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2019 Vol.56, Issue 4 Preview Page

General Remarks

New Opportunities and Challenges of Geo-ICT Convergence Technology: GeoCPS and GeoAI

Geo-ICT 융합기술의 새로운 기회와 도전: GeoCPS와 GeoAI

Dongwoo Ryu1*
류 동우1*
1한국지질자원연구원 지오플랫폼연구본부 Geo-ICT 융합연구팀
* Corresponding Author
30 August 2019. pp. 387-397
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Abstract

Cyber-physical systems (CPS) and artificial intelligence (AI), represented as innovative technologies of the 4th industrial revolution, are developing into Geo-ICT, which is ICT convergence technology based on geoscience knowledge. We proposed the contributions of Geo-ICT to both the mining industry and public sector from various perspectives of the 4th industrial revolution. It was confirmed that Geo-ICT could be an alternative to enhance public acceptance through the coevolution of geoscience and civil society. We compared smart factories and smart mining driven by CPS; through this comparison, we defined the concept of GeoCPS and suggested a direction for the development of smart mining technology. In addition, we reviewed conventional AI and cases where it could be applied to geoscience, and addressed the challenging issues of AI and data-driven informatization in geoscience. In particular, we presented a theory-guided data science model as a Geo-AI utilization model that can take into account the process and data characteristics of the geological resources domain and summarize the effects of synergy.

Keywords
GeoCPS
GeoAI
smart mining
theory-guided data science model

4차 산업혁명의 혁신기술로 대표되는 사이버물리시스템(CPS)과 인공지능(AI)은 지질자원 분야 지식기반 ICT 융합기술인 Geo-ICT라는 새로운 기술 영역으로 발전하고 있다. 다양한 관점에서 4차 산업혁명의 의미를 살펴봄으로써 Geo-ICT 융합기술이 산업과 공공분야에 기여할 수 있는 방향을 찾아보았다. Geo-ICT 융합기술이 지질자원 분야와 시민사회와의 공진화에 기여함으로써 공공 수용성을 제고시킬 수 있는 하나의 대안이 될 수 있음을 확인하였다. IoT의 확장된 개념으로서 CPS가 적용되는 스마트 팩토리와 스마트 마이닝의 산업 환경적 차이를 비교하였다. 이를 통해 GeoCPS의 개념적 정의를 내렸고, 국내 자원산업 환경을 고려한 스마트 마이닝의 기술 개발 방향을 제시하였다. 그리고 전통적 AI 방법론과 활용목적별 적용사례를 살펴보고 자원산업의 디지털화와 지질자원 분야 데이터 정보화를 위한 AI의 도전적 이슈들을 짚어 보았다. 특히 지질자원 분야의 프로세스와 데이터 특성을 고려할 수 있는 GeoAI 활용모델로서 이론기반 데이터과학모델을 제시하였으며 시너지 효과를 정리하였다.

키워드
GeoCPS
GeoAI
스마트 마이닝
이론기반 데이터과학모델
References
1
Accenture, 2019.08.07, https://www.accenture.com/us-en/insight- resources-digital-transformation-future-mining
2
ANSI/ISA-95.00.01, 2000. Enterprise-Control System Integration, Part 1: Models and Terminology.
3
ANSI/ISA-95.00.02, 2001. Enterprise-Control System Integration, Part 2: Object Model Attributes, contains additional details and examples to help explain and illustrate the Part 1 objects.
4
ANSI/ISA-95.00.03, 2005. Enterprise-Control System Integration, Part 3: Activity Models of Manufacturing Operations Management, presents models and terminology for defining the activities of manufacturing operations management.
5
ANSI/ISA-95.00.05, 2007. Enterprise-Control System Integration, Part 5: Business-to-Manufacturing Transactions, defines the transactions to interface business and manufacturing activities.
6
Bellman, R., 1978. An Introduction to Artificial Intelligence: Can Computers Think?, Boyd and Fraser Publishing Company, 146p.
