Application of full waveform inversion algorithms to seismic data lacking low-frequency information from a simple starting model

2018 ◽  
Vol 49 (4) ◽  
pp. 434-449 ◽  
Author(s):  
Hyunggu Jun ◽  
Jungkyun Shin ◽  
Changsoo Shin
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Information ◽  
Full Waveform ◽  
Starting Model

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 Elastic envelope inversion using multicomponent seismic data without low frequency

2014 ◽  
Vol 1 (2) ◽  
pp. 1757-1802
Author(s):  
C. Huang ◽  
L. Dong ◽  
Y. Liu ◽  
B. Chi
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Synthetic Data ◽  
Low Frequency ◽  
Velocity Model ◽  
Inversion Method ◽  
Full Waveform Inversion ◽  
S Wave ◽  
Full Waveform ◽  
Multicomponent Data

Abstract. Low frequency is a key issue to reduce the nonlinearity of elastic full waveform inversion. Hence, the lack of low frequency in recorded seismic data is one of the most challenging problems in elastic full waveform inversion. Theoretical derivations and numerical analysis are presented in this paper to show that envelope operator can retrieve strong low frequency modulation signal demodulated in multicomponent data, no matter what the frequency bands of the data is. With the benefit of such low frequency information, we use elastic envelope of multicomponent data to construct the objective function and present an elastic envelope inversion method to recover the long-wavelength components of the subsurface model, especially for the S-wave velocity model. Numerical tests using synthetic data for the Marmousi-II model prove the effectiveness of the proposed elastic envelope inversion method, especially when low frequency is missing in multicomponent data and when initial model is far from the true model. The elastic envelope can reduce the nonlinearity of inversion and can provide an excellent starting model.

High-resolution Radon preconditioning for full-waveform inversion of land seismic data

2017 ◽  
Vol 5 (4) ◽  
pp. SR23-SR33 ◽  
Author(s):  
Xin Cheng ◽  
Kun Jiao ◽  
Dong Sun ◽  
Zhen Xu ◽  
Denes Vigh ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Velocity Fields ◽  
Surface Velocity ◽  
Surface Model ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Full Waveform

Over the past decade, acoustic full-waveform inversion (FWI) has become one of the standard methods in the industry to construct high-resolution velocity fields from the seismic data acquired. While most of the successful applications are for marine acquisition data with rich low-frequency diving or postcritical waves at large offsets, the application of acoustic FWI on land data remains a challenging topic. Land acoustic FWI application faces many severe difficulties, such as the presence of strong elastic effects, large near-surface velocity contrast, and heterogeneous, topography variations, etc. In addition, it is well-known that low-frequency transmitted seismic energy is crucial for the success of FWI to overcome sensitivity to starting velocity fields; unfortunately, those are the parts of the data that suffer the most from a low signal-to-noise ratio (S/N) in land acquisition. We have developed an acoustic FWI application on a land data set from North Kuwait, and demonstrated our solutions to mitigate some of the challenges posed by land data. More specifically, we have developed a semblance-based high-resolution Radon (HR-Radon) inversion approach to enhance the S/N of the low-frequency part of the FWI input data and to ultimately improve the convergence of the land FWI workflow. To mitigate the impact of elastic effects, we included only the diving and postcritical early arrivals in the waveform inversion. Our results show that, with the aid of HR-Radon preconditioning and a carefully designed workflow, acoustic FWI has the ability to derive a reliable high-resolution near-surface model that could not be otherwise recovered through traditional tomographic methods.

Data-driven low-frequency signal recovery using deep-learning predictions in full-waveform inversion

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. A37-A43
Author(s):  
Jinwei Fang ◽  
Hui Zhou ◽  
Yunyue Elita Li ◽  
Qingchen Zhang ◽  
Lingqian Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
High Frequency ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Data Driven ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Recovery Method ◽  
Full Waveform

The lack of low-frequency signals in seismic data makes the full-waveform inversion (FWI) procedure easily fall into local minima leading to unreliable results. To reconstruct the missing low-frequency signals more accurately and effectively, we have developed a data-driven low-frequency recovery method based on deep learning from high-frequency signals. In our method, we develop the idea of using a basic data patch of seismic data to build a local data-driven mapping in low-frequency recovery. Energy balancing and data patches are used to prepare high- and low-frequency data for training a convolutional neural network (CNN) to establish the relationship between the high- and low-frequency data pairs. The trained CNN then can be used to predict low-frequency data from high-frequency data. Our CNN was trained on the Marmousi model and tested on the overthrust model, as well as field data. The synthetic experimental results reveal that the predicted low-frequency data match the true low-frequency data very well in the time and frequency domains, and the field results show the successfully extended low-frequency spectra. Furthermore, two FWI tests using the predicted data demonstrate that our approach can reliably recover the low-frequency data.

scholarly journals Multisource Seismic Full Waveform Inversion of Metal Ore Bodies

Minerals ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 4
Author(s):  
Fengjiao Zhang ◽  
Pan Zhang ◽  
Zhuo Xu ◽  
Xiangbo Gong ◽  
Liguo Han
Keyword(s):  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Mining Area ◽  
Seismic Exploration ◽  
Full Waveform Inversion ◽  
Metal Mining ◽  
Full Waveform ◽  
Metal Mine ◽  
Ore Bodies

The seismic exploration method could explore deep metal ore bodies (depth > 1000 m). However, it is difficult to describe the geometry of the complex metal ore body accurately. Seismic full waveform inversion is a relatively new method to achieve accurate imaging of subsurface structures, but its success requires better initial models and low-frequency data. The seismic data acquired in the metal mine area is usually difficult to meet the requirements of full waveform inversion. The passive seismic data usually contains good low frequency information. In this paper, we use both passive and active seismic datasets to improve the full waveform inversion results in the metal mining area. The results show that the multisource seismic full waveform inversion could obtain a suitable result for high-resolution seismic imaging of metal ore bodies.

Building good starting models for full-waveform inversion using adaptive matching filtering misfit

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. U61-U72 ◽  
Author(s):  
Hejun Zhu ◽  
Sergey Fomel
Keyword(s):  
Least Squares ◽  
Waveform Inversion ◽  
Basin Of Attraction ◽  
Low Frequency ◽  
Quadratic Function ◽  
Full Waveform Inversion ◽  
Local Minima ◽  
Full Waveform ◽  
Gradient Based ◽  
Starting Model

We have proposed a misfit function based on adaptive matching filtering (AMF) to tackle challenges associated with cycle skipping and local minima in full-waveform inversion (FWI). This AMF is designed to measure time-varying phase differences between observations and predictions. Compared with classical least-squares waveform differences, our misfit function behaves as a smooth, quadratic function with a broad basin of attraction. These characters are important because local gradient-based optimization approaches used in most FWI schemes cannot guarantee convergence toward true models if misfit functions include local minima or if the starting model is far away from the global minimum. The 1D and 2D synthetic experiments illustrate the advantages of the proposed misfit function compared with the classical least-squares waveform misfit. Furthermore, we have derived adjoint sources associated with the proposed misfit function and applied them in several 2D time-domain acoustic FWI experiments. Numerical results found that the proposed misfit function can provide good starting models for FWI, particularly when low-frequency signals are absent in recorded data.

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.

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