• Geophysics and Geophysical Exploration
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• v.5 no.2
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• pp.93-98
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• 2002

A three dimensional model for the orebody of an operating mine in Korea was constructed by using a program called '3-D Modeler'. The program allows the user to interactively construct a 3-D model of an orebody from its horizontal cross-sections. The 3-D Modeler is easily able to combine and display various spatial data for model construction. The result of modeling is strongly influenced by control points that correlate to the adjacent horizontal cross-sections. The control points are determined by comparing the geometrical shape of the adjacent cross-sections in conjunction with the geological features of the orebody. The resulting model can be evaluated in viewing the constructed object in three dimensional space or more closely evaluated by inspecting the cross-section. The model can iteratively be improved by modifying the shape of the cross-section and by using this new cross-section for the model building.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.241-251
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• 2007

The observed spectra from Odaesan earthquake were fitted to a point-source spectral model to evaluate the source spectrum and spatial features of the modelling error. The source spectrum was calculated by removing from the observed spectra the path and site dependent responses (Yun, 2007) that were previously revealed through an inversion process applied to a large accumulated spectral dataset. The stress drop parameter of one-corner Brune's ${\omega}^2$ source model fitted to the estimated source spectrum was well predicted by the scaling relation between magnitude and stress drop developed by Yun et al. (2006). In particular, the estimated spectrum was quite comparable to the two-corner source model that was empirically developed for recent moderate earthquakes occurring around the Korean Peninsula, which indicates that Odaesan earthquake is one of typical moderate earthquakes representative of Korean Peninsula. Other features of the observed spectra from Odaesan earthquake were also evaluated based on the commonly treated random error between the observed data and the estimated point-source spectral model. Radiation pattern of the error according to azimuth angle was found to be similar to the theoretical estimate. It was also observed that the spatial distribution of the errors was correlated with the geological map and the $Q_0$ map which are indicatives of seismic boundaries.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.89-97
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• 2021

Fault detection in seismic data is well suited to the application of machine learning algorithms. Accordingly, various machine learning techniques are being developed. In recent studies, machine learning models, which utilize synthetic data, are the particular focus when training with deep learning. The use of synthetic training data has many advantages; Securing massive data for training becomes easy and generating exact fault labels is possible with the help of synthetic training data. To interpret real data with the model trained by synthetic data, the synthetic data used for training should be geologically realistic. In this study, we introduce a method to generate realistic synthetic seismic data. Initially, reflectivity models are generated to include realistic fault structures, and then, a one-way wave equation is applied to efficiently generate seismic stack sections. Next, a migration algorithm is used to remove diffraction artifacts and random noise is added to mimic actual field data. A convolutional neural network model based on the U-Net structure is used to verify the generated synthetic data set. From the results of the experiment, we confirm that realistic synthetic data effectively creates a deep learning model that can be applied to field data.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.84-93
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• 2023

The supervised learning-based deep-learning seismic inversion techniques have demonstrated successful performance in synthetic data examples targeting small-scale areas. The supervised learning-based deep-learning seismic inversion uses time-domain wavefields as input and subsurface velocity models as output. Because the time-domain wavefields contain various types of wave information, the data size is considerably large. Therefore, research applying supervised learning-based deep-learning seismic inversion trained with a significant amount of field-scale data has not yet been conducted. In this study, we predict subsurface velocity models using Laplace-domain wavefields as input instead of time-domain wavefields to apply a supervised learning-based deep-learning seismic inversion technique to field-scale data. Using Laplace-domain wavefields instead of time-domain wavefields significantly reduces the size of the input data, thereby accelerating the neural network training, although the resolution of the results is reduced. Additionally, a large grid interval can be used to efficiently predict the velocity model of the field data size, and the results obtained can be used as the initial model for subsequent inversions. The neural network is trained using only synthetic data by generating a massive synthetic velocity model and Laplace-domain wavefields of the same size as the field-scale data. In addition, we adopt a towed-streamer acquisition geometry to simulate a marine seismic survey. Testing the trained network on numerical examples using the test data and a benchmark model yielded appropriate background velocity models.

• Korean Journal of Ecology and Environment
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• v.41 no.2
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• pp.155-165
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• 2008

The purpose of this study was to evaluate the ecosystem of Yeongsan Lake using physical, chemical, and biological indicators. We evaluated the integrative ecosystem health using Lentie Ecosystem Health Assessment (LEHA) model, Qualitative Health Evaluation Index (QHEI) model, and chemical water quality. The models of LEHA and QHEI were modified as 10 and 7 metries attributes, respectively. Also, we analyzed bioaccumulation of total mercury on various fish tissues by method of U.S. EPA 7473 using Direct Mercury Analyzer (Model DMA-80). Model values of LEHA model averaged 19 (range: $14{\sim}26$, n=15), which indicated a "poor" condition, and had slightly spatial variations. Values of the QHEI in the all sites averaged 72, which were judged as a "fair" to "good" condition. The QHEI values varied from 48 (fair condition) to 99 (good condition) and showed large longitudinal gradients between the upper and lower reach. Conductivity and salinity were increased from the up-lake to downlake reach. Analysis of total mercury in fish tissues showed that levels of total Hg ranged between 0.002 and $0.087\;mg\;L^{-1}$ depending on the types of tissues. Overall, the ecosystem health in the Yeongsan Lake was judged as a "poor" and the effects of bioaccumulation on the fish tissues were minor. Therefore, it is necessary to keep an efficient management for the lake environment to maintain their ecological health.

