[논문]완전 참조 이미지 품질 평가를 이용한 지하 매질 물성 정보 도출 알고리즘의 정확성 평가

최승표; 전형구; 신성렬; 정우근

doi:10.7582/gge.2021.24.1.006

[국내논문] 완전 참조 이미지 품질 평가를 이용한 지하 매질 물성 정보 도출 알고리즘의 정확성 평가
Evaluating Accuracy of Algorithms Providing Subsurface Properties Using Full-Reference Image Quality Assessment 원문보기

지구물리와 물리탐사 = Geophysics and geophysical exploration, v.24 no.1, 2021년, pp.6 - 19

최승표 (한국해양대학교 해양과학기술융합학과) , 전형구 (한국해양과학기술원 해저활성단층연구단) , 신성렬 (한국해양대학교 에너지자원공학과) , 정우근 (한국해양대학교 에너지자원공학과)

초록
AI-Helper

탄성파 탐사는 속도와 밀도 같은 지하 매질 물성 정보를 파악하고 지하 지층 구조를 영상화 할 수 있으며, 이를 위한 다양한 알고리즘 개발이 이루어지고 있다. 이러한 알고리즘의 성능 검증을 위해 다양한 기준 모델이 사용되며, 정확도의 경우 참 물성 데이터와의 평균 제곱근 오차(Root Mean Squre Error, RMSE)를 통해 정량적으로 평가할 수 있다. RMSE는 수치적으로 단순하다는 장점이 있지만 구조적인 품질과의 상관도가 높지 않다는 한계가 있다. 이러한 한계를 보완하기 위해 인간지각시스템을 반영한 FR-IQA (Full Reference Image Quality Assessment) 기법이 연구되고 있으며, 지하 물성 정보 데이터를 다룰 수 있는 FR-IQA 기법들을 선정하였다. 본 연구는 물성 정보 도출 알고리즘으로 완전 파형 역산을 선정하여 세 가지 기준 모델에서 수치예제 실험을 진행하였으며, 선정 된 FR-IQA 기법들을 이용하여 물성 정보 도출 알고리즘 정확성 평가를 수행하였다. 주요 구조 정확성 평가 시 암염모델 하부 구조의 경우 구조적으로 좋지 않음을 육안으로 확인할 수 있었으나 RMSE 값은 감소하며 결과의 부정확성을 표출하지 못하였다. 반면, 몇몇 FR-IQA의 경우 결과의 부정확성을 수치적으로 표출하는 것을 확인하였다.

Abstract ▼ AI-Helper

Subsurface physical properties can be obtained and imaged by seismic exploration, and various algorithms have been developed for this purpose. In this regard, root mean square error (RMSE) has been widely used to quantitatively evaluate the accuracy of the developed algorithms. Although RMSE has the advantage of being numerically simple, it has limitations in assessing structural similarity. To supplement this, full-reference image quality assessment (FR-IQA) techniques, which reflect the human visual system, are being investigated. Therefore, we selected six FR-IQA techniques that could evaluate the obtained physical properties. In this paper, we used the full-waveform inversion, because the algorithm can provide the physical properties. The inversion results were applied to the six selected FR-IQA techniques using three benchmark models. Using salt models, it was confirmed that the inversion results were not satisfactory in some aspects, but the value of RMSE decreased. On the other hand, some FR-IQA techniques could definitely improve the evaluation.

