The first‐arrival tomographic inversion and its application to identify thick near‐surface structures

2008 ◽  
Author(s):  
Chen Baofu ◽  
Xiong Dingyu ◽  
Ren Xiaoqiao ◽  
Cheng Chunhua
Keyword(s):  
Surface Structures ◽  
Tomographic Inversion ◽  
Near Surface ◽  
First Arrival

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.

Imaging permafrost velocity structure using high resolution 3D seismic tomography

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. B187-B198 ◽  
Author(s):  
Kumar Ramachandran ◽  
Gilles Bellefleur ◽  
Tom Brent ◽  
Michael Riedel ◽  
Scott Dallimore
Keyword(s):  
High Resolution ◽  
Velocity Structure ◽  
Velocity Model ◽  
Water Bodies ◽  
Unfrozen Water ◽  
3D Seismic ◽  
Tomographic Inversion ◽  
Near Surface ◽  
First Arrival ◽  
Velocity Variations

A 3D seismic survey (Mallik 3D), covering [Formula: see text] in the Mackenzie Delta area of Canada’s north, was conducted by industry in 2002. Numerous lakes and marine inundation create a complex near-surface structure in the permafrost terrain. Much of the near subsurface remains frozen but significant melt zones exist particularly from perennially unfrozen water bodies. This results in an irregular distribution of permafrost ice creating a complex pattern of low and high frequency near-surface velocity variations which induce significant traveltime distortions in surface seismic data. A high resolution 3D traveltime tomography study was employed to map the permafrost velocity structure utilizing first-arrival traveltimes picked from 3D seismic shot records. Approximately 900,000 traveltime picks from 3167 shots were used in the inversion. Tomographic inversion of the first-arrival traveltimes resulted in a smooth velocity model for the upper 200 m of the subsurface. Ray coverage in the model is excellent down to 200 m providing effective control for estimating velocities through tomographic inversion. Resolution tests conducted through horizontal and vertical checkerboard tests confirm the robustness of the velocity model in detailing small scale velocity variations. Well velocities were used to validate tomographic velocities. The tomographic velocities do not show systematic correlation with well velocities. The velocity model clearly images the permafrost velocity structure in lateral and vertical directions. It is inferred from the velocity model that the permafrost structure in the near subsurface is discontinuous. Extensions of surface water bodies in depth, characterized by low P-wave velocities, are well imaged by the velocity model. Deep lakes with unfrozen water, inferred from the tomographic velocity model, correlate with areas of strong amplitude blanking and frequency attenuation observed in processed reflection seismic stack sections.

scholarly journals First arrival Q tomography based on an adjoint-state method

2020 ◽  
Author(s):  
Xinwei Huang ◽  
Zhenbo Guo ◽  
Huawei Zhou ◽  
Yubo Yue
Keyword(s):  
Time Domain ◽  
Spectral Ratio ◽  
Velocity Model ◽  
Similarity Coefficients ◽  
Tomographic Inversion ◽  
Near Surface ◽  
Adjoint State ◽  
Q Tomography ◽  
First Arrival ◽  
The Time Domain

Abstract Under the assumption of invariant ray path in a weakly dissipative (high quality factor Q) subsurface medium, a tomographic inversion approach composed of two cascading applications of first arrival traveltime and Q tomography is proposed for compensating amplitude loss caused by near-surface anomalies, such as unconsolidated soils or the overburden gas cloud. To improve the computational efficiency, these two related tomography methods were adopted with an adjoint-state technique. First, arrival traveltime tomography will be performed to provide an inverted velocity model as one of the inputs for the following first arrival Q tomography. Then, the synthetic first break generated by the inverted velocity model will be used as a stable guidance of accessing the scopes of first arrival waveforms in the time domain where the potential attenuated time information is contained. The attenuated time will be estimated through a logarithmic spectral ratio linear regression corresponding to frequency-dependent propagation responses of different wave types. All these estimated attenuated times will be applied with reference signals to generate synthetic attenuated seismic data in the time domain, and their discrepancies with real data will be evaluated using similarity coefficients. The ones with larger values will be selected as optimal attenuated time inputs for the following Q tomographic inversion. Examples of both synthetic and field data reveal the feasibility and potential of this method.

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.

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