• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.32-41
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• 1999

In this paper, an inversion method using chirp signals and two near field receivers is proposed. Inversion problems can be formulated into the probabilistic models composed of signals, a forward model and noise. Forward model to simulate chirp signals is chosen to be the source-wavelet-convolution planewave modeling method. The solution of the inversion problem is defined by a posteriori pdf. The wavelet matching technique, using weighted least-squares fitting, estimates the sediment sound-speed and thickness on which determination of the ranges for a priori uniform distribution is based. The genetic algorithm can be applied to a global optimization problem to find a maximum a posteriori solution for determined a priori search space. Here the object function is defined by an L₂norm of the difference between measured and modeled signals. The observed signals can be separated into a set of two signals reflected from the upper and lower boundaries of a sediment. The separation of signals and successive applications of the genetic algorithm optimization process reduce the search space, therefore improving the inversion results. Not only the marginal pdf but also the statistics are calculated by numerical evaluation of integrals using the samples selected during importance sampling process of the genetic algorithm. The examples applied here show that, for synthetic data with noise, it is possible to carry out an inversion for sedimentary layers using the proposed inversion method.

• Journal of Korean Society on Water Environment
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• v.31 no.3
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• pp.272-285
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• 2015

Accurate assessment of chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing is challenging due to the optical complexity of case 2 waters. and the inherent optical properties (IOPs) of natural waters are the most significant factors affecting light propagation within water columns, and thus play indispensable roles on estimation of Chl-a concentrations. Despite its importance, no IOPs retrieval model was specifically developed for inland water bodies, although significant efforts were made on oceanic inversion models. So we have applied and validated a recently developed Red-NIR three-band model and an IOPs Inversion Model for estimating Chl-a concentration and deriving inland water IOPs in Lake Uiam. Three band and IOPs based Chl-a estimation model accuracy was assessed with samples collected in different seasons. The results indicate that this models can be used to accurately retrieve Chl-a concentration and absorption coefficients. For all datasets the determination coefficients of the 3-band models versus Chl-a concentration ranged 0.65 and 0.88 and IOPs based model versus Chl-a concentration varied from 0.73 to 0.83 respectively. and Comparison between 3-band and IOPs based models showed significant performance with decrease of root mean square error from 18% to 33.6%. The results of this study provides the potential of effective methods for remote monitoring and water quality management in turbid inland water bodies using hyper-spectral remote sensing.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.340-343
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• 2003

This study presents a practical procedure for the Bayesian inversion of geophysical data by Markov chain Monte Carlo (MCMC) sampling and geostatistics. We have applied geostatistical techniques for the acquisition of prior model information, and then the MCMC method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter. This approach provides an effective way to treat Bayesian inversion of geophysical data and reduce the non-uniqueness by incorporating various prior information.

• Geophysics and Geophysical Exploration
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• v.10 no.1
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• pp.60-68
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• 2007

Conventional analysis of spectral induced polarization (SIP) data consists of measuring impedances over a range of frequencies, followed by spectral analysis to estimate spectral parameters. For the quantitative and accurate estimation of subsurface SIP parameter distribution, however, a sophisticated and stable inversion technique is required. In this study, we have developed a two-step inversion approach to obtain the two-dimensional distribution of SIP parameters. In the first inversion step, all the SIP data measured over a range of frequencies are simultaneously inverted, adopting cross regularisation of model complex resistivities at each frequency. The cross regularisation makes it possible to enhance the noise characteristics of the inversion by imposing a strong assumption, that complex resistivities should show similar characteristics over a range of frequencies. In numerical experiments, we could verify that our inversion approach successfully reduced inversion artefacts. As a second step, we have also developed an inversion algorithm to obtain SIP parameters based on the Cole-Cole model, in which frequency-dependent complex resistivities from the first step are inverted to obtain a two-dimensional distribution of SIP parameters. In numerical tests, the SIP parameter images showed a fairly good match with the exact model, which suggests that SIP imaging can provide a very useful subsurface image to complement resistivity.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.4
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• pp.533-542
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• 2000

