• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.5-16
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• 2007

The monitoring and investigation of underwater landslide help to understand its mechanism, increase the usefuless of design and construction and reduce the losses. This paper presents three high resolution geophysical techniques electrical resisitance, ultrasonic wave reflection imaging, and shear wave tomography conducted to determine the lab-scaled submerged landslide. Electrical resistance profiles of a soil mass obtained by an electrical resistance probe provide detailed information to assess the spatial distribution of the soil mass with milimetric resolution. An ultrasonic wave image obtained by recording the reflections from interfaces of different impedance materials permits detecting layers and landslide with submilimetric resolution. The pixel based image of immersed landslides is created by the inversion of the boundary information achieved from the traveling time of shear waves. The experimental results show that the ultrasonic wave imaging and the electrical resistance can provide complementary information; and their association with S-wave tomography image can produce a 3-D view of the underwater landslide. This study suggests that geophysical techniques may be effective tools for the detection of the underwater landslides and spatial distribution offshore.

• Geophysics and Geophysical Exploration
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• v.18 no.3
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• pp.143-153
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• 2015

Polymer flooding for enhancing hydrocarbon production injects into a reservoir polymer solution that is viscous. It is very important to monitor the behavior pattern of the polymer solution in order to evaluate the effectiveness of polymer flooding. To monitor the distribution of polymer solution and thus fluid substitution within the reservoir, we first construct seismic and resistivity rock physics models (RPMs), which are functions of reservoir parameters such as rocks and type of fluid, fluid saturation. For the seismic and resistivity RPMs, responses of seismic and electromagnetic (EM) tomography are numerically simulated as polymer injection, using two dimensional (2D) staggered-grid finite difference elastic modeling and 2.5D finite element EM modeling algorithms, respectively. In constructing RPM for EM tomography, three different reservoir rocks are considered: clean-sand, dispersed shale-sand, and sand-shale lamination rocks. The polymer solution is assumed to have 2 wt% of polymer as normally generated, while water is freshwater or saltwater. Further, neutron logging is also considered to check its sensitivity to polymer flooding. The techniques discussed in the paper are important in monitoring not only hydrocarbon but also geothermal reservoirs.

• Geophysics and Geophysical Exploration
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• v.11 no.4
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• pp.279-285
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• 2008

Korea Institute of Geoscience & Mineral Resources (KIGAM) carried out 2 dimensional multi-channel seismic surveys for Domi-Basin of east-southern part of Jeju Island, South Sea, Korea in 2007. The purpose of this survey is to investigate the structure of acoustic basement and the potential of energy resources in the Korean shelf. It is essential to produce fine stack and migration section to understand the structure of basement. However a basement can not be clearly defined where multiples exist between sea surface and seafloor. This study aimed at designing the optimal data processing parameter, especially to eliminate the peg-leg multiples. Main data processing procedure is composed of minimum phase predictive deconvolution, velocity analysis and Radon filter. We tested the efficiency of processing parameter from stack sections of each step. Our results confirmed that processing parameters are suitable for the seismic data of Domi-Basin.

• Geophysics and Geophysical Exploration
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• v.14 no.2
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• pp.164-175
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• 2011

Using natural electromagnetic (EM) fields at low frequencies, magnetotelluric (MT) surveys can investigate conductivity structures of the deep subsurface and thus are used to explore geothermal energy resources and investigate proper sites for not only geological $CO_2$ sequestration but also enhanced geothermal system (EGS). Moreover, marine MT data can be used for better interpretation of marine controlled-source EM data. In the interpretation of MT data, MT modeling schemes are important. This study improves a three dimensional (3D) MT modeling algorithm which uses edge finite elements. The algorithm computes magnetic fields by solving an integral form of Faraday's law of induction based on a finite difference (FD) strategy. However, the FD strategy limits the algorithm in computing vertical magnetic fields for a topographic model. The improved algorithm solves the differential form of Faraday's law of induction by making derivatives of electric fields, which are represented as a sum of basis functions multiplied by corresponding weightings. In numerical tests, vertical magnetic fields for topographic models using the improved algorithm overcome the limitation of the old algorithm. This study recomputes induction vectors and tippers for a 3D hill and valley model which were used for computation of the responses using the old algorithm.

• Geophysics and Geophysical Exploration
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• v.9 no.2
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• pp.139-147
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• 2006

We had conducted a numerical modeling to investigate seismic properties of gas hydrate with field parameters acquired over the East sea in 1998. We used a 2-D staggered grid finite difference method to generate synthetic elastic seismograms for multi-channel seismic survey, OBC (Ocean Bottom Cable) survey and VCS (Vertical Cable Seismic) survey. The results of this study showed that the method using staggered grid yielded stable results and could be used to seismic imaging. We could find out the high amplitude anomaly and the phase reversal phenomenon of reflection wave at interface between the gas hydrate layer and free gas layer such a BSR (Bottom Simulating Reflector) which is the evidence for existence of gas hydrate in seismic reflection data. And we computed the reflection coefficients at the incident angles corresponding to offset distance with the synthetic seismograms. The reflection coefficients acquired from the numerical modeling were nearly consistent with the reflection coefficient computed by Shuey's equation.

