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1 지구물리와물리탐사 Geophysics and Geophysical Exploration ISSN (Print) Vol. 23, No. 3, 2020, p ISSN X (Online) 머신러닝을이용한탄성파반사법자료의해저면겹반사제거 남호수 1 임보성 2 권일룡 3,4 김지수 4 * 1 케이티파워텔전략상품팀 2 한국석유공사국내사업처국내탐사팀 3 주식회사포도 4 충북대학교지구환경과학과 해설 Removal of Seabed Multiples in Seismic Reflection Data using Machine Learning Ho-Soo Nam 1, Bo-Sung Lim 2, Il-Ryong Kweon 3,4, and Ji-Soo Kim 4 * 1 KT Powertel, Strategic Product Planning Team 2 Korea National Oil Corporation, Domestic Business Dept., Domestic Exploration Team 3 PODO Inc. 4 Chungbuk National University, Dept. of Earth and Environment Sciences 요약 : 해저면탄성파겹반사는발파점모음자료와겹쌓기단면에서모두일차반사파의해석에잘못된결과를초래할수있다. 따라서, 해저면겹반사는자료처리를통해제거해야한다. 전통적인자료처리과정에서겹반사제거는예측오차곱풀기와라돈필터링등과같은모델-기반기법과지표관련-겹반사제거와같은데이터-기반기법에의해이루어져왔다. 그러나대다수의자료처리과정들은방대한컴퓨터자원과전문적인자료처리기법뿐만아니라자료처리변수들을테스트하고선택하는데많은시간을필요로한다. 이논문에서는머신러닝시스템을활용한해저면겹반사의제거효과를살펴보기위해 Marmousi2 속도모델에대한수치모델링으로겹반사가포함된입력데이터와겹반사가포함되지않은레이블데이터를생성하였다. 수직시간차가보정된공통중간점모음자료로훈련데이터를구성하였으며인공신경망은 U-Net 모델을적용하였다. 해저면겹반사를제거하기위해훈련된모델은레이블데이터에거의근접하는예측결과를만들어내며, 현장자료에대한예측테스트에서해저면겹반사를효과적으로제거하는것으로나타났다. 주요어 : 해저탄성파반사법자료, 겹반사파, 수직시간차보정, 머신러닝, U-Net 모델 Abstract: Seabed multiple reflections (seabed multiples) are the main cause of misinterpretations of primary reflections in both shot gathers and stack sections. Accordingly, seabed multiples need to be suppressed throughout data processing. Conventional model-driven methods, such as prediction-error deconvolution, Radon filtering, and data-driven methods, such as the surface-related multiple elimination technique, have been used to attenuate multiple reflections. However, the vast majority of processing workflows require time-consuming steps when testing and selecting the processing parameters in addition to computational power and skilled data-processing techniques. To attenuate seabed multiples in seismic reflection data, input gathers with seabed multiples and label gathers without seabed multiples were generated via numerical modeling using the Marmousi2 velocity structure. The training data consisted of normal-moveout-corrected common midpoint gathers fed into a U-Net neural network. The well-trained model was found to effectively attenuate the seabed multiples according to the image similarity between the prediction result and the target data, and demonstrated good applicability to field data. Keywords: seabed seismic reflection data, multiples, normal-moveout correction, machine learning, U-Net model Received: 7 July 2020; Revised: 24 August 2020; Accepted: 25 August 2020 *Corresponding author geop22@cbnu.ac.kr Address: 1, Chungdae-ro, Seowon-gu, Cheongju-si, Chungcheongbuk-do 29644, Republic of Korea c2020, Korean Society of Earth and Exploration Geophysicists This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 서론 탄성파반사법탐사 (seismic reflection survey) 는육상및해상에서인공샘 (controlled source) 을통해발생한지진파가음향임피던스 (acoustic impedance) 가서로다른지하경계면에서반사되어되돌아오는에너지를수신기로기록하고, 여기에적절한자료처리 (data processing) 를수행하여지구내부의지질 168