7
Bello, J., Silva, C., Nov, O, Dubois, R., Arora, A., Salamon, J., Mydlarz, C., and Doraiswamy, H., 2019. SONYC: a system for monitoring, analyzing, and mitigating urban noise pollution. Communications of the ACM, 62(2), 68-77.
10.1145/3224204
8
Bergen, K., Johnson, P., Hoop, M., and Beroza, G., 2019. Machine learning for data-driven discovery in solid Earth geoscience. Science, 363(6433), eaau0323.
10.1126/science.aau032330898903
9
Bianco, M. and Gerstoft, P., 2017. Travel time tomography with adaptive dictionaries. IEEE Transactions on Computational Imaging, 4, 499-511.
10.1109/TCI.2018.2862644
10
Carneiro, C., Fraser, S., Crósta, A., Silva, A., and Barros, C., 2012. Semiautomated geologic mapping using self-organizing maps and airborne geophysics in the Brazilian Amazon. Geophysics, 77(4), K17-K24.
10.1190/geo2011-0302.1
11
Chua, L. and Yang, L., 1988. Cellular neural networks: theory, IEEE Transactions on Circuits and Systems, 35(10), 1273- 1290.
10.1109/31.7601
12
Cortes, C. and Vapnik, V., 1995. Support-vector networks. Machine Learning, 20(3), 273-297.
10.1007/BF00994018
13
Cracknell, M. and Reading, A., 2013. The upside of uncertainty: identification of lithology contact zones from airborne geophysics and satellite data using random forests and support vector machines. Geophysics, 78(3), WB113-WB126.
10.1190/geo2012-0411.1
14
Cracknell, M. and Reading, A., 2014. Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Computers & Geosciences, 63, 22-33.
10.1016/j.cageo.2013.10.008
15
Derras, B., Bard, P., and Cotton, F., 2014. Towards fully data driven ground-motion prediction models for Europe. Bulletin of Earthquake Engineering, 12(1), 495-516.
10.1007/s10518-013-9481-0
16
DeVries, P., Thompson, T., and Meade, B., 2017. Enabling large-scale viscoelastic calculations via neural network acceleration. Geophysical Research Letters, 44(6), 2662-2669.
10.1002/2017GL072716
17
Dickens, J., Feineman, D., and Roberts, S., 2012. Choices, changes and challenges: lessons for the future development of the digital oilfield. Society of Petroleum Engineers. http://dx.doi.org/10.2118/150173-MS.
10.2118/150173-MS
18
Finazzi, F., 2016. The earthquake network project: Toward a crowdsourced smartphone‐based earthquake early warning system. Bulletin of the Seismological Society of America, 106(3), 1088-1099.
10.1785/0120150354
19
Finn, C., Goodfellow, I., and Levine, S., 2016. Unsupervised learning for physical interaction through video prediction, Proc. of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, p.64-72.
20
Goodfellow, I., Bengio, Y., and Courville, A., 2016. Deep Learning, MIT Press, 800p.
21
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville A., and Bengio, Y., 2014. Generative adversarial networks, Proc. of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, p.2672-2680.
22
Heiskala, M., Jokinen, J., and Tinnilä, M., 2016. Crowdsensing-based transportation services - an analysis from business model and sustainability viewpoints. Research in Transportation Business & Management, 18, 38-48.
10.1016/j.rtbm.2016.03.006
23
Holtzman, B., Paté, A., Paisley, J., Waldhauser, F., and Repetto, D., 2018. Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field. Science Advances, 4(5), eaao2929.
10.1126/sciadv.aao292929806015PMC5966224
24
Hulbert, C., Rouet-Leduc, B., Johnson, P., Ren, C., Riviere, J., Bolton, D., and Marone, C., 2019. Similarity of fast and slow earthquakes illuminated by machine learning. Natural Geoscience, 12, 69-74.