• Geophysics and Geophysical Exploration
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• v.23 no.1
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• pp.38-49
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• 2020

Full-waveform inversion (FWI) is an optimization process of fitting observed and modeled data to reconstruct high-resolution subsurface physical models. In acoustic FWI (AFWI), pressure data acquired using a marine streamer has mainly been used to reconstruct the subsurface P-wave velocity models. With recent advances in marine seismic-acquisition techniques, acquiring multi-component data in marine environments have become increasingly common. Thus, AFWI strategies must be developed to effectively use marine multi-component data. Herein, we proposed an AFWI strategy using horizontal and vertical particle-acceleration data. By analyzing the modeled acoustic data and conducting sensitivity kernel analysis, we first investigated the characteristics of each data component using AFWI. Common-shot gathers show that direct, diving, and reflection waves appearing in the pressure data are separated in each component of the particle-acceleration data. Sensitivity kernel analyses show that the horizontal particle-acceleration wavefields typically contribute to the recovery of the long-wavelength structures in the shallow part of the model, and the vertical particle-acceleration wavefields are generally required to reconstruct long- and short-wavelength structures in the deep parts and over the whole area of a given model. Finally, we present a sequential-inversion strategy for using the particle-acceleration wavefields. We believe that this approach can be used to reconstruct a reasonable P-wave velocity model, even when the pressure data is not available.

• Geophysics and Geophysical Exploration
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• v.26 no.3
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• pp.114-125
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• 2023

The physics-informed neural network (PINN) has been proposed to overcome the limitations of various numerical methods used to solve partial differential equations (PDEs) and the drawbacks of purely data-driven machine learning. The PINN directly applies PDEs to the construction of the loss function, introducing physical constraints to machine learning training. This technique can also be applied to wave equation modeling. However, to solve the wave equation using the PINN, second-order differentiations with respect to input data must be performed during neural network training, and the resulting wavefields contain complex dynamical phenomena, requiring careful strategies. This tutorial elucidates the fundamental concepts of the PINN and discusses considerations for wave equation modeling using the PINN approach. These considerations include spatial coordinate normalization, the selection of activation functions, and strategies for incorporating physics loss. Our experimental results demonstrated that normalizing the spatial coordinates of the training data leads to a more accurate reflection of initial conditions in neural network training for wave equation modeling. Furthermore, the characteristics of various functions were compared to select an appropriate activation function for wavefield prediction using neural networks. These comparisons focused on their differentiation with respect to input data and their convergence properties. Finally, the results of two scenarios for incorporating physics loss into the loss function during neural network training were compared. Through numerical experiments, a curriculum-based learning strategy, applying physics loss after the initial training steps, was more effective than utilizing physics loss from the early training steps. In addition, the effectiveness of the PINN technique was confirmed by comparing these results with those of training without any use of physics loss.

• Geophysics and Geophysical Exploration
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• v.19 no.1
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• pp.11-19
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• 2016

Two representative residual static methods of traveltime decomposition and stack-power maximization are discussed in terms of application to land seismic data. For the model data with synthetic shot/receiver statics (time shift) applied and random noises added, continuities of reflection event are much improved by stack-power maximization method, resulting the derived time-shifts approximately equal to the synthetic statics. Optimal parameters (maximum allowable shift, correlation window, iteration number) for residual statics are effectively chosen with diagnostic displays of CSP (common shot point) stack and CRP (common receiver point) stack as well as CMP gather. In addition to removal of long-wavelength time shift by refraction statics, prior to residual statics, processing steps of f-k filter, predictive deconvolution and time variant spectral whitening are employed to attenuate noises and thereby to minimize the error during the correlation process. The reflectors including horizontal layer of reservoir are more clearly shown in the variable-density section through repicking the velocities after residual statics and inverse NMO correction.

• Journal of the Korean earth science society
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• v.30 no.6
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• pp.692-698
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• 2009

Wavelet transform has been widely used in terms that it may overcome the shortcoming of conventional Fourier transform. Fourier transform has its difficulty to explain how the transformed domain, frequency, is related with time. Traditional semblance technique in Fourier transform was devised to compare two time series on the basis of their phase as a function of frequency. But this method is known not to work well for the non-stationary signal. In this study, we present two applications of the wavelet-based semblance method to geophysical data. Firstly, we show filtered geomagnetic signal remained with components of high correlation to each observatory. Secondly, highly correlated residual signal of gravity and magnetic survey data, which are also filtered by this semblance method, is present.

• Geophysics and Geophysical Exploration
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• v.23 no.4
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• pp.230-242
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• 2020

We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.