• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of the Korean Society of Marine Environment & Safety
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• v.2 no.1
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• pp.1-10
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• 1996

초음파는 액체 및 고체의 매질 속에서도 그 전달 특성이 우수하여 수중 물체의 감지, 지질 조사 자원탐사뿐만 아니라, 의학 분야에서도 널리 사용되고 있다. 물체유동정보 측정방식에는 연속파를 이용한 도플러식과 펄스 신호를 이용한 도플러는 거리 분해능이 좋으므로 깊이에 따른 속도 정보를 쉽게 얻을 수 있는 장점이 있으나, 수신되는 도플러 신호가 탐촉자의 특성과 매질 속에서의 전파특성 등에 의하여 송신된 신호와 파형이 다르고 복잡한 주파수 특성을 가지므로 연속파에서와 같이 도플러 주파수를 직접 측정하기 곤란하다. 도플러 주파수를 검출하기 위하여 여러 방법이 개발되어 있으나, 측정거리와 측정속도의 제약과 더불어, 실시간(real time) 처리에 의한 분포적 측정이 어려운 실정이다. 본 연구에서는 시간 영역에서 국소 데이터를 이용하여 펄스 신호의 위상을 정의하고 실시간에서 펄스 신호를 위상으로 변환하는 신호 처리법을 제안하였다. 또한 이 신호 처리법을 응용하여 측정 범위의 위상 곡선에서 위상 차를 계산함으로써 평균 가속도와 유동속도정보를 분포적으로 얻을 수 있는 새로운 펄스 도플러 기법을 제안하였으며, 모델 신호를 만들어 제안된 방법의 유용성을 검토하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.13-13
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• 2023

디지털 기술은 오늘날 플랫폼과 디지털 트윈의 기술도입을 통해 현실 세계를 네트워크와 가상세계와의 연결이 통합되어진 가상 현실 세계의 입문 도약이다. 현실에서 가상현실의 사이의 디지털 전환(digital transformation)에는 디지털 기술과 솔루션을 비즈니스의 모든 영역에 통합하는 것이 포함된다. 이러한 디지털 전환의 핵심은 데이터에 관한 것이며, 데이터를 활용하여 가치를 창출하고 고객경험과 비즈니스 영역을 극대화하는 방식을 제공한다. 최적의 데이터를 제공하기 위한 플랫폼과 가상 현실세계 구현을 위한 디지털 트윈의 상호연계 관한 기본 개념은 데이터 수집, 데이터 분석, 데이터 시각화 및 데이터 보고와 같은 데이터 비즈니스이다. 현장 데이터는 디지털 양식을 통해 수집, 기록, 저장된다. 현장 IoT 기반 데이터(사진 및 비디오 매체 등)는 지속적으로 수집되고 종종 다른 데이터베이스에 저장되지만 지리 공간적 위치에 연결되지 않는다. 모든 디지털 발전을 조화시키고 지하수 데이터에서 더 빠른 이해를 도출하기 위해서는 디지털 트윈이 시작되어야 한다. 단일 지하수플랫폼에서 현장 조건을 시각화하고 실시간 데이터를 스트리밍하며, 과거 3D 데이터와 상호작용하여지질 또는 지화학 데이터를 선택적 사용을 위해 지하수 플랫폼과 디지털 트윈이 연계되어야 한다. 데이터를 디지털 정보모델과 연결하면 디지털 트윈에 생명을 불어넣을 수 있지만 디지털 트윈의 가치를 극대화하려면 여전히 데이터 플랫폼 서비스와 전달 방식을 선택해야 한다. 지하수 플랫폼동시성을 갖는 디지털 트윈은 정적 및 동적 데이터를 저장하는 데이터베이스 또는 크라우드 서비스에서 데이터를 가져오는 API(애플리케이션 프로그래밍 인터레이스), 디지털 트윈을 위한 호스팅 공간, 디지털 대상을 구축하는 소프트웨어, 구성 요소 간 읽기/쓰기를 위한 스크립트, chatGPT 및 API를 활용할 수 있다. 이를 통해 수집된 데이터의 실시간 양방향 통신기술인 지하수 플랫폼 기술을 활용하여 디지털 트윈을 적용하고 완성할 수 있고, 이를 지하수 분야에도 그대로 적용할 수 있다. 지하수 분야의 디지털 트윈 기술의 근간은 지하수 모니터링을 위한 관측장치와 이를 활용한 지하수 플랫폼의 구축 및 양방향 자료전송을 통한 분석 및 예측기술이다. 특히 낙동강과 같이 유역면적이 넓고 유역 내 지자체가 많아 이해관계가 다양하며, 가뭄과 홍수/태풍 등 기후위기에 따른 극한 기상이변가 자주 발생하고, 또한 보 및 하굿둑 개방 등 정부정책 이행에 따른 민원이 다수 발생하는 지역의 경우 하천과 유역에 대한 지하수 플랫폼과 디지털 트윈의 동시성 기술적용 시 지하수 데이터에 대한 고려가 반드시 수반되어야 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.429-435
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• 2021

The integrated underground space map shows the underground facility(water supply, sewage, gas, electric power, communication, heating), underground structures (subway, underpass, underground walkway, underground parking lot, underground shopping mall, common ward), ground(drilling, coffin, geology) refers to a map constructed so that a total of 15 types of underground information can be checked at a glance on a three-dimensional basis. The purpose of this study is to develop a technology to correct the problem of curved surface processing and the unevenness of underground facility pipelines that occur in converting 2D underground facility data into 3D-based underground space integrated map(3D underground facility model). do it with. To this end, we first investigated and reviewed the domestic and foreign status of technologies that generate data on underground facilities based on three dimensions, and developed a surface correction algorithm and an unevenness correction algorithm to solve practical problems. Algorithms to verify the developed algorithm This applied correction program was developed. Based on the above process, the three-dimensional model of the underground facility could be produced identically to reality. This study is judged to have significance as a basic study to improve the utilization of the underground spatial integration map.

• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.129-139
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• 2022

Analog-based streamers for ultra-high-resolution seismic surveys are capable of additional noise ingress in water, but the specifications cannot be expanded through interconnections. Foreign-produced digital streamers have been introduced and used primarily at domestic research institutes; however, the cost is high and smooth maintenance is challenging. This study investigates the localization of ultra-high-resolution digital streamers capable of high-resolution imaging of a geological structure. A digital streamer capable of 24-bit, 10 kHz digital sampling of up to 64 channel data was developed through research and development. Various quantitative specifications of the system were designed and developed close to the benchmark model, Geometrics' GeoEel streamer, and the number of modules that make up the system was drastically reduced, reducing development costs and making it easier to use. The field applicability of the developed streamer system was evaluated in an in situ experiment conducted in the waters around the Port of Yeong-il Bay in Pohang in April 2022.

• Journal of the Korean Geotechnical Society
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• v.24 no.10
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• pp.57-70
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• 2008

The advantage of piezocone penetration test is a guarantee of continuous data, which is a source of reliable interpretation of target soil layer. Many researches have been carried out f3r several decades and several classification charts have been developed to classify in-situ soil from the cone penetration test result. Since most present classification charts or methods were developed based on the data which were compiled over the world except Korea, they should be verified to be feasible for Korean soil. Furthermore, sometimes their charts provide different soil classification results according to the different input parameters. However, unfortunately, revision of those charts is quite difficult or almost impossible. In this research a new soil classification model is proposed by using fuzzy C-mean clustering and neuro-fuzzy theory based on the 5371 CPT results and soil logging results compiled from 17 local sites around Korea. Proposed neuro-fuzzy soil classification model was verified by comparing the classification results f3r new data, which were not used during learning process of neuro-fuzzy model, with real soil log. Efficiency of proposed neuro-fuzzy model was compared with other soft computing classification models and Robertson method for new data.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.121-129
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• 2009

Today, it is difficult to re-use and share spatial information, because of the explosive growth of heterogeneous information and specific characters of spatial information accumulated by diverse local agency. A spatial analysis of subsurface spatial informa-tion, one of the National Spatial Data Infrastructure, needs related spatial information such as, topographical map, geologic map, underground facility map, etc. However, current methods using standard format or spatial datawarehouse cannot consider a se-mantic hetergenity. In this paper, the layered ontology model which consists of generic concept, measuremnt scale, spatial model, and subsurface spatial information has developed. Also, the current ontology building method pertained to human experts is a expensive and time-consuming process. We have developed the MDA-based metamodel(UML Profile) of ontology that can be a easy under-standing and flexiblity of environment change. The semantic quality of devleoped ontology model has evaluated by reasoning engine, Pellet. We expect to improve a semantic sharing, and strengthen capacities for developing GIS experts system using knowledge representation ability of ontology.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Economic and Environmental Geology
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• v.53 no.4
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• pp.479-489
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• 2020

A fault is a geological structure that can be a migration path or a cap rock of hydrocarbon such as oil and gas, formed from source rock. The fault is one of the main targets of seismic exploration to find reservoirs in which hydrocarbon have accumulated. However, conventional fault detection methods using lateral discontinuity in seismic data such as semblance, coherence, variance, gradient magnitude and fault likelihood, have problem that professional interpreters have to invest lots of time and computational costs. Therefore, many researchers are conducting various studies to save computational costs and time for fault interpretation, and machine learning technologies attracted attention recently. Among various machine learning technologies, many researchers are conducting fault interpretation studies using the support vector machine, multi-layer perceptron, deep neural networks and convolutional neural networks algorithms. Especially, researchers use not only their own convolution networks but also proven networks in image processing to predict fault locations and fault information such as strike and dip. In this paper, by investigating and analyzing these studies, we found that the convolutional neural networks based on the U-Net from image processing is the most effective one for fault detection and interpretation. Further studies can expect better results from fault detection and interpretation using the convolutional neural networks along with transfer learning and data augmentation.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.662-676
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• 2021

The Yeosu Sado dinosaur tracksite is well known for many dinosaur tracks and research on the gregarious behavior of dinosaurs. In addition, various geological and geographical heritage sites are distributed on Sado Island. However, educational field trips for students are very limited due to accessibility according to its geological location, time constraints due to tides, and continuous weathering and damage. Therefore, this study aims to generate 3D models and images of dinosaur tracks using the photogrammetric method, which has recently been used in various fields, and then discuss the possibility of using them as paleontological research and educational contents. As a result of checking the obtained 3D images and models, it was possible to confirm the existence of footprints that were not previously discovered or could not represent details by naked eyes or photos. Even previously discovered tracks could possibly present details using 3D images that could not be expressed by photos or interpretive drawings. In addition, the 3D model of dinosaur tracks can be preserved as semi-permanent data, enabling various forms of utilization and preservation. Here we apply 3D printing and mobile augmented reality content using photogrammetric 3D models for a virtual field trip, and these models acquired by photogrammetry can be used in various educational content fields that require 3D models.