10.1038/s41561-018-0272-8
25
Hunter, T., Moldovan, T., Zaharia, M., Merzgui, S., Ma, J., Franklin, M., Abbeel, P., and Bayen, A., 2011. Scaling the mobile millennium system in the cloud, Proc. of the 2nd ACM Symposium on Cloud Computing, Cascais, Portugal
10.1145/2038916.2038944PMC4945188
26
Kang, J., Kim, I., Lee, S., Ryu, D., and Kwon, J., 2019. A deep CNN-based ground vibration monitoring scheme for MEMS sensed data. IEEE Geoscience and Remote Sensing Letters, 1-5. doi: 10.1109/LGRS.2019.2918641
10.1109/LGRS.2019.2918641
27
Karpatne, A., Atluri, G., Faghmous, J., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S. Samatova, N., and Kumar, V., 2017. Theory-guided data science: a new paradigm for scientific discovery. IEEE Transactions on Knowledge and Data Engineering, 29(10), 2318-2331.
10.1109/TKDE.2017.2720168
28
Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H., and Kumar, V., 2018. Machine learning for the geosciences: challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering, 31(8), 1544-1554.
10.1109/TKDE.2018.2861006
29
Käufl, P., Valentine, A., and Trampert, J., 2016. Probabilistic point source inversion of strong-motion data in 3-D media using pattern recognition: a case study for the 2008 Mw 5.4 Chino Hills earthquake. Geophysical Research Letters, 43(16), 8492-8498.
10.1002/2016GL069887
30
Köhler, A., Ohrnberger, M., Riggelsen, C., and Scherbaum, F., 2008. Unsupervised feature selection for pattern search in seismic time series, Proc. of the 2008 International Conference on New Challenges for Feature Selection in Data Mining and Knowledge Discovery, 4, Antwerp, Belgium, 106-120.
31
Kohonen, T., 2013. Essentials of the self-organizing map. Neural Networks, 37, 52-65.
10.1016/j.neunet.2012.09.01823067803
32
Kong, Q., Allen, R., Schreier, L., and Kwon, Y., 2016. MyShake: a smartphone seismic network for earthquake early warning and beyond. Science Advances, 2(2), e1501055.
10.1126/sciadv.150105526933682PMC4758737
33
Krischer, L. and Fichtner, A., 2017. Generating seismograms with deep neural networks, Proc. of AGU Fall Meeting, S41D-03.
34
Kuhn, S., Cracknell, M., and Reading, A., 2018. Lithologic mapping using random forests applied to geophysical and remote-sensing data: a demonstration study from the Eastern Goldfields of Australia. Geophysics, 83(4), B183- B193.
10.1190/geo2017-0590.1
35
Kurzweil, R., 1990. The Age of Intelligent Machines, MIT Press Cambridge, 565p.
36
Lee, M., 2018. Digital Twrin and Smart Transform, KCERN the 51th Open Forum, Seoul, Korea, 242p.
37
Liu, Y., Mao X., He, Y., Liu, K., Gong, W., and Wang, J., 2013. CitySee: not only a wireless sensor network. IEEE Network, 27(5), 42-47.
10.1109/MNET.2013.6616114
38
Long, Y., She, X., and Mukhopadhyay, S., 2018. HybridNet: integrating model-based and data-driven learning to predict evolution of dynamical systems, Proc. of Conference on Robot Learning, Zürich, Switzerland.
39
Maslow, A., 1943. A theory of human motivation. Psychological Review, 50(4), 370-96.
10.1037/h0054346
40
Mathieu, M., Couprie, C., and LeCun, Y., 2016. Deep multi- scale video prediction beyond mean square error, Proc. of the 4th International Conference on Learning Representations, San Juan, Puerto Rico, 14p.
41
McCann, M., Jin, K., and Unser, M., 2017. Convolutional neural networks for inverse problems in imaging: a review. IEEE Signal Processing Magazine, 34(6), 85-95.
10.1109/MSP.2017.2739299
42
McGagh, J., 2014. Mine of the future, presented in the Internet of Things World Forum 2014, Cicago.
