Random noise attenuation of sparker seismic oceanography data with machine learning SCIE SCOPUS

Cited 7 time in WEB OF SCIENCE Cited 6 time in Scopus
Title
Random noise attenuation of sparker seismic oceanography data with machine learning
Author(s)
Jun, Hyunggu; Jou, Hyeong Tae; Kim, Chung Ho; Lee, Sang Hoon; Kim, Han Joon
KIOST Author(s)
Jou, Hyeong Tae(주형태)Kim, Chung Ho(김충호)Lee, Sang Hoon(이상훈)
Alternative Author(s)
전형구; 주형태; 김충호; 이상훈; 김한준
Publication Year
2020-11-11
Abstract
Seismic oceanography (SO) acquires water column reflections using controlled source seismology and provides high lateral resolution that enables the tracking of the thermohaline structure of the oceans. Most SO studies obtain data using air guns, which can produce acoustic energy below 100 Hz bandwidth, with vertical resolution of approximately 10 m or more. For higher-frequency bands, with vertical resolution ranging from several centimeters to several meters, a smaller, low-cost seismic exploration system may be used, such as a sparker source with central frequencies of 250 Hz or higher. However, the sparker source has a relatively low energy compared to air guns and consequently produces data with a lower signal-to-noise (S/N) ratio. To attenuate the random noise and extract reliable signal from the low S /N ratio of sparker SO data without distorting the true shape and amplitude of water column reflections, we applied machine learning. Specifically, we used a denoising convolutional neural network (DnCNN) that efficiently suppresses random noise in a natural image. One of the most important factors of machine learning is the generation of an appropriate training dataset. We generated two different training datasets using synthetic and field data. Models trained with the different training datasets were applied to the test data, and the denoised results were quantitatively compared. To demonstrate the technique, the trained models were applied to an SO sparker seismic dataset acquired in the Ulleung Basin, East Sea (Sea of Japan), and the denoised seismic sections were evaluated. The results show that machine learning can successfully attenuate the random noise in sparker water column seismic reflection data.
ISSN
1812-0784
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/37745
DOI
10.5194/os-16-1367-2020
Bibliographic Citation
OCEAN SCIENCE, v.16, no.6, pp.1367 - 1383, 2020
Publisher
COPERNICUS GESELLSCHAFT MBH
Subject
WAVE-FORM INVERSION; TURBULENT DIFFUSIVITY; REFLECTION; WATER; TEMPERATURE; NETWORKS; LAPLACE; GULF
Type
Article
Language
English
Document Type
Article
Publisher
COPERNICUS GESELLSCHAFT MBH
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