2 머신러닝을이용한탄성파반사법자료의해저면겹반사제거 169 학적구조를규명하는기법이다. 반사법을통해얻은원자료 (raw data) 로부터지하단면을작성하기위해서는탐사목적과대상체의깊이에따라다양한자료처리기법들을선택적으로적용해야하는데이는자료처리의목적이궁극적으로지하반사면의연속성과분해능을최대한향상시키는것이기때문이다 (Hatton et al., 1986; Dahl- Jensen, 1989). 반사법탐사자료의단면도에서다양한형상으로표현되는겹반사파 (multiples) 는특히해양지층단면도의해석에있어서잘못된결과를초래할수있으므로자료처리과정을통해일차반사파 (primary reflection) 만을남기고대부분제거되어야한다. 겹반사파를완화또는제거하기위해알려진기술로는다른영역으로변환해서수행하는모델기반기법 (models-driven methods) 인예측오차곱풀기 (predictive error deconvolution)(peacock and Treitel, 1969), 라돈필터링 (Radon filtering)(hampson, 1986), 라돈예측곱풀기 (Radon prediction deconvolution) (Lokshtanov, 1995) 를들수있다. 그러나반사파와겹반사파의속도차이가작을경우이들을서로구분하기어렵다. 이에반해자료기반기법 (data-driven methods) 인지표관련겹반사제거 (surface-related multiple elimination, SRME) (Stewart et al., 2007; Wang et al., 2017; Naidu et al., 2013) 기법은속도와같은지하정보가전혀필요없고지표와관련된겹반사를예측하는기법으로서시간차이가매우작은짧은벌림거리 (offset) 에서효과가있지만일반적으로먼거리벌림거리를운용하는심부해양탐사자료에서는서로구분하기어렵다는단점을가지고있다. 따라서일반적인상업용전산처리에서는먼저 SRME를적용하여가까운벌림거리 (near offset) 의겹반사파를제거하고, 수직시간차 (normal moveout, NMO) 보정을적용한후라돈예측곱풀기등을이용하여먼벌림거리에걸쳐남아있는겹반사파를제거하는것이일반적이다. 이때라돈필터등의전산처리기법은겹반사가아닌실제반사파를제거하거나원하지않는인공잡음 (artifacts) 을생성시킬수있어많은주의를필요로한다. 머신러닝 (machine learning) 의한분야인딥러닝 (deep learning) 은최근컴퓨터비젼 (computer vision), 언어인식 (speech recognition), 자연어분석 (natural language processing) 을포함 한다양한분야에서우수한성능을보이고있다 (Deng and Yu, 2014; Lee, 2017). 엄밀히딥러닝은새로운개념은아니며, 많은양의자료를원활히취급할수있는하드웨어의성능개선과함께활성화함수 ReLU (rectified linear units)(hahnloser et al., 2000) 와같은개선된알고리즘들의개발로복잡한비선형관계를한꺼번에다룰수있게되었으며, 다양한학습모델의개발로응용범위와성능이급성장하고있는추세이다. 최근국내에서도탄성파전산처리및해석분야에서 Jo and Ha (2020), Choi et al. (2020) 은각각머신러닝기법을이용한탄성파잡음제거및자료단층해석에대한국외사례들을주제별로자세히소개한바있다. 특히 Siahkoohi et al. (2019) 은딥러닝을통한겹반사제거에합성곱신경망 (convolutional neural network, CNN) 과적대적생성신경망 (generative adversarial network, GAN) 을접목하여그효용성을검증한바있다. 이연구에서는머신러닝을이용한겹반사제거에대한시험검증을위해 2차원의해양지각탄성파수치모델링을통해서겹반사가포함된입력데이터 (input data) 와겹반사가포함되지않은레이블데이터 (label data) 를생성하였다. 생성된훈련데이터는 U-Net 모델 (Ronneberger et al., 2015) 을이용하여훈련시켰으며여기서훈련된모델 (Nam, 2020) 을바탕으로수직시간차가보정된공통중간점모음자료 (NMO-corrected CMP gather) 를훈련데이터로활용하여특히먼벌림거리에서의겹반사파제거의효과를확인하고현장자료에도테스트해보았다. 2D 모델링을통한탄성파학습자료취득 머신러닝시스템을구축하고활용하기위해서는정밀하고충분한양의훈련데이터확보가필수적이다. 머신러닝분야는매우다양한분야에서응용되고있지만활용하고자하는영역에서훈련데이터를획득하는것은해당분야의전문화된지식을기초로많은노력과연구가필요하다. 이연구의관심영역인해양탄성파의겹반사파훈련데이터를얻는경우도마찬가지이다. 이연구에서는겹반사파들을포함한자료를분석하기위해 Marmousi2 속도구조 (Fig. 1)(Martin, 2004) 에대한수치모델링으로반사법자료를수집하였다. 송신원과수신기의수심은 Fig. 1. Elastic Marmousi2 P-wave velocity model (Martin, 2004).

