Application of Early Arrival Waveform Inversion to Qademah Shallow Land Data

Xue, Qingfeng; Hanafy, Sherif M.; Zheng, Yingcai; Wang, Y. B.

doi:10.3997/2214-4609.201601292

Cited by 3 publications

(4 citation statements)

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“…Finally, to further demonstrate the applicability of the proposed framework, we test our algorithm on 2D near‐surface land seismic data acquired along the Qademah fault in the western part of Saudi Arabia (Xue et al., 2016). The acquisition aims to delineate the corresponding fault blocks interpreted as low‐velocity layers in the subsurface.…”

Section: Resultsmentioning

confidence: 99%

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Learned Regularizations for Multi‐Parameter Elastic Full Waveform Inversion Using Diffusion Models

Taufik,

Wang,

Alkhalifah

2024

Journal of Geophysical Research: Machine Learning and Computati

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Elastic full waveform inversion (EFWI) promises to account for the Earth's elastic nature and corresponding reflectivity, which is often disregarded in the commonly used acoustic FWI. However, EFWI usually requires a more sophisticated recording apparatus (beyond the usual single‐component data). Even in the presence of multicomponent recordings, an empirical formulation that relates the elastic parameters is usually employed. Such approximations, thus, render the inverted elastic parameters (and their relationship) hostage to our assumptions. To overcome these limitations, we introduce learned regularization using diffusion models. Specifically, we first train the (unsupervised) diffusion model to understand the coupling relationship of the distribution of the elastic parameters and use the trained model in the inversion process with a negligible additional computational cost. To fully realize the effect of our regularization and to mimic a realistic scenario, the vertical component of the particle velocity is used to invert the elastic parameters. Unlike other learned (deep) regularizers, diffusion models offer a unique conditional capability that suits the nature of an FWI process (going from low to high frequency) while maintaining the problem‐agnostic feature of such regularizers. Numerical experiments, ranging from synthetic to land field data, show that our framework solves the illumination effects from an imperfect acquisition setup and provides more realistic elastic parameter ratios than the conventional EFWI. We also empirically demonstrate that, unlike traditional regularization schemes, our framework converges to better model estimates that fit the observed data better.

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Section: Resultsmentioning

confidence: 99%

“…The example shot gathers in Figure 12b is used in the inversion. The initial model is inspired and obtained by travel time tomography (Xue et al, 2016) with an increase in depth starting from 30 m to a maximum velocity of 2,200 m/s. Empirical formulations are used to generate the initial shear velocity and density.…”

Section: Field Data Examplesmentioning

confidence: 99%

Learned Regularizations for Multi‐Parameter Elastic Full Waveform Inversion Using Diffusion Models

Taufik,

Wang,

Alkhalifah

2024

Journal of Geophysical Research: Machine Learning and Computati

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“…) and land data (Xue et al . ). Shen () developed a weighted early‐arrival waveform inversion method, which focuses more on matching the phase rather than the amplitude in the data and later applied to land data (Yu, Zhang and Wang ).…”

Section: Introductionmentioning

confidence: 97%

Applying joint traveltime and waveform inversion to image the Sichuan Basin, China

Zhang

2017

Near Surface Geophysics

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We apply a joint first-arrival traveltime and early-arrival waveform inversion method to image complex near-surface structures in the Sichuan Basin, China. The area includes rugged topography and large near-surface velocity variations. Due to the near-surface effects, it is difficult to produce highquality reflection images of the deep subsurface. First-arrival traveltime tomography is often applied for near-surface imaging, but the results may not be sufficiently accurate because of ray assumptions and the limited traveltime information. Waveform inversion should allow complex structures to be resolved; however, it may fall into local minima because of cycle skipping issues and may also produce artefacts in very shallow areas that are associated with rugged topography. Therefore, we combine the advantages of the two methods and mitigate their problems by performing joint inversion of the two types of data. We demonstrate the effectiveness of the joint inversion method using synthetic and real data from Sichuan, China. In the real data example, we compare the velocity models resolved from waveform inversion alone with those resolved from the joint inversion. We calculate long-wavelength static corrections and apply them to the data processing. The common midpoint stacking results show that the joint inversion method produces a more effective statics solution. 1995). FWI can resolve the details of a velocity model because it is based on the waveform information; thus, FWI should be able to image geologically complex areas (Tarantola 1984;Sheng 2006). The FWI solution should also provide a higher resolution velocity image than traveltime tomography (Gauthier, Virieux and Tarantola 1986).However, the objective function of waveform inversion is highly nonlinear and may include many local minima. The inversion may fall into a local minima due to cycle skipping between the predicted and observed data (Virieux and Operto 2009). Several methods have been proposed recently to overcome or mitigate the cycle skipping issue. Ma and Hale (2013) proposed a hybrid waveform inversion method in which wave-equation reflection traveltime inversion was used to update the lowwavenumber component, and FWI was employed to update the high-wavenumber details of the velocity model. Wu, Luo and Wu (2013) developed a waveform envelope inversion method to utilise low-frequency information from the data. The envelope inversion method can avoid the cycle skipping problem and retrieve the long-wavelength background velocity model for FWI. Warner and Guasch (2014) proposed adaptive waveform inversion, which reformulates the objective functions in terms of matching filters. The difference between the observed and predicted data is projected onto the time lag of the filter. Other

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“…Recently, several studies of early arrival waveform inversion (EWI) have been investigated. Sheng et al [14] proposed an EWI method for imaging the near-surface velocity, by minimizing the data misfit between the synthetic and observed waveform in the time window of early arrivals, and they later applied this method to marine data [15] and land data [16]. Shen [17] developed a weighted EWI method by matching the phase instead of amplitude during inversion, which was later applied to shallow land data [18].…”

Section: Introductionmentioning

confidence: 99%

Geometrical-Feature-Preserving Adjoint Tomography of Near-Surface Structure with Seismic Early Arrival

Zhang

Cai

2022

Shock and Vibration

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Early arrival waveform inversion (EWI) is an essential approach to obtaining velocity structures in near-surface. Due to suffering from a cycle‐skipping issue, it is difficult to reach the global minima for conventional EWI with the misfit function of least-squares norm (L2‐norm). Following the optimal transportation theory, we developed an EWI solution with a new objective function based on quadratic‐Wasserstein‐metric (W2-norm) to maintain the geometric characteristics of the distribution and improve the stability and convexity of the inverse problem. First, we gave the continuous form of the adjoint source and the Frechet gradient of the Wasserstein metric for seismic early arrival, which leads to an easy and efficient way to implement in the adjoint-state method. Then, we conducted two synthetic experiments on the target model containing some velocity anomalies and hidden layers to test its effectiveness in mapping accurate and high-resolution near-surface velocity structure. The results show that the W2-normed EWI can mitigate cycle-skipping issues compared with the L2-normed EWI. In addition, it can deal with hidden layers and is robust in terms of noise. The application to a real dataset indicates that this new solution can recover more details in the shallow structure, especially in the aspect of dealing with hidden layers.

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Application of Early Arrival Waveform Inversion to Qademah Shallow Land Data

Cited by 3 publications

References 0 publications

Learned Regularizations for Multi‐Parameter Elastic Full Waveform Inversion Using Diffusion Models

Learned Regularizations for Multi‐Parameter Elastic Full Waveform Inversion Using Diffusion Models

Applying joint traveltime and waveform inversion to image the Sichuan Basin, China

Geometrical-Feature-Preserving Adjoint Tomography of Near-Surface Structure with Seismic Early Arrival

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