Prestack waveform inversion of three-component, two-azimuth surface seismic data for azimuthally dependent anisotropic parameters using a parallelized nondominated sorting genetic algorithm

The choice of an initial model for seismic waveform inversion is important. In matured exploration areas with adequate well control, we can generate a suitable initial model using well information. However, in new areas where well control is sparse or unavailable, such an initial model is compromised and/or biased by the regions with more well controls. Even in matured exploration areas, if we use time-lapse seismic data to predict dynamic reservoir properties, an initial model, that we obtain from the existing preproduction wells could be incorrect. In this work, we outline a new methodology and workflow for a nonlinear prestack isotropic elastic waveform inversion. We call this method a data driven inversion, meaning that we derive the initial model entirely from the seismic data without using any well information. By assuming a locally horizonal stratification for every common midpoint and starting from the interval P-wave velocity, estimated entirely from seismic data, our method generates pseudo wells by running a two-pass one-dimensional isotropic elastic prestack waveform inversion that uses the reflectivity method for forward modeling and genetic algorithm for optimization. We then use the estimated pseudo wells to build the initial model for seismic inversion. By applying this methodology to real seismic data from two different geological settings, we demonstrate the usefulness of our method. We believe that our new method is potentially applicable for subsurface characterization in areas where well information is sparse or unavailable. Additional research is however necessary to improve the compute-efficiency of the methodology.

Download Full-text

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

Pure and Applied Geophysics ◽

10.1007/s00024-021-02727-w ◽

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.

Download Full-text

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

Journal of Geophysics and Engineering ◽

10.1093/jge/gxz062 ◽

2019 ◽

Vol 16 (6) ◽

pp. 1017-1031 ◽

Cited By ~ 5

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.

Download Full-text

Treatment and Assessment of Nonlinear Seismic Data by a Genetic Algorithm Based Neural Network Model

International Journal of Nonlinear Sciences and Numerical Simulation ◽

10.1515/ijnsns.2009.10.1.45 ◽

2009 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

T. Kerh ◽

Y. Chan ◽

D. Gunaratnam

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Network Model ◽

Neural Network Model ◽

Seismic Data

Download Full-text

An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽

10.1155/2018/3051854 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 7

Author(s):

Jing Xiao ◽

Jing-Jing Li ◽

Xi-Xi Hong ◽

Min-Mei Huang ◽

Xiao-Min Hu ◽

...

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Portfolio Optimization ◽

Optimization Problem ◽

Experimental Comparison ◽

Software Project ◽

Project Portfolio ◽

Multiobjective Evolutionary Algorithms ◽

Reference Distance ◽

Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

Download Full-text