3 170 남호수 임보성 권일룡 김지수 Fig. 2. Schematic diagram of a source and 801 receivers at water depth 40 m on a 10 m 10 m grid model. The symbols,, and represent the source, stream cable, and receivers, respectively. Table 1. Data acquisition parameters from the Marmousi2 velocity structure. Model Parameters Model grid 3,400(x) 700(z) Grid size 10 m Model size 34,000 m 7,000 m Recording time 10,000 ms Sampling interval ms Number of samples 11,200 (resampled to 5,000 samples) Number of receivers 800 Receiver interval 10 m Maximum offset 8,000 m 모두 40m이며가로세로기본단위 10m의가로축 (x) 3,400 개, 세로축 (z) 700 개의 2차원영역에서수신기의거리를 10m 단위로총 801개를설정하여최대벌림거리 (maximum offset) 를 8,000 m로설계하였다 (Fig. 2, Table 1). 기록시간은지하경계면의반사파정보를충분히기록할수있도록 10,000 ms로설정하였다. 훈련자료에해저면겹반사를포함시키기위해속도모델에공기층을삽입하여수치모델링을수행할경우에수반되는도깨비 (ghost) 는결과적으로음원의형태를바꾸게된다. 따라서적합한머신러닝을위해서는얻어진자료에서먼저도깨비를제거 (deghosting) 하거나학습을도깨비제거와해저면겹반사제거의두가지단계로확장시켜야한다. 전자의경우도깨비제거에정확한음원요소파 (source wavelet) 를결정해야하며, 하나의모델에학습목적을두개로확장하는후자의경우에는신경망이더욱복잡해지고검증이어려워지는한계가있다. 이러한이유로음원의파형은동일하고해저면겹반사의존재유무만을차이로가지는훈련자료를각각만들고자하였다. 이를위해수치모델링의경계조건에서 40개그리드의흡수경계조건을네개의경계면에모두적용하였다. 우선공기층을모델에서제외시켜도깨비와해저면겹반사를발생하지않도록하여출력된자료에서직접파를제외한나머지이벤트들을일정한시간간격으로반복적으로만들어내어이를원시자료에 Fig. 3. Data preparation flow for the machine-learning system. 더하였다. 이때해저면겹반사의모사에는파의극성과해수면의반사계수를고려하였다. 머신러닝에서해저면겹반사가더해진자료는입력자료, 해저면겹반사가더해지지않은자료는레이블자료로설정하였다. 수치모델링으로얻어진공통발파점모음자료 (common shot gathers) 는공통중간점분류 (CMP sorting), 수직시간차보정 (NMO correction) 과스트레치뮤팅 (stretches muting) 을거쳐자료고르기 (data sampling) 와자료정규화 (data normalization) 와같은입력자료전처리 (input data handling) 의단계를거쳐지도학습 (supervised training) 자료로준비되었다 (Fig. 3). 이때, 현장자료의속도분석이잡음등으로 100% 정확하지않은것을고려하여, 훈련자료의속도에도 10% 가량의오차를임의적으로더하거나빼주었다. 따라서이와같은인위적인속도오차의설정은수직시간차보정이정확하지않은현장자료의겹반사를효과적으로제거하는훈련모델을구축하는데역할을할것으로기대한다. 이모형에대한머신러닝용데이터취득을위해 X86 서버및 Ubuntu 운영체제에서파이썬을포함한 Anaconda 및 Devito Project (Luporini et al., 2020) 기반의소프트웨어를사용하였다 (Table 2). Anaconda는현재가장대중화된 Data Table 2. Hardware and software specifications for the 2D elastic modeling. Item Specification CPU Intel Xeon(R) 2.50GHz 8 Hardware RAM 30 GB OS UBUNTU Modeling solution Devito Project Software Data science platform Anaconda Language Python 3.7