43
Ministry of Science and ICT, 2018. Artificial Intelligence (AI) R&D Strategy for I-Korea 4.0, 40p.
44
Minson, S., Brooks, B., Glennie, C., Murray, J., Langbein, J., Owen, S., Heaton, T., Iannucci, R., and Hauser, D., 2015. Crowdsourced earthquake early warning. Science Advances, 1(3), e1500036.
10.1126/sciadv.150003626601167PMC4640622
45
Mosser, L., Dubrule, O., and Blunt, M., 2017. Reconstruction of three-dimensional porous media using generative adversarial neural networks. Physical Review E, 96, 043309.
10.1103/PhysRevE.96.04330929347591
46
Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin D., Hansen, M., Howard, E., West, R., and Boda, P., 2009. PEIR, the personal environmental impact report, as a platform for participatory sensing systems research, Proc. of the 7th international conference on Mobile systems, applications, and services, NY, USA, p.55-68.
10.1145/1555816.1555823
47
Ochoa, L., Niño, L., and Vargas, C., 2017. Fast magnitude determination using a single seismological station record implementing machine learning techniques. Geodesy and Geodynamics, 9(1), 34-41.
10.1016/j.geog.2017.03.010
48
Perol, T., Gharbi, M., and Denolle, M., 2018. Convolutional neural network for earthquake detection and location. Science Advances, 4(2), e1700578.
10.1126/sciadv.170057829487899PMC5817932
49
Poulton, M., 2002. Neural networks as an intelligence amplification tool: a review of applications. Geophysics, 67(3), 979-993.
10.1190/1.1484539
50
Ravela, S., 2012. Quantifying uncertainty for coherent structures. Procedia Computer Science, 9, 1187-1196.
10.1016/j.procs.2012.04.128
51
Ravela, S., 2015. Statistical inference for coherent fluids. Dynamic Data-Driven Environmental Systems Science, 121-133.
10.1007/978-3-319-25138-7_12
52
Reddy, R. and Nair, R., 2013. The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan. J. of Earth System Science, 122(5), 1423-1434.
10.1007/s12040-013-0346-3
53
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais N., and Prabhat, 2019. Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195-204.
10.1038/s41586-019-0912-130760912
54
Robinson, B., Power, R., and Cameron, M., 2013. A sensitive Twitter earthquake detector, Proc. of the 22nd International Conference on World Wide Web, p.999-1002.
10.1145/2487788.2488101
55
Roden, R., Smith, T., and Sacrey, D., 2015. Geologic pattern recognition from seismic attributes: principal component analysis and self-organizing maps. Interpretation, 3(4), SAE59-SAE83.
10.1190/INT-2015-0037.1
56
Rouet-Leduc, B., Hulbert, C., Bolton, D., Ren, C., Riviere, J., Marone, C., Guyer, R., and Johnson, P., 2018. Estimating fault friction from seismic signals in the laboratory. Geophysical Research Letters, 45(3), 1321-1329.
10.1002/2017GL076708
57
Rouet-Leduc, B., Hulbert, C., and Johnson, P., 2019. Continuous chatter of the Cascadia subduction zone revealed by machine learning. Natural Geoscience, 12, 75-79.
10.1038/s41561-018-0274-6
58
Rouet-Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C., and Johnson, P., 2017. Machine learning predicts laboratory earthquakes. Geophysical Research Letters, 44(18), 9276-9282.
10.1002/2017GL074677
59
Sadowski, P., Fooshee, D., Subrahmanya, N., and Baldi, P., 2016. Synergies between quantum mechanics and machine learning in reaction prediction. J. Chemical Information and Modeling, 56(11), 2125-2128.
10.1021/acs.jcim.6b0035127749058
60
Saputelli, L. A., Bravo C., Moricca, G., Cramer R., Nikolaou, M., Lopez, C., and Mochizuki S., 2013. Best practices and lessons learned after 10 years of digital oilfield (DOF) implementations, SPE 167269, SPE Kuwait Oil and Gas Show and Conference, Kuwait City, Kuwait, 19p.
