• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.203-213
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• 2022

To understand an underlying geological structure via seismic imaging, the velocity information of the subsurface medium is crucial. Although the full-waveform inversion (FWI) method is considered useful for estimating subsurface velocity models, 3D FWI needs a lot-of computing power and time. Herein, we compare the calculation efficiency and accuracy of frequency-domain 2D and 3D acoustic FWIs. Thereafter, we demonstrate that the artifacts from 2D approximation can be partially suppressed via frequency-domain 2D FWI by employing diffraction angle filtering (DAF). By applying DAF, which employs only big reflection angle components, the impact of noise and out-of-plane reflections can be reduced. Additionally, it is anticipated that the DAF can create long-wavelength velocity structures for 3D FWI and migration.

• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.49-53
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• 2011

• Geophysics and Geophysical Exploration
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• v.7 no.1
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• pp.19-24
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• 2004

Frequency-space domain full-wave tomography is a promising technique for delineating detailed subsurface structure with high resolution. However, this method requires criteria for the selection of a set of optimal temporal frequency components, to achieve stability in the sequence of inversion processes together with computational efficiency. We propose a method of selecting optimal temporal frequencies, based on wavenumber continuity. The proposed method is tested numerically and is shown to be able to select an optimal set of frequency components that are sufficient to image the anomalies.

• Economic and Environmental Geology
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• v.28 no.5
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• pp.481-485
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• 1995

This paper presents a new approaching method to determine the velocity and geometry of shallow subsurface from seismic refraction events. After picking the first breaks from seismic refraction data, we assume that field refraction seismogram can be replaced by the unit delta function having time shift of first break. Time curves are generated by shooting ray tracing. The partial derivatives seismogram for a damped least squares method is computed analytically at each step of the forward ray tracing. The technique is successfully tested on synthetic and real data. It has the advantage of real full waveform inversion, which is robust at low frequency band even if the initial guess is far from the true model.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.

• Geophysics and Geophysical Exploration
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• v.24 no.1
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• pp.6-19
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• 2021

Subsurface physical properties can be obtained and imaged by seismic exploration, and various algorithms have been developed for this purpose. In this regard, root mean square error (RMSE) has been widely used to quantitatively evaluate the accuracy of the developed algorithms. Although RMSE has the advantage of being numerically simple, it has limitations in assessing structural similarity. To supplement this, full-reference image quality assessment (FR-IQA) techniques, which reflect the human visual system, are being investigated. Therefore, we selected six FR-IQA techniques that could evaluate the obtained physical properties. In this paper, we used the full-waveform inversion, because the algorithm can provide the physical properties. The inversion results were applied to the six selected FR-IQA techniques using three benchmark models. Using salt models, it was confirmed that the inversion results were not satisfactory in some aspects, but the value of RMSE decreased. On the other hand, some FR-IQA techniques could definitely improve the evaluation.

• Geophysics and Geophysical Exploration
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• v.7 no.3
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• pp.191-196
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• 2004

Seismic waveform inversion can be solved by using the classical Gauss-Newton method, which needs to construct the huge Hessian by the directly computed Jacobian. The property of Hessian mainly depends upon a source and receiver aperture, a velocity model, an illumination Bone and a frequency content of source wavelet. In this paper, we try to invert the Marmousi seismic data by controlling the huge Hessian appearing in the Gauss-Newton method. Wemake the two kinds of he approximate Hessian. One is the banded Hessian and the other is the approximate Hessian with automatic gain function. One is that the 1st updated velocity model from the banded Hessian is nearly the same of the result from the full approximate Hessian. The other is that the stability using the automatic gain function is more improved than that without automatic gain control.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.214-216
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• 2022

Distributed acoustic sensing (DAS), an increasingly growing acquisition technique in the oil and gas exploration and seismology fields, has been used to record seismic signals using optical cables as receivers. With the development of imaging methods for DAS data, full waveform inversion (FWI) is been applied to DAS data to obtain high-resolution property models such as P- and S-velocity. However, because the DAS systems measure strain from the phase distortion between two points along optical cables, DAS data must be transformed from strain to particle velocity for FWI algorithms. In this study, a plane-wave FWI algorithm based on the relationship between strain and horizontal particle velocity in the plane-wave assumption is proposed to apply FWI to DAS data. Under the plane-wave assumption, strain equals the horizontal particle velocity, which is scaled by the velocity at the receiver position. This relationship was confirmed using a numerical experiment. Furthermore, 4-layer and modified Marmousi-2 velocity models were used to verify the applicability of the proposed FWI algorithm in various survey environments. The proposed FWI was implemented in land and marine survey environments and provided high-resolution P- and S-velocity models.

• 한국지구물리탐사학회:학술대회논문집
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• 2011.10a
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• pp.41-44
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• 2011

• 한국지구물리탐사학회:학술대회논문집
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• 2010.10a
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• pp.185-188
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• 2010