4 머신러닝을 이용한 탄성파 반사법 자료의 해저면 겹반사 제거 Fig. 4. Selected common shot gather at a distance position of 21,320 m (a) with and (b) without multiples. Science Platform 으로 데이터의 가공, 연산, 디스플레이하기 위한 많은 기본 도구와 함께 3rd Party library가 제공되는 장점 이 있다. 이 연구에서는 Anaconda 환경 내에서 구동되 는 유한차분법(FDM) 및 파동방정식-기반 탄성파 Modeling library인 Devito Project를 활용하여 일차반사파와 같은 곡률 을 보이는 겹반사파가 있는 발파점 모음자료(Fig. 4a)와 겹반 사파가 없는 발파점 모음자료(Fig. 4b)를 만들었다. 현장자료의 속도분석 과정에는 오차가 수반되기 마련이며 이러한 오차는 수직시간차 보정에 잘못된 결과를 초래한다. 따 라서 현장자료에도 적용되는 훈련모델을 구성하기 위해 각 공 통중간점 모음자료에 대한 속도값을 다시 최고 5%의 오차로 무작위로 더하거나 빼주었다. 공통중간점 모음자료(Fig. 5a)에 서 짧은 벌림거리에서 곡률이 거의 같은 쌍곡선의 형태로 보 이는 겹반사들이 먼 벌림거리에서는 선형으로 나타난다. Fig. 5b는 공통중간점 모음자료로서 겹반사가 없는 공통발파점 모 음자료들로부터 분류된 것이다. 수직시간차 보정에서 자동 속도분석에 사용하는 속도 스펙 트라(velocity spectra)에서 겹반사는 영오프셋 시간(zero-offset time)이 같은 반사파와 비교할 때 속도가 상대적으로 작아 일 렬로 정렬되는 반사파에 비해 과소보정(under correction)되는 경향이 있다(Fig. 6)(Scheriff and Geldart, 1995; Peacock and Treitel, 1969). 이러한 현상은 수직시간차 보정이 된 자료(Figs. 7a,b)에서도 잘 나타나는데 특히 보정에서 수반되는 저주파 스 트레치(stretches)는 먼 벌림거리에서 부각되는데 차후 머신러 171 Fig. 5. Selected common midpoint (CMP) gather at a distance position of 15,000m (a) with and (b) without multiples. Fig. 6. Schematic diagram showing the effect of the nomalmoveout(nmo) correction for primary and multiple events: (a) prior to and (b) after the NMO correction (modified from Sheriff and Geldart(1995)). 닝의 정확한 연산을 위해 뮤팅(muting)시켰다. 머신러닝 훈련수행 앞 단계에서 작성한 탄성파 자료는 기록시간과 거리가 충분 히 길기 때문에 머신러닝 훈련을 시키기에는 충분하고 적절하 지만 빠른 훈련과 안정적인 해를 얻기 위해 입력데이터에 대한 전처리 과정을 거쳤다. 첫 번째 고려할 것은 머신러닝은 모델 설계에 따라 입력데이터(input data)와 예측데이터(prediction data)의 형태가 특정 규격으로 정의되어야 한다는 점이다. 이 연구에서는 일반적으로 Computer Vision 영역에서 데이터의 취급이 용이하여 주로 사용되는 가로 256, 세로 256 형태의 2

