals A priori estimates of attraction basins for velocity model reconstruction by time-harmonic full-waveform inversion and data-space reflectivity formulation

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. R223-R241 ◽  
Author(s):  
Florian Faucher ◽  
Guy Chavent ◽  
Hélène Barucq ◽  
Henri Calandra
Keyword(s):  
Optimization Problems ◽  
A Priori ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Successful Implementation ◽  
Full Waveform Inversion ◽  
Complex Frequency ◽  
Data Space ◽  
Full Waveform ◽  
Attraction Basins

The determination of background velocity by full-waveform inversion (FWI) is known to be hampered by the local minima of the data misfit caused by the phase shifts associated with background perturbations. Attraction basins for the underlying optimization problems can be computed around any nominal velocity model, and they guarantee that the misfit functional has only one (global) minimum. The attraction basins are further associated with tolerable error levels representing the maximal allowed distance between the (observed) data and the simulations (i.e., the acceptable noise level). The estimates are defined a priori, and they only require the computation of (possibly many) the first- and second-order directional derivatives of the (model to synthetic) forward map. The geometry of the search direction and the frequency influence the size of the attraction basins, and the complex frequency can be used to enlarge the basins. The size of the attraction basins for the perturbation of background velocities in classic FWI (global model parameterization) and the data-space reflectivity reformulation (migration-based traveltime [MBTT]) are compared: The MBTT reformulation substantially increases the size of the attraction basins (by a factor of 4–15). Practically, this reformulation compensates for the lack of low-frequency data. Our analysis provides guidelines for a successful implementation of the MBTT reformulation.

scholarly journals Interferometric full-waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. R45-R60
Author(s):  
Mrinal Sinha ◽  
Gerard T. Schuster
Keyword(s):  
Field Data ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Target Region ◽  
Data Space ◽  
Full Waveform ◽  
Shallow Part

Velocity errors in the shallow part of the velocity model can lead to erroneous estimates of the full-waveform inversion (FWI) tomogram. If the location and topography of a reflector are known, then such a reflector can be used as a reference reflector to update the underlying velocity model. Reflections corresponding to this reference reflector are windowed in the data space. Windowed reference reflections are then crosscorrelated with reflections from deeper interfaces, which leads to partial cancellation of static errors caused by the overburden above the reference interface. Interferometric FWI (IFWI) is then used to invert the tomogram in the target region, by minimizing the normalized waveform misfit between the observed and predicted crosscorrelograms. Results with synthetic and field data with static errors above the reference interface indicate that an accurate tomogram can be inverted in areas lying within several wavelengths of the reference interface. IFWI can also be applied to synthetic time-lapse data to mitigate the nonrepeatability errors caused by time-varying overburden variations. The synthetic- and field-data examples demonstrate that IFWI can provide accurate tomograms when the near surface is ridden with velocity errors.

Full-waveform inversion with randomized space shift

2019 ◽  
Vol 38 (3) ◽  
pp. 197-203 ◽  
Author(s):  
Jizhong Yang ◽  
Yunyue Elita Li ◽  
Yanwen Wei ◽  
Haohuan Fu ◽  
Yuzhu Liu
Keyword(s):  
Optimization Problems ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Explicit Calculation ◽  
Sampling Point ◽  
Full Waveform Inversion ◽  
Time Step ◽  
Model Estimate ◽  
Velocity Models ◽  
Full Waveform

Full-waveform inversion (FWI) has the great potential to retrieve high-fidelity subsurface models, with the constraint that the traveltime difference between the predicted data and the observed data should be less than half of the period at the lowest available frequency. If the above constraint is not satisfied, FWI will suffer from severe convergence problems and may get stuck in erroneous local minimum. To mitigate the dependence of FWI on the quality of the starting model, we apply the robust gradient sampling algorithm (GSA) on nonsmooth, nonconvex optimization problems to FWI. The original implementation of GSA requires explicit calculation of the gradient at each sampling point. When combined with FWI, this procedure involves tremendous computational costs for calculating the forward- and backward-propagated wavefields at each sampled velocity model within the vicinity of the current model estimate. Through numerical analyses, we find that the gradients corresponding to slightly perturbed velocity models can be approximated by space shifting the gradient obtained from the current velocity model. By randomly choosing one space shift at each time step during the gradient calculation, the computational cost is thus the same as conventional FWI. Numerical examples based on the 2004 BP model demonstrate that the proposed method can provide much better results than conventional FWI when starting from a crude initial velocity model.

