Machine learning: A new approach to intelligent exploration of seafloor mineral resources

Yang LIU, Sanzhong LI, Shihua ZHONG, Guanghui GUO, Jiaqing LIU, Jinghui NIU, Zimeng XUE, Jianping ZHOU, Hao DONG, Yanhui SUO

PDF(3090 KB)
PDF(3090 KB)
Earth Science Frontiers ›› 2024, Vol. 31 ›› Issue (3) : 520-529. DOI: 10.13745/j.esf.sf.2023.5.90

Machine learning: A new approach to intelligent exploration of seafloor mineral resources

Author information +
History +

Abstract

The seafloor is characterized by abundant key mineral resources, which is a hotspot of current research and a new field of industry in the future. With the continuous progress of ocean exploration technology, the volume and dimensions of data from seafloor mineral exploration have increased dramatically, which has brought great difficulties and challenges to data processing and interpretation. In the face of massive data, traditional data interpretation and analysis methods expose many problems. Machine learning, with its strong self-learning ability, provides a series of intelligent analysis and decision-making solutions for unsolvable or difficult problems, improving the efficiency of data analysis. It is a new way for the intelligent exploration of subsea mineral resources. In recent years, machine learning has obtained extensive attention and research in the field of geosciences. Therefore, focusing on the application of machine learning to seafloor resource exploration technology, this paper firstly briefly introduces the classical model algorithms in machine learning; then elaborates on the application status of machine learning in two aspects of seafloor energy resources and metal mineral, and finally summarizes the application prospects of machine learning in the field of intelligent exploration of seafloor minerals, points out the problems in existing research, and proposes solutions and future development directions.

Key words

machine learning / ferromanganese nodule / cobalt-rich crusts / natural gas hydrate / seafloor minerals

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
Yang LIU , Sanzhong LI , Shihua ZHONG , et al . Machine learning: A new approach to intelligent exploration of seafloor mineral resources. Earth Science Frontiers. 2024, 31(3): 520-529 https://doi.org/10.13745/j.esf.sf.2023.5.90

