• Geophysics and Geophysical Exploration
• /
• v.20 no.3
• /
• pp.176-184
• /
• 2017

To interpret the seismic image precisely, random noises should be suppressed and the continuity of the image should be enhanced by using the appropriate smoothing techniques. Structure-Oriented Filter-Edge Preserving (SOF-EP) technique is one of the methods, that have been actively researched and used until now, to efficiently smooth seismic data while preserving the continuity of signal. This technique is based on the principle that diffusion occurs from large amplitude to small one. In a continuous structure such as a horizontal layer, diffusion or smoothing is operated along the layer, thereby increasing the continuity of layers and eliminating random noise. In addition, diffusion or smoothing across boundaries at discontinuous structures such as faults can be avoided by employing the continuity decision factor. Accordingly, the precision of the smoothing technique can be improved. However, in the case of the structure-oriented semblance technique, which has been used to calculate the continuity factor, it takes lots of time depending on the size of the filter and data. In this study, we first implemented the SOF-EP method and confirmed its effectiveness by applying it step by step to the field data. Next, we proposed and applied the corner response method which can efficiently calculate the continuity decision factor instead of structure-oriented semblance. As a result, we could confirm that the computation time can be reduced by about 6,000 times or more by applying the corner response method.

• Tunnel and Underground Space
• /
• v.23 no.5
• /
• pp.383-391
• /
• 2013

A number of true triaxial tests on rock samples have been conducted since the late 1960 and their results strongly suggest that the intermediate principal stress has a considerable effect on rock strength. Based on these experimental evidence, various 3-D rock failure criteria accounting for the effect of the intermediate principal stress have been proposed. Most of the 3-D failure criteria, however, are focused on the phenomenological description of the rock strength from the true triaxial tests, so that the associated strength parameters have little physical meaning. In order to confirm the likelihood that the intermediate principal stress dependency of rock strength is related to the presence of weak planes and their distribution to the preferred orientation, true triaxial tests are simulated with the transversely isotropic rock model. The conventional Mohr-Coulomb criterion is extended to its anisotropic version by incorporating the concept of microstructure tensor. With the anisotropic Mohr-Coulomb criterion, the critical plane approach is applied to calculate the strength of the transversely isotropic rock model and the orientation of the fracture plane. This investigation hints that the spatial distribution of microstructural planes with respect to the principal stress triad is closely related to the intermediate principal stress dependency of rock strength.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.48 no.10
• /
• pp.773-782
• /
• 2020

The development of joint FE models for deep learning neural network (DLNN)-based hybrid FEA is presented. Material models of bolts and bearings in the front axle of tractor, showing complex behavior induced by various tightening conditions, were replaced with DLNN models. Bolts are modeled as one-dimensional Timoshenko beam elements with six degrees of freedom, and bearings as three-dimensional solid elements. Stress-strain data were extracted from all elements after finite element analysis subjected to various load conditions, and DLNN for bolts and bearing were trained with Tensorflow. The DLNN-based joint models were implemented in the ABAQUS user subroutines where stresses from the next increment are updated and the algorithmic tangent stiffness matrix is calculated. Generalization of the trained DLNN in the FE model was verified by subjecting it to a new loading condition. Finally, the DLNN-based FEA for the front axle of the tractor was conducted and the feasibility was verified by comparing with results of a static structural experiment of the actual tractor.

• Tunnel and Underground Space
• /
• v.23 no.6
• /
• pp.493-505
• /
• 2013

This numerical study investigates seismicity and fault slip induced by fluid injection in deep geothermal reservoir with pre-existing fractures and fault. Particle Flow Code 2D is used with additionally implemented hydro-mechanical coupled fluid flow algorithm and acoustic emission moment tensor inversion algorithm. The output of the model includes spatio-temporal evolution of induced seismicity (hypocenter locations and magnitudes) and fault deformation (failure and slip) in relation to fluid pressure distribution. The model is applied to a case of fluid injection with constant rates changing in three steps using different fluid characters, i.e. the viscosity, and different injection locations. In fractured reservoir, spatio-temporal distribution of the induced seismicity differs significantly depending on the viscosity of the fracturing fluid. In a fractured reservoir, injection of low viscosity fluid results in larger volume of induced seismicity cloud as the fluid can migrate easily to the reservoir and cause large number and magnitude of induced seismicity in the post-shut-in period. In a faulted reservoir, fault deformation (co-seismic failure and aseismic slip) can occur by a small perturbation of fracturing fluid (<0.1 MPa) can be induced when the injection location is set close to the fault. The presented numerical model technique can practically be used in geothermal industry to predict the induced seismicity pattern and magnitude distribution resulting from hydraulic stimulation of geothermal reservoirs prior to actual injection operation.