• Geophysics and Geophysical Exploration
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• v.2 no.4
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• pp.167-173
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• 1999

Since the resolution of the 2-D hole-to-hole seismic traveltime tomography is affected by the limited ray transmission angle, various methods were used to improve the resolution. Linear traveltime interpolation(LTI) ray tracing method was chosen for forward-modeling method. Inversion results using the LTI method were compared with those using the other ray tracing methods. As an inversion algorithm, SIRT method was used. In the iterative non-linear inversion method, the cost of ray tracing is quite expensive. To reduce the cost, each raypath was stored and the inversion was performed from this information. Using the proposed method, fast convergence was achieved. Inversion results are likely to be affected by the initial velocity guess, especially when the ray transmission angle was limited. To provide a good initial guess for the inversion, generalized regression neural network(GRNN) method was used. When the transmitted raypath angle is not limited or the geological model is very complex, the inversion results are not affected by initial velocity model very much. Since the raypath angles, however, are limited in most geophysical tomographic problems, the enhancement of resolution in tomography can be achieved by providing a proper initial velocity model by another inversion algorithm such as GRNN.

• Korean Journal of Ecology and Environment
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• v.40 no.3
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• pp.370-378
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• 2007

The objective of this research was to diagnose integrative ecological health in Bansuk Stream, one of the tributaries of Gap Stream, using the fish assemblage during July 2006${\sim}$April 2006. For this research, we selected six sampling sites and applied some approaches such as the Index of Biological Integrity (IBI), Qualitative Habitat Evaluation Index (QHEI), and necropsy-based Health Assessment Index (HAI). The stream health condition, based on the IBI values, averaged 24 (n= 18, range: $10{\sim}46$), indicating "poor${\sim}$fair" condition according to the criteria of US EPA (1993). Physical habitat condition, based on the QHEI, averaged 116 (n=6, range: $77{\sim}139$), indicating "fair${\sim}$good" condition. Values of IBI were more correlated with 3 metrics of instream cover ($M_1$, r=0.553, p=0.017, n=18), flow/velocity ($M_3$, r=0.627, p=0.005, n=18), and riffes/bends ($M_7$, r=0.631, p=0.005, n=18) than other metrics. Value of HAI in the control was zero (i.e., excellent condition), while the values in the T1 and T2 treatments were 5 (range: 0${\sim}$30) and 50 (range: 40${\sim}$80), respectively. The maximum values of IBI (46) were coincided with zero of HAI. Thus, these approaches seem to be a good tool for a diagnosis and evaluations of stream ecosystem health.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.41-49
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• 2005

We have developed a random heterogeneous velocity model with bimodal distribution in methane hydrate-bearing Bones. The P-wave well-log data have a von Karman type autocorrelation function and non-Gaussian distribution. The velocity histogram has two peaks separated by several hundred metres per second. A random heterogeneous medium with bimodal distribution is generated by mapping of a medium with a Gaussian probability distribution, yielded by the normal spectral-based generation method. By using an ellipsoidal autocorrelation function, the random medium also incorporates anisotropy of autocorrelation lengths. A simulated P-wave velocity log reproduces well the features of the field data. This model is applied to two simulations of elastic wane propagation. Synthetic reflection sections with source signals in two different frequency bands imply that the velocity fluctuation of the random model with bimodal distribution causes the frequency dependence of the Bottom Simulating Reflector (BSR) by affecting wave field scattering. A synthetic cross-well section suggests that the strong attenuation observed in field data might be caused by the extrinsic attenuation in scattering. We conclude that random heterogeneity with bimodal distribution is a key issue in modelling hydrate-bearing Bones, and that it can explain the frequency dependence and scattering observed in seismic sections in such areas.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.27-40
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• 2022

Every year landslides cause serious casualties and property damages around the world. As the accurate prediction of landslides is important to reduce the fatalities and economic losses, various approaches have been developed to predict them. Prediction methods can be divided into landslide susceptibility analysis, landslide hazard analysis and landslide risk analysis according to the type of the conditioning factors, the predicted level of the landslide dangers, and whether the expected consequence cased by landslides were considered. Landslide susceptibility analyses are mainly based on the available landslide data and consequently, they predict the likelihood of landslide occurrence by considering factors that can induce landslides and analyzing the spatial distribution of these factors. Various qualitative and quantitative analysis techniques have been applied to landslide susceptibility analysis. Recently, quantitative susceptibility analyses have predominantly employed the physically based model due to high predictive capacity. This is because the physically based approaches use physical slope model to analyze slope stability regardless of prior landslide occurrence. This approach can also reproduce the physical processes governing landslide occurrence. This review examines physically based landslide susceptibility analysis approaches.

• 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.