10.2118/167269-MS
61
Schwab, K., 2016. https://www.weforum.org/about/the-fourth- industrial-revolution-by-klaus-schwab
62
Shoji, D., Noguchi, R., Otsuki, S., and Hino, H., 2018. Classification of volcanic ash particles using a convolutional neural network and probability. Scientific Reports, 8, 8111.
10.1038/s41598-018-26200-229802305PMC5970178
63
Srivastava, N., Mansimov, E., and Salakhudinov, R., 2015. Unsupervised learning of video representations using LSTMs, Proc. of the 32nd International Conference on Machine Learning, Lille, France, p.843-852.
64
Thiagarajan, A., Ravindranath, L., LaCurts, K., Toledo, S., and Eriksson, J., 2009. VTrack: accurate, energy-aware road traffic delay estimation using mobile phones, Proc. of the 7th ACM Conference on Embedded Networked Sensor Systems, Berkeley, California, p.85-98.
10.1145/1644038.1644048
65
Trugman, D. and Shearer, P., 2018. Strong correlation between stress drop and peak ground acceleration for recent M 1-4 earthquakes in the San Francisco bay area. Bulletin of the Seismological Society of America, 108(2), 929-945.
10.1785/0120170245
66
Valentine, A. and Trampert, J., 2012. Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data. Geophysical Journal International, 189(2), 1183-1202.
10.1111/j.1365-246X.2012.05429.x
67
Valera, M., Guo, Z., Kelly, P., Matz, S., Cantu, V., Percus, A., Hyman, J., Srinivasan, G., and Viswanathan, H., 2018. Machine learning for graph-based representations of three dimensional discrete fracture networks. Computational Geosciences, 22(3), 695-710.
10.1007/s10596-018-9720-1
68
Van der Baan, M. and Jutten, C., 2000. Neural networks in geophysical applications. Geophysics, 65(4), 1032-1047.
10.1190/1.1444797
69
Winston, P., 1992. Artificial Intelligence, Pearson, 737p.
70
Wu, X. and Hale, D., 2016. 3D seismic image processing for faults. Geophysics, 81(2), IM1-IM11.
10.1190/geo2015-0380.1
71
Wunsch, C., 2006. Discrete Inverse and State Estimation Problems: With Geophysical Fluid Applications, Cambridge University Press, 384p.
10.1017/CBO9780511535949
72
Xingjian, S., Chen, Z., Wang, H., Yeung, D., Wong, W., and Woo, W., 2015. Convolutional LSTM network: a machine learning approach for precipitation nowcasting, Proc. of the 28th International Conference on Neural Information Processing Systems, Montreal Canada, p.802-810.
73
Yin, J., Karimi, S., Robinson, B., and Cameron, M., 2012. ESA: emergency situation awareness via microbloggers, Proc. of the 21st ACM International Conference on Information and Knowledge Management, Maui, Hawaii, USA, p.2701-2703.
10.1145/2396761.2398732
1)
(사)창조경제연구회, Korea Creative Economy Research Network
2)
International Society of Automation
3)
하드 데이터(hard data)와 대비되는 개념으로, 측정하기 어려운 대상에 대한 정보를 담은 데이터를 소프트 데이터(soft data)라고 하며, 일반적으로 역산모델링 결과나 인간의 판단이나 평가에 의해 제공되는 데이터가 이에 해당한다. 하드 데이터에 비해 불확실성이 매우 크며, 불확실성에 대한 중요한 정보를 제공하는 관측값을 의미할 때도 있다.
Information
  • Publisher :The Korean Society of Mineral and Energy Resources Engineers
  • Publisher(Ko) :한국자원공학회
  • Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
  • Journal Title(Ko) :한국자원공학회지
  • Volume : 56
  • No :4
  • Pages :387-397
  • Received Date : 2019-08-21
  • Revised Date : 2019-08-26
  • Accepted Date : 2019-08-26
  • DOI :https://doi.org/10.32390/ksmer.2019.56.4.383
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