Analytical experiments to determine the line-of-sight temperature distribution is conducted by using spectral radiation intensities. For this study, fourteen narrow bands of $25cm^{-1}$ interval in $CO_2\;4.3{\mu}m$ band ($2,050cm^{-1}$ to $2375cm^{-1}$) are selected. The applied system is a one-dimensional gas slab filled with 100% $CO_2$ gas at 1 atm. Two types of temperature profile are tested; parabolic and boundary layer types. Three kinds of radiation calculation are used in the iteration procedure for the temperature inversion; LBL(Line by Line), SNB(Statistical Narrow Band) and WNB(WSGGM. based Narrow Band) models. The LBL solution shows perfect agreement while some error of temperature prediction is caused by radiation modeling error when using SNB and WNB models. The inversion result shows that the WNB model may be used more accurately in spectral remote sensing techniques than the traditional SNB 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.26 no.4
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• pp.268-274
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• 2023

Finite source inversion is performed with a Green's function matrix and geodetic coseismic displacement. Conventionally, the Green's function matrix is constructed using the Okada model (Okada, 1985). However, for more realistic earthquake simulations, recent research has widely adopted the physics-based model, which can consider various material properties such as elasticity, viscoelasticity, and elastoplasticity. We used the physics-based software PyLith, which is suitable for earthquake modeling. However, the PyLith does not provide a mesh generator, which makes it difficult to perform finite source inversions that require numerous subfaults and observation points within the model. Therefore, in this study, we developed CPInterface (COMSOL-PyLith Interface) to improve the convenience of finite source inversion by combining the processes of creating a numerical model including sub-faults and observation points, simulating earthquake modeling, and constructing a Green's function matrix. CPInterface combines the grid generator of COMSOL with PyLith to generate the Green's function matrix automatically. CPInterface controls model and fault information with simple parameters. In addition, elastic subsurface anomalies and GPS observations can be placed flexibly in the model. CPInterface is expected to enhance the accessibility of physics-based finite source inversions by automatically generating the Green's function matrix.

• Geophysics and Geophysical Exploration
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• v.20 no.1
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• pp.33-42
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• 2017

Recently, the grounded electrical-source airborne transient electromagnetic (GREATEM) system with high power source was introduced to achieve deeper investigation depth and to overcome high noise level. Although the GREATEM is a transient electromagnetic system using a long grounded wire as the transmitter, GREATEM data have been interpreted with 1D earth models because 2D or 3D modeling and inversion of vast airborne data are complicated and expensive to calculate. Generally, 1D inversion is subsequently applied to every survey point and combining 1D images together forms the stitched conductivity-depth image. However, the stitched models often result in abrupt variations in neighboring models. To overcome this problem, laterally constrained inversion (LCI) has been developed in inversion of ATEM data, which can yield layered sections with lateral smooth transitions. In this study, we analysed the GREATEM data through 1D numerical modeling for a curved grounded wire source. Furthermore, we developed a laterally constrained inversion scheme for continuous GREATEM data based on a layered earth model. All 1D data sets and models are inverted as one system, producing layered sections with lateral smooth transitions. Applying the developed LCI technique to the GREATEM data, it was confirmed that the laterally constrained inversion can provide laterally smooth model sections that reflect the layering of the survey area effectively.

• Electrical & Electronic Materials
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• v.9 no.2
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• pp.174-179
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• 1996

We present a new physically based analytical equation for electron effective mobility in MOS inversion layers. The new semi-empirical model is accounting expicitly for surface roughness scattering and screened Coulomb scattering in addition to phonon scattering. This model shows excellent agreement with experimentally measured effective mobility data from three different published sources for a wide range of effective transverse field, channel doping and temperature. By accounting for screened Coulomb scattering due to doping impurities in the channel, our model describes very well the roll-off of effective mobility in the low field (threshold) region for a wide range of channel doping level (Na=3.0*10$^{14}$ - 2.8*10$^{18}$ cm$^{-3}$ ).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.335-341
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• 2010

To improve the performance of inner loop based on PD controller for a unmanned helicopter, neural networks are applied. The performance of PD controller designed on the response characteristics of error dynamics decreases because of uncertain nonlinearities of the system. The nonlinearities are decoupled to modified dynamic inversion model(MDIM) and are compensated by the neural networks. For the training of the neural networks, online weight adaptation laws which are derived from Lyapunov's direct method are used to guarantee the stability of the controller. The results of the improved performance of PD controller by neural networks are illustrated in the simulation of unmanned helicopter with nonlinearities,