• Geophysics and Geophysical Exploration
• /
• v.23 no.1
• /
• pp.22-37
• /
• 2020

We analyze the comprehensive three-dimensional (3D) magnetic structure characteristics from the seafloor to the deep layer of the Tofua Arc (TA) 12 seamount in the Tonga Arc, Southwestern Pacific, using bathymetric and geomagnetic data, and magnetization vector inversion (MVI) results. The seafloor features surrounding TA 12 highlight a NW-SE-oriented elliptical caldera at the summit of the seamount, two small cones in the depressed area. A large-scale sea valley is present on the western flank of the seamount, extending from these caldera cones to the southwestern base of the seamount. TA 12 seamount exhibits a low magnetic anomaly in the caldera depression, whereas a high magnetic anomaly is observed surrounding the low magnetic anomaly across the caldera summit. It is therefore presumed that there may be a strong magnetic material distribution or magma intrusion in the caldera. The 3D MVI results show that the high anomaly zones are mainly present in the surrounding slopes of the seamount from the seafloor to the -3,000 m (below the seafloor) level, with the magnetic susceptibility intensity increasing as the seafloor level increases at the caldera depression. However, small high anomaly zones are present across the study area near the seafloor level. Therefore, we expect that the magma ascent in TA 12 seamount migrated from the flanks to the depression area. Furthermore, we assume that the complex magnetic distribution near the seafloor is due to the remnant magnetization.

• Geophysics and Geophysical Exploration
• /
• v.14 no.1
• /
• pp.88-97
• /
• 2011

We present a method for modelling the terrain response of gravity gradiometry surveys utilising an adaptive quadtree mesh discretization. The data- and terrain-dependent method is tailored to provide rapid and accurate terrain corrections for draped and barometric airborne surveys. The surface used in the modelling of the terrain effect for each datum is discretized automatically to the largest cell size that will yield the desired accuracy, resulting in much faster modelling than full-resolution calculations. The largest cell sizes within the model occur in areas of minimal terrain variation and at large distances away from the datum location. We show synthetic and field examples for proof of concept. In the presented field example, the adaptive quadtree method reduces the computational cost by performing 351 times fewer calculations than the full model would require while retaining an accuracy of one E$\"{o}$tv$\"{o}$s for the gradient data. The method is also used for the terrain correction of the gravity field and performed 310 times faster compared with a calculation of the full digital elevation model.

• Journal of Korean Society for Geospatial Information Science
• /
• v.16 no.1
• /
• pp.83-93
• /
• 2008

The topographic effect is one of the most important component in the solution of the geodetic boundary value problem (geodetic BVP). Therefore, topographic effect should be considered properly for developing the precise geoid model, especially for the area where contains many mountains like Korea. The selection of gravity reduction method in the context of the precise geoid determination depends on the magnitude of its indirect effect, the smoothness and magnitude of the reduced gravity anomalies, and their related geophysical interpretation. In this study, Korean digital elevation model with 100m resolution was constructed and topographic effect was calculated by three reduction methods as like Helmert condensation method and RTM method and Airy-isostatic reduction method. Through the analysis of computation results, we can find that RTM reduction method is the best optimal method and the results shows that gravity anomaly and indirect effect of geoidal height are $0.660{\pm}13.009mGal$, $-0.004{\pm}0.131m$ respectively and it is the most gentle slow of the three methods. Through this study, it was found that the RTM method is better suitable for calculating topographic effect precisely in context of precise geoid determination in Korea than other reduction methods.

• The Journal of the Petrological Society of Korea
• /
• v.14 no.1
• /
• pp.24-37
• /
• 2005

In this study we examined the geomorphological and geological characteristics of the granite landforms in the Iksan area. Moreover we investigated the source areas of stones which are used to build the Mieruksaji west stone pagoda. Joint is most identifiable geomorphological and geological structure in the Iksan area. Direction of J1 joint appears to be N71°E∼EW, and that of J2 joint ranges N20°W∼N20°E. Cross-pattern joint is predominant in the study area, and linen.-pattern vertical joint is also observed. Tor and corestone are easily found as geomorphological features in the study area. Corestones forming for are almost 2∼3 m across and 2∼3 m or over 5 m high. Their hardness is mainly of hammer bounce. Tower-type and castle-type of for are characteristic in the Mireuksan granites. Other geomorphological features such as tafoni, gnamma are also observed in the study area. Petrographical and geochemical features of the stones used for the Mireuksaji west stone pagoda are compared with those of the granites cropped out nearby, and indicate that the stones from the Mieruksaji west stone pagoda are quite similar to the Mireuksan granites. In the Mireuksan we can easily find lots of old traces for rock cutting.

• Geophysics and Geophysical Exploration
• /
• v.10 no.3
• /
• pp.163-172
• /
• 2007

A seafloor curvature measurement method was developed to extract redundant topographic features from the multi-beam bathymetry data, and then applied to the data of abyssal plain area in the Pacific. Any seafloor might be modeled to a quadratic surface determined in a linear least squares sense, and its curvature could be derived from the eigen values related with quadratic model parameters. The curvature's magnitude as well as polarity showed distinct relationship with geometric characteristics of the seafloor like as ridge and valley. From the investigation of curvature's variation with the number of data in the quadratic surface, the optimal size of data aperture could be applied to real bathymetry data. The application to real data also required the determination of the accompanying threshold values to cope with corresponding topographic features. The calculation method of previous studies were reported to be sensitive to the background noise. The improved curvature measurement method, incorporating the sum of eigen values has reduced unwanted artifacts and enhanced ability to extract lineament features along strike direction. The result of application shows that the curvature measurement method is effective tool for the estimation of a possible mining area in the seamount free abyssal hill area.

• Journal of Digital Convergence
• /
• v.18 no.10
• /
• pp.233-238
• /
• 2020

In the cloud environment, IoT devices using sensors and wearable devices are being applied in various environments, and technologies that accurately determine the information generated by IoT devices are being actively studied. However, due to limitations in the IoT environment such as power and security, information generated by IoT devices is very weak, so financial damage and human casualties are increasing. To accurately collect and analyze IoT information, this paper proposes a topographic information-based key management technique that considers IoT information errors. The proposed technique allows IoT layout errors and groups topographic information into groups of dogs in order to secure connectivity of IoT devices in the event of arbitrary deployment of IoT devices in the cloud environment. In particular, each grouped terrain information is assigned random selected keys from the entire key pool, and the key of the terrain information contained in the IoT information and the probability-high key values are secured with the connectivity of the IoT device. In particular, the proposed technique can reduce information errors about IoT devices because the key of IoT terrain information is extracted by seed using probabilistic deep learning.

• Water for future
• /
• v.39 no.12 s.161
• /
• pp.52-58
• /
• 2006

• Water for future
• /
• v.35 no.1
• /
• pp.73-84
• /
• 2002

• Plant Disease and Agriculture
• /
• v.4 no.2
• /
• pp.30-36
• /
• 1998

• Plant Disease and Agriculture
• /
• v.4 no.1
• /
• pp.79-89
• /
• 1998