잡음 자료 취득을 통한 기계학습 기반 현장 탄성파 자료 잡음 억제 연구
Machine-learning Based Noise Attenuation of Field Seismic Data using Noise Data Acquisition
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초록
현장 탄성파 탐사를 통해 자료를 취득하는 과정에서 탄성파 자료에는 반사 신호뿐만 아니라 다양한 형태의 잡음이 함께 기록되므로 탄성파 자료처리를 통해 잡음을 억제하는 것은 매우 중요한 부분이다. 본 연구에서는 기계학습 적용을 통해 탄성파 자료의 무작위 잡음을 억제하고자 하였다. 탄성파 자료에 기록된 무작위 잡음은 탄성파 탐사환경에 따라 서로 다른 특성을 가지게 된다. 기계학습을 통한 잡음 억제를 위해서는 적절한 학습자료의 구축이 필수적이며 잡음 제거 대상 탄성파 자료와 학습자료에 포함된 잡음의 특성이 서로 유사해야 한다. 본 연구에서는 학습자료 구축을 위해서 현장 탄성파 탐사 중 송신원 발파 없이 잡음자료를 직접 취득하였다. 취득한 잡음자료와 인공 합성 탄성파 자료를 조합하여 학습자료를 구축하였으며 기계학습 모델을 학습시켰다. 학습된 모델을인공 합성 자료 및 현장자료에 적용하여 무작위 잡음을 제거하였으며, 제안된 방법을 통해 구축된학습자료가 유효함을 검증하였다.
Abstract
The seismic data acquired by field seismic exploration contains not only seismic reflections but also various types of noise. Therefore, noise attenuation in seismic data is one of the most important tasks in seismic data processing. In this study, we attempted to attenuate random noise in field seismic data by using machine learning. The proper training data is an important part of machine learning, and the characteristics of training data should be similar to the characteristics of target data. To construct the proper training data for noise attenuation, we acquired noise data from field seismic exploration without a seismic source. The training data was generated by combining the acquired noise data and synthetic seismic data. The trained model was applied to the synthetic and field seismic data, and the results were used to verify the effectiveness of the proposed method.
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