2D Joint Full Waveform Inversion of Elastic Surface and VSP Seismic Data in Time Domain

2011 ◽  
Author(s):  
I. Wenske ◽  
J. Mispel ◽  
D. Köhn
Keyword(s):  
Time Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Elastic Surface ◽  
Full Waveform

Source-independent time-domain vector-acoustic full-waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. R489-R505 ◽  
Author(s):  
Yu Zhong ◽  
Yangting Liu
Keyword(s):  
Particle Velocity ◽  
Time Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Adjoint State ◽  
Dual Sensor ◽  
Seismic Acquisition

Dual-sensor seismic acquisition systems that record the pressure and particle velocity allow the recording of the full-vector-acoustic (VA) wavefields. Most previous studies have focused on data-domain processing methods based on VA seismic data; whereas, few studies focused on using full-VA seismic data in full-waveform inversion (FWI). Conventional acoustic FWI only takes advantage of the pressure recordings to estimate the medium’s velocity model. Some artifact events will appear in the adjoint-state wavefields based on the conventional acoustic FWI method. These artifact events further reduce the accuracy of acoustic FWI. To simultaneously use pressure and vertical particle velocity recordings, we introduced a new time-domain VA FWI method. The VA FWI method can take advantage of directivity information contained in the VA seismic data. Thus, the adjoint-state wavefields based on VA FWI are more accurate than those from the conventional acoustic FWI method. In addition, we applied a convolution-based objective function to eliminate the effects of the source wavelet and implement a time-domain multiscale strategy in VA FWI. Synthetic examples are presented to demonstrate that VA FWI can improve the accuracy of acoustic FWI in the presence and absence of a free surface in the acoustic case. In addition, VA FWI does not significantly increase the computation and memory costs, but it has better convergence when compared with conventional acoustic FWI.

Robust source-independent elastic full-waveform inversion in the time domain

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. R29-R44 ◽  
Author(s):  
Qingchen Zhang ◽  
Hui Zhou ◽  
Qingqing Li ◽  
Hanming Chen ◽  
Jie Wang
Keyword(s):  
Objective Function ◽  
Time Domain ◽  
Seismic Data ◽  
Time Window ◽  
A Priori ◽  
Waveform Inversion ◽  
Accurate Estimation ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
The Time Domain

Accurate estimation of source wavelet is crucial in a successful full-waveform inversion (FWI); however, it cannot be guaranteed in the case of real seismic data. We have developed time-domain source-independent elastic FWI using the convolution-based objective function that was originally developed for acoustic FWI. We have applied a new time window on the reference traces in the objective function to suppress the noises induced by the convolution and crosscorrelation operations. Also, we have adopted [Formula: see text]-, Huber-, and hybrid-norm objective functions to improve the antinoise ability of our algorithm. We implemented a multiscale inversion strategy to conduct the tests with a quasi-Newton limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method to reduce the sensitivity to initial models and to improve the quality of inversion results. Synthetic tests verified that the new added time window can not only improve the inversion results, but also accelerate the convergence rate. Our method can be implemented successfully without a priori knowledge or accurate estimation of the source wavelet and can be more robust to Gaussian and spike noises, even for a Dirac wavelet. Finally, we applied our method to real seismic data. The similarity between the observed and modeled seismic data, the higher resolution of the migration image, and flatter common image gathers corresponding to the inverted models proved the relevance of our algorithm.

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.

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 Time domain viscoelastic full waveform inversion

2017 ◽  
Vol 209 (3) ◽  
pp. 1718-1734 ◽  
Author(s):  
Gabriel Fabien-Ouellet ◽  
Erwan Gloaguen ◽  
Bernard Giroux
Keyword(s):  
Time Domain ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Full Waveform
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