• Economic and Environmental Geology
• /
• v.50 no.1
• /
• pp.61-71
• /
• 2017

The effects of joint density and size distribution on the hydrogeologic characteristics of jointed rock masses are addressed through numerical experiments based on the 2-D DFN (discrete fracture network) fluid flow analysis. Using two joint sets, a total of 51 2-D joint network system were generated with various joint density and size distribution. Twelve fluid flow directions were chosen every $30^{\circ}$ starting at $0^{\circ}$, and total of 612 $20m{\times}20m$ DFN blocks were prepared to calculate the directional block conductivity. Also, the theoretical block conductivity, principal conductivity tensor and average block conductivity for each generated joint network system were determined. The directional block conductivity and chance for the equivalent continuum behavior of the 2-D DFN system were found to increase with the increase of joint density or size distribution. However, the anisotropy of block hydraulic conductivity increases with the increase of density discrepancy between the joint sets, and the chance for the equivalent continuum behavior were found to decrease. The smaller the intersection angle of the two joint sets, the more the equivalent continuum behavior were affected by the change of joint density and size distribution. Even though the intersection angle is small enough that it is difficult to have equivalent continuum behavior, the chance for anisotropic equivalent continuum behavior increases as joint density or size distribution increases.

• Polymer(Korea)
• /
• v.31 no.3
• /
• pp.206-214
• /
• 2007

The theoretical study is developed for predicting the thermal expansion changes of composites which include complex inclusion, which is used three-dimensional ellipsoid model ($a_1>a_2>a_3$), which has two aspect ratios (the primary aspect ratio, $\rho_{\alpha}=a_1/a_3$ and the secondary aspect ratio, $\rho_{\beta}=a_1/a_2$). We can predict the feature of general thermal expansion factors by theoretical approach of matrix with aligned ellipsoidal inclusion using the Eshelby's equivalent tensor. The coefficients of longitudinal linear thermal expansion ${\alpha}_{11}$ decrease to those of inclusions, ${\alpha}_f$, as both aspect ratios increase. The coefficients of transverse linear thermal expansion of composites ${\alpha}_{33}$ initially increase and show the parabolic corves with maximum values, as the concentrations of filler increase. The coefficient of thermal expansion, ${\alpha}_{22}$ in the transverse direction decreases, as $\rho_{\alpha}$ increases, however, ${\alpha}_{22}$ increases as $\rho_{\beta}$ increases. The coefficient of linear thermal expansion of composites, ${\alpha}_{33}$ in the normal direction increases, as $\rho_{\alpha}$ increases, while ${\alpha}_{33}$ decreases as $\rho_{\beta}$ increases.

• Journal of Biomedical Engineering Research
• /
• v.25 no.1
• /
• pp.43-47
• /
• 2004

Interests in human brain functionality and its connectivity have much frown up. DTI (Diffusion tensor imaging) has been known as a non-invasive MR) technique capable of providing information on water diffusion in tissues and the organization of white matter tract. Thus. It can provide us the information on the direction of brain fiber tract and the connectivity among many important cortical regions which can not be examined by other anatomical or functional MRI techniques. In this study. was used the 24 bit color coding scheme on the IDL platform in the windows environment to visualize the orientation of major fiber tracts of brain such as main association, projection, commissural fibers and corticospinal tracts. We additionally implemented a color coding scheme for each directional component and FA (fractional anisotropy), and used various color tables for them to be visualized more definitely. Consequently we implemented a fancy and basic technique to visualize the directional information of fiber tracts efficiently and we confirmed the feasibility of the 24 bit color coding scheme in DTI by visualizing main fiber tracts.

• Journal of the Korean Magnetics Society
• /
• v.19 no.3
• /
• pp.113-119
• /
• 2009

$^{57}Fe_xCu_{1-x}O$(x = 0.0, 0.02) powders were prepared by sol-gel method and their crystallographic and magnetic hyperfine properties have been studied using X-ray diffraction and $M{\ddot{o}}ssbauer$ spectroscopy (MS). The crystal structure of the samples is found to be monoclinic without any secondary phases and their lattice parameters increase with increasing annealing temperature ($T_A$), which is attributed to an increase in oxygen-vacancy content. MS measurements at room temperature indicate that $Fe^{3+}$ ions substitute $Cu^{2+}$ sites and ferromagnetic phase grow with increasing $T_A$. Magnetic hyperfine and quadrupole interactions of $^{57}Fe_{0.02}Cu_{0.98}O$ ($T_A=500^{\circ}C$) in the antiferromagnetic state at 17 K have been studied, yielding the following results: $H_{hf}=426.94\;kOe$, ${\Delta}E_Q=-3.67\;mm/s$, I.S.=0.32 mm/s, ${\theta}=65^{\circ}$, ${\phi}=0^{\circ}$, and ${\eta}=0.6$.

• Journal of the Korean earth science society
• /
• v.30 no.2
• /
• pp.153-164
• /
• 2009

We examined the regional 1-D deep resistivity structure of the Korean Peninsula using MT data acquired at seven sites located in the Kyongsang Basin and Kyonggi Massif. At the sites located in the Kyongsang Basin, surrounding sea distorts observed MT response and hence this distortion, so called "sea effect", is corrected using an iterative tensor stripping method. The 1-D layered inversion results for the seven MT sites reveal 4 layered structure, which is composed of 1) near surface layer, 2) upper crust, 3) lower crust and upper mantle, and 4) asthenosphere from the surface downward. Conrad interface, which is a boundary between upper and lower crust, is distinctly identified beneath all the MT sites. Conrad interface depth is estimated to about be 17km in the Kyongsang Basin and about 12km in the Kyonggi Massif, while the upper crust of the Kyongsang Basin is about 5 times more resistive than that of the Kyonggi Massif. Finally, asthenosphere is inferred to exist below a depth of approximately 100km with a resistivity of 200-300 ohm-m.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.2
• /
• pp.204-208
• /
• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.