주제어

표/그림 (18)

그림 Fig. 1. Block diagram of the Visual Information Fidelity (VIF) index (Sheikh and Bovik, 2006). C is the signal of the original image, D is the signal of the image distorted by the distortion channel, E is the signal of the original image recognized by HVS, and F is the signal of the distorted image recognized by HVS.
표 Table 1. Running time of the computing IQA models (Xue et al., 2013).
그림 Fig. 3. Smoothed Marmousi2 P-wave velocity models from (a) to (j) were assumed as the number of inversion iterations from 1 to 10. Parameter "r" indicates the smoothing distance.
그림 Fig. 2. P-wave velocity of the Marmousi2 model.
그림 Fig. 4. Evaluation of accuracy of the velocity models with 10-step smoothing filters using (a) RMSE, (b) PSNR, (c) SSIM, (d) MS-SSIM, (e) FSIM, (f) VIF, (g) MAD, and (h) GMSD. The green arrow indicates the direction of higher image quality.
그림 Fig. 5. (a) Initial, (b) inverted, and (c) true velocity models of Marmousi2 P-wave velocity model for FWI.
그림 Fig. 6. (a) Initial, (b) inverted, and (c) true velocity models of SEG/EAGE salt model crossline 4 km sliced 2D P-wave velocity model for FWI.
표 Table 2. Modeling and Inversion parameters
그림 Fig. 8. Evaluation of accuracy of the updated velocity models during FWI iterations with the Marmousi2 model using (a) RMSE, (b) PSNR, (c) SSIM, (d) MS-SSIM, (e) FSIM, (f) VIF, (g) MAD, and (h) GMSD. The green arrow indicates the direction of higher image quality.
그림 Fig. 7. (a) Initial, (b) inverted, and (c) true velocity models of SEG/EAGE overthrust model crossline 11.5 km sliced 2D P-wave velocity model for FWI.
그림 Fig. 9. Evaluation of accuracy of the updated velocity models during FWI iterations with the SEG/EAGE salt model using (a) RMSE, (b) PSNR, (c) SSIM, (d) MS-SSIM, (e) FSIM, (f) VIF, (g) MAD, and (h) GMSD. The green arrow indicates the direction of higher image quality.
그림 Fig. 10. Evaluation of accuracy of the updated velocity models during FWI iterations with the SEG/EAGE overthrust model using (a) RMSE, (b) PSNR, (c) SSIM, (d) MS-SSIM, (e) FSIM, (f) VIF, (g) MAD, and (h) GMSD. The green arrow indicates the direction of higher image quality.
그림 Fig. 11. Location of a selected section in (a) Marmousi2 and (b) SEG/EAGE salt models. White boxes represent the selected sections.
그림 Fig. 12. True and inverted models of four specific sections selected for the Marmousi2 and salt models. The sections represent (a) Marmousi2 gas sand section, (b) Marmousi2 oil cap section, (c) upper salt dome, and (d) lower salt dome.
그림 Fig. 13. Evaluation of accuracy of the updated velocity models during FWI iterations in the Marmousi2 gas sand section using (a) RMSE, (b) PSNR, (c) SSIM, (d) MS-SSIM, (e) FSIM, (f) VIF, (g) MAD, and (h) GMSD. The green arrow indicates the direction of higher image quality.
그림 Fig. 14. Evaluation of accuracy of the updated velocity models during FWI iterations in the Marmousi2 oil cap section using (a) RMSE, (b) PSNR, (c) SSIM, (d) MS-SSIM, (e) FSIM, (f) VIF, (g) MAD, and (h) GMSD. The green arrow indicates the direction of higher image quality. The red arrow indicates the trend of FR-IQA score and is in a direction opposite to that of the green arrow.
그림 Fig. 15. Evaluation of accuracy of the updated velocity models during FWI iterations in the upper salt dome section using (a) RMSE, (b) PSNR, (c) SSIM, (d) MS-SSIM, (e) FSIM, (f) VIF, (g) MAD, and (h) GMSD. The green arrow indicates the direction of higher image quality.
그림 Fig. 16. Evaluation of accuracy of the updated velocity models during FWI iterations in the lower salt dome section using (a) RMSE, (b) PSNR, (c) SSIM, (d) MS-SSIM, (e) FSIM, (f) VIF, (g) MAD, and (h) GMSD. The green arrow indicates the direction of higher image quality. The red arrow indicates the trend of the FR-IQA score and is in a direction opposite to that of the green arrow.

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저자의 다른 논문 :

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