The impact of acquisition geometry on full-waveform inversion updates

2021 ◽  
Vol 40 (5) ◽  
pp. 335-341
Author(s):  
Denes Vigh ◽  
Xin Cheng ◽  
Kun Jiao ◽  
Wei Kang ◽  
Nolan Brand
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Depth Image ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
Full Waveform ◽  
Data Content ◽  
The Impact

Full-waveform inversion (FWI) is a high-resolution model-building technique that uses the entire recorded seismic data content to build the earth model. Conventional FWI usually utilizes diving and refracted waves to update the low-wavenumber components of the velocity model. However, updates are often depth limited due to the limited offset range of the acquisition design. To extend conventional FWI beyond the limits imposed by using only transmitted energy, we must utilize the full acquired wavefield. Analyzing FWI kernels for a given geology and acquisition geometry can provide information on how to optimize the acquisition so that FWI is able to update the velocity model for targets as deep as basement level. Recent long-offset ocean-bottom node acquisition helped FWI succeed, but we would also like to be able to utilize the shorter-offset data from wide-azimuth data acquisitions to improve imaging of these data sets by developing the velocity field with FWI. FWI models are heading toward higher and higher wavenumbers, which allows us to extract pseudoreflectivity directly from the developed velocity model built with the acoustic full wavefield. This is an extremely early start to obtaining a depth image that one would usually produce in much later processing stages.

Earth-model building from shallow to deep with full-waveform inversion

2016 ◽  
Vol 35 (12) ◽  
pp. 1025-1030 ◽  
Author(s):  
Denes Vigh ◽  
Kun Jiao ◽  
Xin Cheng ◽  
Dong Sun ◽  
Winston Lewis
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Reflection Data ◽  
Full Waveform ◽  
Resolution Model ◽  
Data Content ◽  
High Resolution Model

Full-waveform inversion (FWI) is a high-resolution model-building technique that uses the entire recorded seismic data content to build the earth model. Conventional FWI usually utilizes diving and refracted waves to update the low-wavenumber/background components of the model; however, the update is often depth limited due to the limited offset range acquired. To extend conventional FWI beyond the limits of the transmitted energy, we must use reflection data. Synthetic and field data examples demonstrate that, even in a complex subsalt Gulf of Mexico setting, the background velocity model can be updated from shallow to deep water using conventional FWI followed by reflection-based FWI. A future refinement of the technique shows that, after updating the sediment model, the salt boundaries can be further updated by level-set technology.

Application of multiparameter full-waveform inversion in Central Luconia Basin, Sarawak

2016 ◽  
Vol 4 (4) ◽  
pp. SU17-SU24 ◽  
Author(s):  
Vanessa Goh ◽  
Kjetil Halleland ◽  
René-Édouard Plessix ◽  
Alexandre Stopin
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Thin Layers ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Full Waveform ◽  
Velocity Variations ◽  
Central Luconia

Reducing velocity inaccuracy in complex settings is of paramount importance for limiting structural uncertainties, therefore helping the geologic interpretation and reservoir characterization. Shallow velocity variations due, for instance, to gas accumulations or carbonate reefs, are a common issue offshore Malaysia. These velocity variations are difficult to image through standard reflection-based velocity model building. We have applied full-waveform inversion (FWI) to better characterize the upper part of the earth model for a shallow-water field, located in the Central Luconia Basin offshore Sarawak. We have inverted a narrow-azimuth data set with a maximum inline offset of 4.4 km. Thanks to dedicated broadband preprocessing of the data set, we could enhance the signal-to-noise ratio in the 2.5–10 Hz frequency band. We then applied a multiparameter FWI to estimate the background normal moveout velocity and the [Formula: see text]-parameter. Full-waveform inversion together with broadband data processing has helped to better define the faults and resolve the thin layers in the shallow clastic section. The improvements in the velocity model brought by FWI lead to an improved image of the structural closure and flanks. Moreover, the increased velocity resolution helps in distinguishing between two different geologic interpretations.

An offshore Gabon full-waveform inversion case study

2016 ◽  
Vol 4 (4) ◽  
pp. SU25-SU39 ◽  
Author(s):  
Bingmu Xiao ◽  
Nadezhda Kotova ◽  
Samuel Bretherton ◽  
Andrew Ratcliffe ◽  
Gregor Duval ◽  
...  
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Earth Model ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Velocity Model Building ◽  
Building Process ◽  
Model Building Process

Velocity model building is one of the most difficult aspects of the seismic processing sequence. But it is also one of the most important: an accurate earth model allows an accurate migrated image to be formed, which allows the geologist a better chance at an accurate interpretation of the area. In addition, the velocity model itself can provide complementary information about the geology and geophysics of the region. Full-waveform inversion (FWI) is a popular, high-end velocity model-building tool that can generate high-resolution earth models, especially in regions of the model probed by the transmitted (diving wave) arrivals on the recorded seismic data. The history of the South Gabon Basin is complex, leading to a rich geologic picture today and a very challenging velocity model-building process. We have developed a case study from the offshore Gabon area showing that FWI is able to help with the model-building process, and the resulting velocity model reveals features that improve the migrated image. The application of FWI is made on an extremely large area covering approximately 25,000 [Formula: see text], demonstrating that FWI can be applied to this magnitude of survey in a timely manner. In addition, the detail in the FWI velocity model aids the geologic interpretation by highlighting, among other things, the location of shallow gas pockets, buried channels, and carbonate rafts. The concept of actively using the FWI-derived velocity model to aid the interpretation in areas of complex geology, and/or to identify potential geohazards to avoid in an exploration context, is applicable to many parts of the world.

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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