scholarly journals Elastic Full-Waveform Inversion Using Migration-Based Depth Reflector Representation in the Data Domain

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 76
Author(s):  
Vladimir Tcheverda ◽  
Kirill Gadylshin
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Critical Element ◽  
Full Waveform Inversion ◽  
Seismic Data Processing ◽  
Velocity Models ◽  
Data Space ◽  
Full Waveform

The depth velocity model is a critical element for providing seismic data processing success, as it is responsible for the times of waves’ propagation and, therefore, prescribes the location of geological objects in the resulting seismic images. Constructing a deep velocity model is the most time-consuming part of the entire seismic data processing, which usually requires interactive human intervention. This article introduces the consistently numerical method for reconstructing a depth velocity model based on the modified version of the elastic Full Waveform Inversion (FWI). The specific feature of this approach to FWI is the decomposition of the space of admissible velocity models into subspaces of propagator (macro velocity) and reflector components. In turn, the latter transforms to the data space reflectivity on the base of migration transformation. Finally, we perform minimisation in two different spaces: (1) Macro velocity as a smooth spatial function; (2) Migration transforms data space reflectivity to the spatial reflectivity. We present numerical experiments confirming less sensitiveness of the modified version of FWI to the lack of the low time frequencies in the data acquired. In our computations, we use synthetic data with valuable time frequencies from 5 Hz.

scholarly journals Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope

2021 ◽  
Author(s):  
Ehsan Jamali Hondori ◽  
Chen Guo ◽  
Hitoshi Mikada ◽  
Jin-Oh Park
Keyword(s):  
Seismic Data ◽  
Initial Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Control Measures ◽  
Japan Trench ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Time Migration ◽  
Shallow Sediments

AbstractFull-waveform inversion (FWI) of limited-offset marine seismic data is a challenging task due to the lack of refracted energy and diving waves from the shallow sediments, which are fundamentally required to update the long-wavelength background velocity model in a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to ensure that the observed and simulated waveforms kinematically fit within an error of less than half a wavelength to protect the FWI iterative local optimization scheme from cycle skipping. We use a migration-based velocity analysis (MVA) method, including a combination of the layer-stripping approach and iterations of Kirchhoff prestack depth migration (KPSDM), to build an accurate initial velocity model for the FWI application on 2D seismic data with a maximum offset of 5.8 km. The data are acquired in the Japan Trench subduction zone, and we focus on the area where the shallow sediments overlying a highly reflective basement on top of the Cretaceous erosional unconformity are severely faulted and deformed. Despite the limited offsets available in the seismic data, our carefully designed workflow for data preconditioning, initial model building, and waveform inversion provides a velocity model that could improve the depth images down to almost 3.5 km. We present several quality control measures to assess the reliability of the resulting FWI model, including ray path illuminations, sensitivity kernels, reverse time migration (RTM) images, and KPSDM common image gathers. A direct comparison between the FWI and MVA velocity profiles reveals a sharp boundary at the Cretaceous basement interface, a feature that could not be observed in the MVA velocity model. The normal faults caused by the basal erosion of the upper plate in the study area reach the seafloor with evident subsidence of the shallow strata, implying that the faults are active.

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

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.

Multi-parameter model building for the Qiuyue structure using four-component ocean-bottom seismometer data

Geophysics ◽  
2021 ◽  
pp. 1-52
Author(s):  
Yuzhu Liu ◽  
Xinquan Huang ◽  
Jizhong Yang ◽  
Xueyi Liu ◽  
Bin Li ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Velocity Analysis ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Ocean Bottom Seismometer ◽  
Full Waveform ◽  
P Wave Velocity

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multi-channel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom seismometer (OBS) acquisition program acquired a four-component dataset in East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we applied a full-waveform inversion (FWI) workflow to this dense four-component OBS dataset. After preprocessing, including receiver geometry correction, moveout correction, component rotation, and energy transformation from 3D to 2D, a preconditioned first-arrival traveltime tomography based on an improved scattering integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavefield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at 2.0 km and 4.7 km depth. Initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic full-waveform inversion to refine both velocity models. Compared to a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single-component, the workflow presented in this study represents a good approach for inverting the four-component OBS dataset to characterize sub-seafloor velocity structures.

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