scholarly journals Multiscale seismic imaging with inverse homogenization

2021 ◽  
Author(s):  
N Hedjazian ◽  
Y Capdeville ◽  
T Bodin
Keyword(s):  
Seismic Imaging ◽  
Effective Properties ◽  
Waveform Inversion ◽  
Homogenization Theory ◽  
Small Scale ◽  
Full Waveform Inversion ◽  
Inverse Homogenization ◽  
Long Wavelength ◽  
Full Waveform ◽  
Scale Models

Summary Seismic imaging techniques such as elastic full waveform inversion (FWI) have their spatial resolution limited by the maximum frequency present in the observed waveforms. Scales smaller than a fraction of the minimum wavelength cannot be resolved, and only a smoothed, effective version of the true underlying medium can be recovered. These finite-frequency effects are revealed by the upscaling or homogenization theory of wave propagation. Homogenization aims at computing larger scale effective properties of a medium containing small-scale heterogeneities. We study how this theory can be used in the context of FWI. The seismic imaging problem is broken down in a two-stage multiscale approach. In the first step, called homogenized full waveform inversion (HFWI), observed waveforms are inverted for a smooth, fully anisotropic effective medium, that does not contain scales smaller than the shortest wavelength present in the wavefield. The solution being an effective medium, it is difficult to directly interpret it. It requires a second step, called downscaling or inverse homogenization, where the smooth image is used as data, and the goal is to recover small-scale parameters. All the information contained in the observed waveforms is extracted in the HFWI step. The solution of the downscaling step is highly non-unique as many small-scale models may share the same long wavelength effective properties. We therefore rely on the introduction of external a priori information, and cast the problem in a Bayesian formulation. The ensemble of potential fine-scale models sharing the same long wavelength effective properties is explored with a Markov chain Monte Carlo algorithm. We illustrate the method with a synthetic cavity detection problem: we search for the position, size and shape of void inclusions in a homogeneous elastic medium, where the size of cavities is smaller than the resolving length of the seismic data. We illustrate the advantages of introducing the homogenization theory at both stages. In HFWI, homogenization acts as a natural regularization helping convergence toward meaningful solution models. Working with fully anisotropic effective media prevents the leakage of anisotropy induced by the fine scales into isotropic macro-parameters estimates. In the downscaling step, the forward theory is the homogenization itself. It is computationally cheap, allowing us to consider geological models with more complexity (e.g. including discontinuities) and use stochastic inversion techniques.

Downscaling seismic tomography models

2021 ◽  
Author(s):  
Navid Hedjazian ◽  
Thomas Bodin ◽  
Yann Capdeville
Keyword(s):  
Seismic Imaging ◽  
Waveform Inversion ◽  
Effective Medium ◽  
Scale Model ◽  
Multiscale Approach ◽  
Full Waveform Inversion ◽  
Fine Scale ◽  
Full Waveform ◽  
Macro Scale ◽  
Scale Models

<p>Seismic imaging techniques such as elastic full waveform inversion (FWI) have their spatial resolution limited by the maximum frequency present in the observed waveforms. Scales smaller than a fraction of the minimum wavelength cannot be resolved, only a smoothed version of the true underlying medium can be recovered. Application of FWI to media containing small and strong heterogeneities therefore remains problematic. This smooth tomographic image is related to the effective elastic properties, which can be exposed with the homogenization theory of wave propagation. We study how this theory can be used in the FWI context. The seismic imaging problem is broken down in a two-stage multiscale approach. In the first step, called homogenized full waveform inversion (HFWI), observed waveforms are inverted for a macro-scale, fully anisotropic effective medium, smooth at the scale of the shortest wavelength present in the wavefield. The solution being an effective medium, it is difficult to directly interpret it. It requires a second step, called downscaling, where the macro-scale image is used as data, and the goal is to recover micro-scale parameters. All the information contained in the waveforms is extracted in the HFWI step. The solution of the downscaling step is highly non-unique as many fine-scale models may share the same long wavelength effective properties. We therefore rely on the introduction of external a priori information. In this step, the forward theory is the homogenization itself. It is computationally cheap, allowing to consider geological models with more complexity.</p><p>In a first approach to downscaling, the ensemble of potential fine-scale models is described with an object-based parametrization, and explored with a MCMC algorithm. We illustrate the method with a synthetic cavity detection problem. In a second approach, the prior information is introduced by the means of a training image, and the fine-scale model is recovered with a multi-point statistics algorithm. We apply this method on a subsurface synthetic problem, where the goal is to recover geological facies.</p><p> </p>

