Full-Waveform Modeling for Solving the Direct and Inverse Seismic Problems in the Tomsk Region

2020 ◽  
Author(s):  
I. Romanchenko ◽  
K. Starkov ◽  
A. Ivlev ◽  
M. Tarakanovsky ◽  
D. Litvichenko ◽  
...  
Keyword(s):  
Waveform Modeling ◽  
Full Waveform ◽  
Tomsk Region

On full-waveform modeling and inversion of fiber-optic VSP data

2017 ◽  
Author(s):  
Olga Podgornova ◽  
Scott Leaney ◽  
Smaine Zeroug ◽  
Lin Liang
Keyword(s):  
Fiber Optic ◽  
Waveform Modeling ◽  
Full Waveform ◽  
Vsp Data ◽  
And Inversion

scholarly journals 2D full-waveform modeling of seismic waves in layered karstic media

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. T25-T34 ◽  
Author(s):  
Yingcai Zheng ◽  
Adel H. Malallah ◽  
Michael C. Fehler ◽  
Hao Hu
Keyword(s):  
Seismic Waves ◽  
Finite Size ◽  
Spectral Element ◽  
Seismic Wave Propagation ◽  
Complex Model ◽  
Waveform Modeling ◽  
Frequency Space ◽  
Full Waveform ◽  
Matrix Scheme ◽  
Element Method

We have developed a new propagator-matrix scheme to simulate seismic-wave propagation and scattering in a multilayered medium containing karstic voids. The propagator matrices can be found using the boundary element method. The model can have irregular boundaries, including arbitrary free-surface topography. Any number of karsts can be included in the model, and each karst can be of arbitrary geometric shape. We have used the Burton-Miller formulation to tackle the numerical instability caused by the fictitious resonance due to the finite size of a karstic void. Our method was implemented in the frequency-space domain, so frequency-dependent [Formula: see text] can be readily incorporated. We have validated our calculation by comparing it with the analytical solution for a cylindrical void and to the spectral element method for a more complex model. This new modeling capability is useful in many important applications in seismic inverse theory, such as imaging karsts, caves, sinkholes, and clandestine tunnels.

Pseudo-full-waveform inversion of borehole GPR data using stochastic tomography

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. J43-J51 ◽  
Author(s):  
Erwan Gloaguen ◽  
Bernard Giroux ◽  
Denis Marcotte ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Waveform Inversion ◽  
Waveform Modeling ◽  
Arrival Times ◽  
Covariance Functions ◽  
Full Waveform ◽  
Amplitude Data ◽  
Ground Penetrating ◽  
Difference Time ◽  
Selection Of ◽  
Do So

Electromagnetic full-waveform tomography is computer intensive and requires good knowledge of antenna characteristics and ground coupling. As a result, ground-penetrating-radar tomography usually uses only the first wavelet’s arrival time and amplitude data. We propose to improve the classical approach by inverting multiple slowness and attenuation fields using stochastic tomography. To do so, we model the slowness and attenuation covariance functions to generate geostatistical simulations that are conditional to the arrival times, amplitudes, slowness, and attenuation observed along boreholes. We combine slowness and attenuation fields to compute conductivity and permittivity fields from which we model synthetic radar traces using a finite-difference time-domain full-waveform algorithm. Modeled traces that best match the measured ones correspond to the computed conductivity and permittivity fields that correlate best with the true physical properties of the ground. We apply the method to a synthetic example with known electric properties. We show that a combination of stochastic tomography and full-waveform modeling allows a better selection of permittivity fields close to the reference field, at a reasonable computing cost.

scholarly journals Scale properties of the seismic wavefield perspectives for full-waveform matching

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. A37-A44 ◽  
Author(s):  
Margherita Maraschini ◽  
Daniele Boiero ◽  
Sebastiano Foti ◽  
Laura Valentina Socco
Keyword(s):  
Equations Of Motion ◽  
Waveform Inversion ◽  
Computational Time ◽  
Model Parameters ◽  
Waveform Modeling ◽  
Velocity Models ◽  
Full Waveform ◽  
Scale Properties ◽  
And Inversion ◽  
Seismic Wavefield

Starting from the nondimensionalization of equations of motion we partition the set of the velocity models in equivalence classes, such that the full waveform of an element in a given class can be calculated from the full waveform of any other element in the same class by scaling model parameters. We give a formal derivation of the seismic wavefield scale properties and we prove their capability through the use of numerical examples. Besides this, we introduce how the scale properties can be used to save computational time in full waveform modeling and inversion. In forward modeling we can use them for the calculation of the full waveform of any model in the same equivalence class of a model whose full waveform has been previously calculated. In full waveform inversion, scale properties can be used for full waveform matching: Given an experimental seismogram and a synthetic one, we can choose, in the same class of the synthetic model, another element whose waveform is closer to the experimental one.

The near-surface velocity reversal and its detection via unsupervised machine learning

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. U55-U63
Author(s):  
Mengyao Sun ◽  
Jie Zhang
Keyword(s):  
Machine Learning ◽  
Real Data ◽  
Automated Detection ◽  
Surface Velocity ◽  
Unsupervised Machine Learning ◽  
Detection Scheme ◽  
Near Surface ◽  
Waveform Modeling ◽  
Full Waveform ◽  
Velocity Reversal

In land seismic data processing, picking the first arrivals and imaging the near-surface velocity structures are important tasks. However, in many areas, the near-surface weathering layer includes high-velocity reversals, causing the first arrivals to exhibit shingling effects, which are difficult for picking at the far offset. We have used an acoustic full-waveform modeling method in a multilayered half-space to simulate first arrivals with the velocity reversal. Numerical tests indicate that under certain conditions, shingling occurs if the seismic wave propagates through a thin velocity reversal layer embedded in the shallow structures. Detection of shingling is essential for the selection of valid near-surface imaging solutions, such as first-arrival refraction, or waveform solutions for the appropriate areas. We find that an automated detection scheme that uses unsupervised machine learning can help identify the velocity reversal. We test the method on synthetic and real data, and the testing shows that the automated detection result matches our visual judgment well. After the automated detection, appropriate inversion approaches can be applied to corresponding areas.

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