5 172 남호수 임보성 권일룡 김지수 Table 3. Hardware and software specifications for the machinelearning system. Item Specification CPU 4 Core Hardware RAM 16 GB GPU NVIDIA TESLA P100 GPU OS Docker with Python Data science platform Kaggle ( Software Machine-learning platform Tensor Flow, Keras Language Python 3.7 (TensorFlow) 와백엔드로케라스 (Keras) 를사용하였고파이썬으로머신러닝시스템을구현하였다 (Table 3). Fig. 7. Selected NMO-corrected CMP gather (a) with and (b) without multiples. The NMO stretches are muted. 차원데이터로입출력구조를설계하였다. 따라서훈련데이터역시가로 256, 세로 256 형태의 2차원규격으로준비하였다. 두번째고려할사항은계산시간의효율성을위해공통중간점모음자료는전체모음자료중 10번째마다하나의샘플을선택하여경량화시켰고훈련은지역최솟값 (local minimum) 에빠지지않고빠른훈련을위해최소-최대정규화법 (min-max normalization)( 식 1) 을이용하여데이터의값을 0.0~1.0 구간으로정규화시키는작업을수행하였다. x min x x = max x min x 여기서 x 는표본값 x 를정규화시킨값이며 min(x) 와 max(x) 는각각표본의최솟값과최댓값을의미한다. 최근대중화된고성능병렬컴퓨터하드웨어에서특히 NVIDIA에서만든 GPU는대중적으로고성능의게임을위한그래픽카드였지만딥러닝을위해새롭게설계되어활용되고있다 (Chollet, 2018). 이연구에서활용하는 U-Net 모델은 2차원데이터에대한훈련및예측작업에최적화된것으로써합성곱 (convolution) 을기반으로구성된모델이다. 컴퓨터의중앙처리장치 CPU와비교할때 GPU는개별연산속도는느리지만행렬연산에대해병렬처리가가능하여합성곱연산의경우에는 CPU에비해매우빠른연산결과를기대할수있다. 이연구에서는합성곱을효율적으로처리할수있는 GPU 기반의시스템에서구글의오픈소스라이브러리인텐서플로우 (1) U-Net 모델구현및훈련 U-Net 모델의구조 (Fig. 8) 는 2차원의자료를패치 (patch) 라고하는작은단위의이미지로구분하여인식하고해석하는구조로이루어져있다 (Ronneberger et al., 2015). 각합성곱 (convolution) 레이어마다 3 3의합성곱연산이수행되었다. 이때 stride는 1 1로계산하며합성곱에의한차원의감소를막기위해 zero-padding을적용하였다. 각합성곱레이어에활성화함수 ReLU가사용되었다. 각풀링계층에선 2 2 최대풀링 (max pooling) 이사용되어계층이내려갈때마다 1/2 하향샘플링 (down sampling) 이되도록하였다. 각최대풀링마다채널수는 2배씩증가시켰다. 확장경로 (expanding path) 에서는 2 2 상향합성곱 (up convolution) 이사용되고같은계층안에서는수축경로 (contracting path) 와같이 3 3 합성곱연산이수행되었다. 이때상향합성곱은트렌스포즈합성곱 (transpose convolution) 이아닌일반적인형태의합성곱을사용하였다. U-Net 훈련을위한기본인자는 Adam 옵티마이저와손실함수로평균절대오차 (mean absolute error, MAE) 를사용하였다. U-Net 모델의훈련데이터에적합한인자들을찾기위해시행한사전훈련시험을통해지역최적화되거나로스 (loss) 값이발산하지않도록학습률 (learning rate) 의값으로 , epoch는 100 회로적용하였다 (Table 4). 