머신러닝을 이용한 탄성파 해양학 자료 잡음 억제

Title
머신러닝을 이용한 탄성파 해양학 자료 잡음 억제
Alternative Title
Noise attenuation of the Sparker Seismic Oceanography data using Machine learning
Author(s)
전형구; 주형태; 이상훈; 문혜진; 전청균; 안신혜
KIOST Author(s)
Jun, Hyunggu(전형구)Jou, Hyeong Tae(주형태)Lee, Sang Hoon(이상훈)Moon, Hyejin(문혜진)Jeon, Chung Kyun(전청균)Ahn, Shinhye(안신혜)
Publication Year
2019-12-12
Abstract
Seismic oceanography (SO) is a method of obtaining the structure and physical properties of ocean by using the seismic exploration and processing. The advantage of the SO is that the data acquired by using SO has higher lateral resolution than the data acquired by using the conventional oceanographic devices. Therefore, the SO has been used to study the distribution of the water mass, the dissipation of the turbulence, and characteristic of the internal waves in many regions. In most SO studies, the seismic data was obtained by using the air-gun, but recently the sparker was also used to generate higher frequency source wavelet. The use of the higher frequency components increases the vertical resolution of the seismic data, which can provide much detail information of the ocean. However, the low signal to noise ratio of the sparker seismic data is one of the biggest obstacles of using sparker source in SO study. The energy of the sparker sourceis much smaller than the energy of the air-gun source, thus the influence of the random noise is severer in sparker seismic data than in the air-gun seismic data. Therefore, the attenuation of the random noise in the sparker seismic data is one of the important issues in SO data processing. In this study, we applied convolutional neural network (CNN) to attenuate the random noise in the sparker seismic data. The Denoising Convolutional Neural Network (DnCNN) which extracts th
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/21027
Bibliographic Citation
AGU 2019, 2019
Publisher
American Geophysical Union
Type
Conference
Language
English
Publisher
American Geophysical Union
Related Researcher
Research Interests

Seismic data processing,Seismic modeling,Machine learning,탄성파 자료처리,탄성파모델링,머신러닝

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