Frequency-domain reflection-based full waveform inversion for short-offset seismic data

2019 ◽  
Vol 164 ◽  
pp. 106-116
Author(s):  
Hyunggu Jun
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Full Waveform

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

scholarly journals Source‐independent full‐waveform inversion of seismic data

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 2010-2015 ◽  
Author(s):  
Ki Ha Lee ◽  
Hee Joon Kim
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Source Spectrum ◽  
Full Waveform Inversion ◽  
Source Inversion ◽  
Inversion Algorithm ◽  
Source Information ◽  
Full Waveform ◽  
Precise Knowledge

A rigorous full‐waveform inversion of seismic data has been a challenging subject, partly because of the lack of precise knowledge of the source. Since currently available approaches involve some form of approximations to the source, inversion results are subject to the quality and choice of the source information used. We propose a new full‐waveform inversion methodology that does not involve source spectrum information. Thus, potential inversion errors from source estimation can be eliminated. A gather of seismic traces is first Fourier transformed into the frequency domain, and a normalized wavefield is obtained for each trace in the frequency domain. Normalization is done with respect to the frequency response of a reference trace selected from the gather, so the complex‐valued normalized wavefield is dimensionless. The source spectrum is eliminated during the normalization procedure. With its source spectrum eliminated, the normalized wavefield lets us construct an inversion algorithm without the source information. The inversion algorithm minimizes misfits between a measured normalized wavefield and a numerically computed normalized wavefield. The proposed approach has been demonstrated successfully using a simple 2D scalar problem.

Frequency domain full-waveform inversion in a fluid-saturated poroelastic medium

2020 ◽  
Author(s):  
Qingjie Yang ◽  
Alison Malcolm
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Saturated Porous Media ◽  
Poroelastic Medium ◽  
Full Waveform ◽  
Trade Offs ◽  
Poroelasticity Theory ◽  
Two Parameter

Summary Determining subsurface properties is of fundamental importance in exploration seismic imaging. Poroelasticity theory provides an opportunity to extract quantitative fluid- and attenuation-dependent properties from seismic data. Following Pratt’s frequency-domain full-waveform-inversion (FWI) procedure and extending the basic FWI equations from the elastic case to the poroelastic case, we implement poroelastic FWI (PFWI) of fluid-saturated porous media. By analyzing the sensitivity kernels of poroelastic parameters, we explain the reason why some parameters are more difficult to recover than others. We also show the analytical and numerical radiation patterns based on which we predict the trade-offs among parameters. In numerical experiments, we invert two models to demonstrate the feasibility and effectiveness of the proposed PFWI and to verify our predictions about trade-offs for two-parameter PFWI. Finally, we discuss the various factors that influence the inversion results.

scholarly journals Frequency-Domain Full-Waveform Inversion Based on Tunnel Space Seismic Data

Engineering ◽  
2021 ◽  
Author(s):  
Mingyu Yu ◽  
Fei Cheng ◽  
Jiangping Liu ◽  
Daicheng Peng ◽  
Zhijian Tian
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Full Waveform

scholarly journals Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope

2021 ◽  
Author(s):  
Ehsan Jamali Hondori ◽  
Chen Guo ◽  
Hitoshi Mikada ◽  
Jin-Oh Park
Keyword(s):  
Seismic Data ◽  
Initial Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Control Measures ◽  
Japan Trench ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Time Migration ◽  
Shallow Sediments

AbstractFull-waveform inversion (FWI) of limited-offset marine seismic data is a challenging task due to the lack of refracted energy and diving waves from the shallow sediments, which are fundamentally required to update the long-wavelength background velocity model in a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to ensure that the observed and simulated waveforms kinematically fit within an error of less than half a wavelength to protect the FWI iterative local optimization scheme from cycle skipping. We use a migration-based velocity analysis (MVA) method, including a combination of the layer-stripping approach and iterations of Kirchhoff prestack depth migration (KPSDM), to build an accurate initial velocity model for the FWI application on 2D seismic data with a maximum offset of 5.8 km. The data are acquired in the Japan Trench subduction zone, and we focus on the area where the shallow sediments overlying a highly reflective basement on top of the Cretaceous erosional unconformity are severely faulted and deformed. Despite the limited offsets available in the seismic data, our carefully designed workflow for data preconditioning, initial model building, and waveform inversion provides a velocity model that could improve the depth images down to almost 3.5 km. We present several quality control measures to assess the reliability of the resulting FWI model, including ray path illuminations, sensitivity kernels, reverse time migration (RTM) images, and KPSDM common image gathers. A direct comparison between the FWI and MVA velocity profiles reveals a sharp boundary at the Cretaceous basement interface, a feature that could not be observed in the MVA velocity model. The normal faults caused by the basal erosion of the upper plate in the study area reach the seafloor with evident subsidence of the shallow strata, implying that the faults are active.

Application of the variable projection scheme for frequency-domain full-waveform inversion

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. R249-R257 ◽  
Author(s):  
Maokun Li ◽  
James Rickett ◽  
Aria Abubakar
Keyword(s):  
Frequency Domain ◽  
Waveform Inversion ◽  
Simulated Data ◽  
Calibration Procedure ◽  
Full Waveform Inversion ◽  
Inversion Algorithm ◽  
Unknown Parameters ◽  
Variable Projection ◽  
Full Waveform ◽  
Data Calibration

We found a data calibration scheme for frequency-domain full-waveform inversion (FWI). The scheme is based on the variable projection technique. With this scheme, the FWI algorithm can incorporate the data calibration procedure into the inversion process without introducing additional unknown parameters. The calibration variable for each frequency is computed using a minimum norm solution between the measured and simulated data. This process is directly included in the data misfit cost function. Therefore, the inversion algorithm becomes source independent. Moreover, because all the data points are considered in the calibration process, this scheme increases the robustness of the algorithm. Numerical tests determined that the FWI algorithm can reconstruct velocity distributions accurately without the source waveform information.

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