앞에서훈련이완료된 U-Net 머신러닝시스템의겹반사제거성능을확인하고자수직시간차보정을거친공통중간점자료들중에서 373개의모음자료를대상으로가로축 8,000 m 세로축 3,000 ms의윈도 (window) 크기로천부와심부균일한비율로데이터취득이되도록상단중단하단의구역경계를지정하여모음자료별로 3개씩총 1,119개의훈련데이터를확보하였다. 그후주어진각구역안에서의샘플링원도 (sampling window) 는거리축은최대벌림거리로일정하게유지하고시간축을따라점진적으로이동시켜가면서설정하였다. 이와같은시간축이동윈도를통해상부뮤팅된부분과겹반사의큰진폭변화를제어할수있었으며훈련에서필요이상으로데이터학습이많이수행되는과적합현상 (over fitting) 을줄일수있

6 Fig. 8. The U-Net layer structure employed in this study (after Nam (2020)). 머신러닝을이용한탄성파반사법자료의해저면겹반사제거 173

7 174 남호수 임보성 권일룡 김지수 Table 4. Parameters and training data set for the U-Net model. Parameters Training set Item Specification Optimizer Adam Learning rate Epoch 100 Loss function Mean absolute error (MAE) Dropout-rate 0.5 Training data count 951 (85%) Validation data count 168 (15%) Total data count 1,119 (100%) 었다 (Fig. 9). 이렇게선별된가로 8,000 m 세로 3,000 ms 규격의데이터를머신러닝시스템의훈련자료로활용하기위해입력규격인 크기로재조정했으며 (Fig. 9), 훈련데이터는전체데이터의 85% 인 951개의데이터를사용하고나머지 15% 인 168개의데이터는검증데이터로사용했다. 또한손실이력 (loss history) 에서훈련데이터세트 (training data set) 와검증데이터세트 (validation data set) 의손실차이가 epoch이증가할수록 0으로수렴되어머신러닝이정상적으로훈련된것으로판단하였다 (Fig. 10). 훈련된 U-Net 머신러닝시스템의성능을확인하고자훈련에사용되었던데이터를예측을위한입력데이터로재활용하여예측결과를확인해보았다. 포함된입력데이터 (Fig. 11a) 에대한 U-Net 시스템의예측결과 (Fig. 11b) 에서일차반사파는그대로유지된상태에서 3,500 ms와 4,000 ms에서의과소보정 Fig. 10. Training history showing the training loss and validation loss for a U-Net neural network model. 된쌍곡선을제외하고대부분의겹반사가제거되어레이블데이터 (Fig. 11c) 에접근하는예측성능을확인하였다. 겹반사를제거하는학습시스템의성능은각데이터및결과사이의 RMS 오차에서예측결과와레이블데이터사이가 2.80% 로서예측결과와입력데이터사이의 8.41% 에비해매우감소된것으로뒷받침된다. 현장자료에대한적용앞서훈련된 U-Net 머신러닝시스템의실제자료에대한적용성및활용성을살펴보기위해서 CMP 간격 25m, 고르기 Fig. 9. Moving sample-window set for training data on NMO-corrected CMP gathers (a) with and (b) without multiples. The window sets (represented by the dashed lines) were rescaled to a size of for the machine-learning system. The white open thick arrows indicate the upward and downward moving sample windows.