Full-waveform inversion with wavefield gradients

2021 ◽  
Author(s):  
Sneha Singh ◽  
Yann Capdeville ◽  
Heiner Igel ◽  
Navid Hedjazian ◽  
Thomas Bodin
Keyword(s):  
Waveform Inversion ◽  
Effective Model ◽  
Small Scale ◽  
Full Waveform Inversion ◽  
Coupling Tensor ◽  
Full Waveform ◽  
Surface Topographies ◽  
Good For ◽  
Incorrect Model

<p>Wavefield gradient instruments, such as rotational sensors and DAS systems, are becoming more and more accessible in seismology. Their usage for Full Waveform Inversion (FWI) is in sight. Nevertheless, local small-scale heterogeneities, like geological inhomogeneities, surface topographies, and cavities are known to affect wavefield gradients. This effect is in fact measurable with current instruments. For example, the agreement between data and synthetics computed in a tomographic model is often not as good for rotation as it is for displacement.</p><p>The theory of homogenization can help us understand why small-scale heterogeneities strongly affect wavefield gradients, but not the wavefield itself. It tells us that at any receiver measuring wavefield gradient, small-scale heterogeneities cause the wavefield gradient to couple with strain through a coupling tensor <strong>J</strong>. Furthermore, this <strong>J</strong> is 1) independent of source, 2) independent of time, but 3) only dependent on the receiver location. Consequently, we can invert for <strong>J</strong> based on an effective model for which synthetics fit displacement data reasonably well. Once inverted, <strong>J</strong> can be used to correct all other wavefield gradients at that receiver.</p><p>Here, we aim to understand the benefits and drawbacks of wavefield gradient sensors in a FWI context. We show that FWIs performed with rotations and strains are equivalent to that performed with displacements provided that 1) the number of data is sufficient, and 2) the receivers are placed far away from heterogeneities. In the case that receivers are placed near heterogeneities, we find that due to the effect of these heterogeneities, an incorrect model is recovered from inversion. In this case therefore, the coupling tensor <strong>J</strong> needs to be taken into account for each receiver to get rid of the effect.</p>

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.

Improvement of GPR Full-waveform inversion images using Cone Penetration Test data

Geophysics ◽  
2021 ◽  
pp. 1-74
Author(s):  
Zhen Zhou ◽  
Anja Klotzsche ◽  
Jessica Schmäck ◽  
Harry Vereecken ◽  
Jan van der Kruk
Keyword(s):  
Waveform Inversion ◽  
Cone Penetration Test ◽  
Small Scale ◽  
Inversion Method ◽  
Full Waveform Inversion ◽  
Penetration Test ◽  
Cone Penetration ◽  
Full Waveform ◽  
Effective Source

Detailed characterization of aquifers is critical and challenging due to the existence of heterogeneous small-scale high-contrast layers. For an improved characterization of subsurface hydrological characteristics, crosshole ground penetrating radar (GPR) and Cone Penetration Test (CPT) measurements are performed. In comparison to the CPT approach that can only provide 1D high resolution data along vertical profiles, crosshole GPR enables measuring 2D cross-sections between two boreholes. Generally, a standard inversion method for GPR data is the ray-based approach that considers only a small amount of information and can therefore only provide limited resolution. In the last decade, full-waveform inversion (FWI) of crosshole GPR data in time domain has matured, and provides inversion results with higher resolution by exploiting the full recorded waveform information. However, the FWI results are limited due to complex underground structures and the non-linear nature of the method. A new approach that uses CPT data in the inversion process is applied to enhance the resolution of the final relative permittivity FWI results by updating the effective source wavelet. The updated effective source wavelet possesses a priori CPT information and a larger bandwidth. Using the same starting models, a synthetic model comparison between the conventional and updated FWI results demonstrates that the updated FWI method provides reliable and more consistent structures. To test the method, five experimental GPR cross-section results are analyzed with the standard FWI and the new proposed updated approach. Both synthetic and experimental results indicate the potential of improving the reconstruction of subsurface aquifer structures by combining conventional 2D FWI results and 1D CPT data.

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