Study of two‐step tomographic inversion for near surface modeling

2011 ◽  
Author(s):  
Xinyuan Feng ◽  
Xishuang Wang ◽  
Yuchao Wang ◽  
Ziduo Hu ◽  
Liansheng Liu
Keyword(s):  
Surface Modeling ◽  
Tomographic Inversion ◽  
Near Surface

A reality check on full-wave inversion applied to land seismic data for near-surface modeling

2022 ◽  
Vol 41 (1) ◽  
pp. 40-46
Author(s):  
Öz Yilmaz ◽  
Kai Gao ◽  
Milos Delic ◽  
Jianghai Xia ◽  
Lianjie Huang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Surface Modeling ◽  
P Wave ◽  
Borehole Data ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Wave Inversion ◽  
Full Wave ◽  
Elastic Inversion

We evaluate the performance of traveltime tomography and full-wave inversion (FWI) for near-surface modeling using the data from a shallow seismic field experiment. Eight boreholes up to 20-m depth have been drilled along the seismic line traverse to verify the accuracy of the P-wave velocity-depth model estimated by seismic inversion. The velocity-depth model of the soil column estimated by traveltime tomography is in good agreement with the borehole data. We used the traveltime tomography model as an initial model and performed FWI. Full-wave acoustic and elastic inversions, however, have failed to converge to a velocity-depth model that desirably should be a high-resolution version of the model estimated by traveltime tomography. Moreover, there are significant discrepancies between the estimated models and the borehole data. It is understandable why full-wave acoustic inversion would fail — land seismic data inherently are elastic wavefields. The question is: Why does full-wave elastic inversion also fail? The strategy to prevent full-wave elastic inversion of vertical-component geophone data trapped in a local minimum that results in a physically implausible near-surface model may be cascaded inversion. Specifically, we perform traveltime tomography to estimate a P-wave velocity-depth model for the near-surface and Rayleigh-wave inversion to estimate an S-wave velocity-depth model for the near-surface, then use the resulting pairs of models as the initial models for the subsequent full-wave elastic inversion. Nonetheless, as demonstrated by the field data example here, the elastic-wave inversion yields a near-surface solution that still is not in agreement with the borehole data. Here, we investigate the limitations of FWI applied to land seismic data for near-surface modeling.

Near-surface modeling: A multiphysics approach

2016 ◽  
Vol 35 (11) ◽  
pp. 968-976 ◽  
Author(s):  
Marco Mantovani
Keyword(s):  
Surface Modeling ◽  
Near Surface

Model–parameterization scaling for improving accuracy of seismic tomography in reconstructing near–surface velocity model

2020 ◽  
Author(s):  
Gleb S. Chernyshov ◽  
◽  
Anton A. Duchkov ◽  
Aleksander A. Nikitin ◽  
Ivan Yu. Kulakov ◽  
...  
Keyword(s):  
Model Building ◽  
Regularization Parameter ◽  
Velocity Model ◽  
Surface Velocity ◽  
Surface Model ◽  
Tomographic Inversion ◽  
Near Surface ◽  
Model Parameterization ◽  
Regularization Parameters ◽  
Improving Accuracy

The problem of tomographic inversion is non–unique and requires regularization to solve it in a stable manner. It is highly non–trivial to choose between various regularization approaches or tune the regularization parameters themselves. We study the influence of one particular regularization parameter on the resolution and accuracy the tomographic inversion for the near–surface model building. We propose another regularization parameter, which allows to increase the accuracy of model building.

A new near-surface modeling method for static correction and PSDM processing in the thick piedmont alluvial fan area

2015 ◽  
Author(s):  
Wang Xinquan* ◽  
Feng Zeyuan ◽  
Ma Qingpo ◽  
Wei Deju ◽  
Shu Xianqiang ◽  
...  
Keyword(s):  
Surface Modeling ◽  
Modeling Method ◽  
Alluvial Fan ◽  
Near Surface ◽  
Static Correction

Near-surface modeling challenges highlighted in recent workshop

2020 ◽  
Vol 39 (5) ◽  
pp. 354-356
Author(s):  
Abdulaziz Saad ◽  
Moosa Al-Jahdhami
Keyword(s):  
Guided Waves ◽  
Joint Inversion ◽  
Waveform Inversion ◽  
Surface Modeling ◽  
Hydrocarbon Exploration ◽  
Surface Model ◽  
Near Surface ◽  
Velocity Inversion ◽  
Geophysical Imaging ◽  
Near Surface Geophysics

Despite technological and computational advances in geophysical imaging, near-surface geophysics continues to pose significant challenges in modeling and imaging the subsurface. Geoscientists from around the world attended the first and second editions of the SEG/DGS Near-surface Modeling and Imaging Workshop in 2014 and 2016 to address these challenges. A range of near-surface disciplines were represented from academia and industry, covering aspects of engineering and hydrocarbon exploration. The previous workshops explored emerging and underdeveloped techniques, including deep learning (machine learning), nonseismic methods, full-waveform inversion (FWI), and joint inversion. The necessity to further understand guided waves, anisotropy, velocity inversion, and the creation of an inclusive near-surface model was identified. The previous editions led to a greater understanding of the importance of knowledge sharing among various disciplines in modeling and imaging of the near surface.

Linearized tomographic inversion of first‐arrival times

Geophysics ◽  
1992 ◽  
Vol 57 (11) ◽  
pp. 1482-1492 ◽  
Author(s):  
James L. Simmons ◽  
Milo M. Backus
Keyword(s):  
Reference Model ◽  
Depth Range ◽  
Inversion Algorithm ◽  
Data Set ◽  
Tomographic Inversion ◽  
Seismic Reflection Data ◽  
Near Surface ◽  
Geometric Patterns ◽  
First Arrival ◽  
Thick Layers

A linearized tomographic‐inversion algorithm estimates the near‐surface slowness anomalies present in a conventional, shallow‐marine seismic reflection data set. First‐arrival time residuals are the data to be inverted. The anomalies are treated as perturbations relative to a known, laterally‐invariant reference velocity model. Below the sea floor the reference model varies smoothly with depth; consequently the first arrivals are considered to be diving waves. In the offset‐midpoint domain the geometric patterns of traveltime perturbations produced by the anomalies resemble hyperbolas. Based on simple ray theory, these geometric patterns are predictable and can be used to relate the unknown model to the data. The assumption of a laterally‐invariant reference model permits an efficient solution in the offset‐wavenumber domain which is obtained in a single step using conventional least squares. The tomographic image shows the vertical‐traveltime perturbations associated with the anomalies as a function of midpoint at a number of depths. As implemented, the inverse problem is inherently stable. The first arrivals sample the subsurface to a maximum depth of roughly 500 m (≈ one‐fifth of the spread length). The model is parameterized to consist of fifteen 20-m thick layers spanning a depth range of 80–380 m. One‐way vertical‐traveltime delays as large as 10 ms are estimated. Assuming that these time delays are distributed over the entire 20-m thick layers, velocities much slower than water velocity are implied for the anomalies. Maps of the tomographic images show the spatial location and orientation of the anomalies throughout the prospect for the upper 400 m. Each line is processed independently, and the results are corroborated to a high degree at the line intersections.

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