8 머신러닝을 이용한 탄성파 반사법 자료의 해저면 겹반사 제거 175 Fig. 11. Examination of the rejection of multiples in the NMO-corrected CMP gathers: (a) input data, (b) prediction result, and (c) label data. 간격(sampling interval) 2 ms, 최대 벌림거리 6,000 m, 기록시 간 6,150 ms의 현장자료를 사용하였다. 현장자료는 3D SRME 가 이미 적용되고 수직시간차 보정을 거친 공통중간점 자료 (Fig. 12a)이다. SRME 적용 후 먼 벌림거리에 남아있던 겹반 사파가 머신러닝 시스템을 통해 출력된 예측결과(Fig. 12b)에 서 효과적으로 제거된 것을 확인할 수 있다. 또한 이 결과를 일차반사와 겹반사의 속도차이에 민감한 전통적인 라돈 필터 등이 적용된 자료(Fig. 12c)와 비교해보았다. 라돈 필터링 결과 에서 먼 벌림거리의 겹반사가 두루 제거되고 있지만 여러 단 계의 자료처리 과정에서 수반되는 인공잡음 형태의 수평 이벤 트들이 보이는 반면, 학습이 잘된 머신러닝 시스템은 비록 제 한적이지만 겹반사만 효과적으로 완화시키는 것으로 나타났다. 겹반사를 제거하는 머신러닝 시스템의 성능은 입력자료(Fig. 12a)와 예측결과(Fig. 12b)의 차이 자료(Fig. 12d)의 값이 뮤팅 경계를 제외하고 최소화되는 점에서 확인할 수 있다. 앞서 수행한 모델링 자료에 비해 현장자료에는 각종 많은 잡음들이 포함되어있어 해저면반사파의 형태 및 진폭이 훈련 자료와 많이 다른 양상을 보였다. 특히 현장자료는 잡음들의 간섭으로 진폭 변화가 커서 보다 정확한 연산으로 자료를 정 규화시키는 작업이 필요하다. 한 예로 Fig. 12c에서 5,700 ms 이후에 보이는 인공잡음 이벤트들은 진폭의 불완전한 정규화 과정 및 막바지 시간대에서의 이동 샘플링 윈도의 제한으로 발생한 것으로 해석된다. 이번 연구에서는 정규화기법의 한계 로 강한 반사파의 신호까지 그 진폭이 함께 완화되어 결과적 으로 출력 해상도가 저하된 것으로 나타났다. 차후에는 SRME 는 적용되지 않고 NMO 보정만 적용된 자료 혹은 SRME와 NMO 보정 모두 적용되지 않은 원시자료를 대상으로 학습자 료를 구축할 때 정규화기법 등을 포함한 보다 효율적인 모듈 들이 개발될 것으로 기대한다. 결 론 해양탄성파 탐사자료에서 수반되는 겹반사를 제거하기 위해 U-Net 구조를 기본으로 하는 머신러닝 시스템을 활용하였다. 겹반사 제거의 성능을 확인하고자 2D Marmousi2 속도구조에 대하여 수집한 수치모델링 자료에서 수직시간차가 보정된 공 통중간점 모음자료를 머신러닝 시스템의 훈련에 사용하였다. 겹반사가 포함된 자료와 일차반사파만을 가진 자료를 각각 입 력데이터와 레이블데이터로 활용하여 훈련을 시켰고 이 훈련 된 머신러닝 시스템을 통해 출력된 예측결과에서 일차 반사파 는 그대로 유지한 상태에서 대부분의 겹반사파가 효과적으로 제거되는 것으로 나타났다. 이 연구에서는 심층 구조의 U-Net 모델이 겹반사파 완화에 훌륭한 성능을 보여주는 머신러닝 모델임을 확인 할 수 있었

9 176 남호수 임보성 권일룡 김지수 Fig. 12. Multiple attenuation performance test using seismic field data: (a) input data with the Surface Related Multiple Elimination (SRME) technique applied, (b) prediction result, (c) processing result with Radon filtering, and (d) the difference between panels (a) and (b). 으며전통적인해양탄성파자료처리방식과함께머신러닝시스템을활용하여실제현장자료에적용할수있는가능성을보여주었다. 현장자료는모델링자료에비해진폭변화가커지게되므로이에대한자료의정규화작업이필요하였다. 차후에는 SRME 와 NMO 보정모두적용되지않은원시자료를대상으로훈련자료를구축할때보다효율적인정규화기법이개발될것으로기대한다. 해양탄성파탐사자료해석분야에서머신러닝시스템을활용하는연구는활성화초기단계로보이며이번연구에서활용한 U-Net과같은검증된머신러닝모델을이용하는연구방식과더불어해양탄성파자료처리를위한자체적인머신러닝모델개발과같은후속연구를통해다양한자료처리문제들이더욱쉽고빠르게해결될수있을것으로보인다. 감사의글 이논문은충북대학교국립대학육성사업 (2020) 지원을받아작성되었습니다. 완성도있는논문을위해세심한논평과조언을해주신심사위원분들과편집위원들께감사드립니다. References Choi, W., Lee, G., Cho, S., Choi, B., and Pyun, S., 2020, Fault Detection for Seismic Data Interpretation Based on Machine Learning: Research Trends and Technological Introduction, Geophys. and Geophys. Explor., 23(2), (in Korean with English abstract), doi: /GGE Chollet, F., 2018, Deep Learning mit Python und Keras, Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek, MITP-Verlags GmbH & Co. KG. Dahl-Jensen, T., 1989, Reflection Seismic Studies in the Baltic Shield: Special Processing Techniques and Results, Uppsala University, 125p. Deng, L., and Yu, D., 2014, Deep Learning: Methods and Applications, Found. Trends Signal Process., 7(3-4), doi: / Hahnloser, R., Sarpeshkar, R., Mahowald, M. A., Douglas, R. J., and Seung, H. S., 2000, Digital Selection and Analogue Amplification Coexist in a Cortex-inspired Silicon Circuit, Nature, 405(6789), , doi: / Hampson, D., 1986, Inverse Velocity Stacking for Multiple Elimination, 56th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, , doi: / Hatton, L., Worthington, M. H., and Makin, J., 1986, Seismic Data Processing: Theory and Practice, Oxford, Blackwell Scientific Publications. Jo, J. H., and Ha, W., 2020, Case Analysis of Applications of Seismic Data Denoising Methods using Deep-Learning Techniques, Geophys. and Geophys. Explor., 23(2), (in Korean with English abstract), doi: /GGE Lee, W., 2017, A Deep Learning Analysis of the KOSPI s Directions, Journal of the Korean Data & Information

10 머신러닝을이용한탄성파반사법자료의해저면겹반사제거 177 Science Society, 28(2), (in Korean with English abstract), doi: /jkdi Lokshtanov, D., 1995, Multiple Suppression by Single Channel and Multichannel Deconvolution in the Tau-P Domain, 65th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, , doi: / Luporini, F., Louboutin, M., Lange, M., Kukreja, N., Witte, P., Huckelheim, J., Yount, C., Kelly, P. H. J., Herrmann, F. J., and Gorman, G. J., 2020, Architecture and performance of Devito, a system for automated stencil computation, ACM Trans. Math. Softw., 46(1), 6, doi: / Martin, G. S., 2004, The Marmousi2 model, elastic synthetic data, and an analysis of imaging and AVO in a structurally complex environment, Master s dissertation, University of Houston. Naidu, P., Santosh, Chand, S., and Saxena, U. C., 2013, Surface Related Multiple Elimination: A Case study from East Coast India, 10 th Biennial International Conference & Exposition., 217p Nam, H. S., 2020, Attenuation of the Multiples in Seabed Seismic Reflection Data using Machine Learning System, Master s dissertation, Chungbuk National University, 49p. Peacock, K. L., and Treitel, S., 1969, Predictive Deconvolution: Theory and Practice, Geophysics, 34(2), , doi: / Ronneberger, O., Fischer, P., and Brox, T., 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation, Med. Image Comput. Comput. Assist. Interv., , doi: / _28. Sheriff, R. E., and Geldart, L.P., 1995, Exploration Seismology, 2nd Ed., Cambridge University Press, doi: / CBO Siahkoohi, A., Verschuur, D. J., and Herrmann, F. J., 2019, Surface-related multiple elimination with deep learning, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, , doi: /segam Stewart, P. G., Jones, I. F., and Hardy, P. B., 2007, Solutions for Deep Water Imaging, GeoHorizons, Wang, T., Wang, D., and Sun, J., 2017, Closed-loop SRME based on 3D L1-norm sparse inversion. Acta Geophys., 65, , doi: /s

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