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
[1]
PETERSEN S. Marine mineral resources[M]//HARFF J, MESCHEDE M, PETERSEN S, et al. Encyclopedia of marine geosciences. Dordrecht: Springer, 2016: 475-480.
本文引用 [1]
[2]
PETERSEN S, KRÄTSCHELL A, AUGUSTIN N, et al. News from the seabed-geological characteristics and resource potential of deep-sea mineral resources[J]. Marine Policy, 2016, 70: 175-187.
本文引用 [1]
[3]
RONA P A. The changing vision of marine minerals[J]. Ore Geology Reviews, 2007, 33(3): 618-666.
本文引用 [1]
[4]
石学法, 符亚洲, 李兵, 等. 我国深海矿产研究: 进展与发现(2011—2020)[J]. 矿物岩石地球化学通报, 2021, 40(2): 305-318.
本文引用 [1]
[5]
周守为, 李清平, 朱海山, 等. 海洋能源勘探开发技术现状与展望[J]. 中国工程科学, 2016, 18(2): 19-31.
本文引用 [1]
[6]
HEIN J R, KOSCHINSKY A, KUHN T. Deep-ocean polymetallic nodules as a resource for critical materials[J]. Nature Reviews Earth & Environment, 2020, 1(3): 158-169.
本文引用 [1]
[7]
杨燕子, 陈华勇. 大洋富钴结壳研究进展及展望[J]. 大地构造与成矿学, 2023, 47(1): 1-19.
本文引用 [1]
[8]
HANNINGTON M, JAMIESON J, MONECKE T, et al. The abundance of seafloor massive sulfide deposits[J]. Geology, 2011, 39(12): 1155-1158.
本文引用 [1]
[9]
KVENVOLDEN K. Gas hydrates: geological perspective and global change[J]. Reviews of Geophysics, 1993, 31(2): 173-187.
本文引用 [1]
[10]
BERGEN K J, JOHNSON P A, DE HOOP M V, et al. Machine learning for data-driven discovery in solid Earth geoscience[J]. Science, 2019, 363(6433): eaau0323.
本文引用 [6]
[11]
DAI H C, MACBETH C. Automatic picking of seismic arrivals in local earthquake data using an artificial neural network[J]. Geophysical Journal International, 1995, 120(3): 758-774.
本文引用 [1]
[12]
DOWLA F, TAYLOR S R, ANDERSON R W. Seismic discrimination with artificial neural networks: preliminary results with regional spectral data[J]. Bulletin of the Seismological Society of America, 1990, 80(5): 1346-1373.
本文引用 [1]
[13]
DYSART P, PULLI J. Regional seismic event classification at the NORESS array: seismological measurements and the use of trained neural networks[J]. Bulletin of the Seismological Society of America, 1990, 80(6B): 1910-1933.
本文引用 [1]
[14]
周永章, 左仁广, 刘刚, 等. 数学地球科学跨越发展的十年: 大数据、人工智能算法正在改变地质学[J]. 矿物岩石地球化学通报, 2021, 40(3): 556-573, 777.
本文引用 [2]
[15]
周永章, 张良均, 张奥多, 等. 地球科学大数据挖掘与机器学习[M]. 广州: 中山大学出版社, 2018.
本文引用 [1]
[16]
REICHSTEIN M, CAMPS-VALLS G, STEVENS B, et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature, 2019, 566(7743): 195-204.
本文引用 [1]
[17]
COX D R. The regression analysis of binary sequences[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1959, 21(1): 238.
本文引用 [1]
[18]
CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297.
本文引用 [1]
[19]
VAPNIK V N. An overview of statistical learning theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988-999.
本文引用 [1]
[20]
BURGES C J C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
本文引用 [1]
[21]
CHANG C C, LIN C J. LIBSVM[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-27.
本文引用 [1]
[22]
BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
本文引用 [2]
[23]
BREIMAN L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.
本文引用 [1]
[24]
LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
本文引用 [1]
[25]
LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
本文引用 [1]
[26]
ABDEYAZDAN M. Data clustering based on hybrid K-harmonic means and modifier imperialist competitive algorithm[J]. The Journal of Supercomputing, 2014, 68(2): 574-598.
本文引用 [1]
[27]
FRADKIN D. Within-class and unsupervised clustering improve accuracy and extract local structure for supervised classification[D]. New Brunswick: Rutgers, The State University of New Jersey, 2006.
本文引用 [1]
[28]
GOODFELLOW I, BENGIO Y, COURVILLE A. Deep learning[M]. Cambridge: The MIT Press, 2016.
本文引用 [1]
[29]
BOURLARD H, KAMP Y. Auto-association by multilayer perceptrons and singular value decomposition[J]. Biological Cybernetics, 1988, 59(4): 291-294.
本文引用 [1]
[30]
ZHAI J H, ZHANG S F, CHEN J F, et al. Autoencoder and its various variants[C]//Proceedings of 2018 IEEE international conference on systems, man, and cybernetics (SMC). Miyazaki: IEEE, 2018: 415-419.
本文引用 [1]
[31]
LIM B, YU H, YOON D, et al. Machine learning derived AVO analysis on marine 3D seismic data over gas reservoirs near South Korea[J]. Journal of Petroleum Science and Engineering, 2021, 197: 108105.
本文引用 [4]
[32]
ZHU L Q, WEI J G, WU S G, et al. Application of unlabelled big data and deep semi-supervised learning to significantly improve the logging interpretation accuracy for deep-sea gas hydrate-bearing sediment reservoirs[J]. Energy Reports, 2022, 8: 2947-2963.
本文引用 [4]
[33]
MONDAL I, SINGH H K. Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates, Indian offshore[J]. Energy Geoscience, 2022, 3(1): 49-62.
本文引用 [4]
[34]
YOU J C, CAO J X, WANG X J, et al. Shear wave velocity prediction based on LSTM and its application for morphology identification and saturation inversion of gas hydrate[J]. Journal of Petroleum Science and Engineering, 2021, 205: 109027.
本文引用 [4]
[35]
PANG Y M, SHI B B, GUO X W, et al. Source-reservoir relationships and hydrocarbon charging history in the central uplift of the South Yellow Sea Basin (East Asia): constrained by machine learning procedure and basin modeling[J]. Marine and Petroleum Geology, 2021, 123: 104731.
本文引用 [1]
[36]
HAN F L, ZHANG H B, RUI J W, et al. Multiple point geostatistical simulation with adaptive filter derived from neural network for sedimentary facies classification[J]. Marine and Petroleum Geology, 2020, 118: 104406.
本文引用 [1]
[37]
BROWN N, ROUBÍČKOVÁ A, LAMPAKI I, et al. Machine learning on Crays to optimize petrophysical workflows in oil and gas exploration[J]. Concurrency and Computation: Practice and Experience, 2020, 32(20): e5655.
本文引用 [1]
[38]
MUKHERJEE B, SAIN K. Prediction of reservoir parameters in gas hydrate sediments using artificial intelligence (AI): a case study in Krishna-Godavari Basin (NGHP Exp-02)[J]. Journal of Earth System Science, 2019, 128(7): 199.
本文引用 [2]
[39]
CHONG L, SINGH H, CREASON C G, et al. Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA)[J]. Computational Geosciences, 2022, 26(5): 1151-1165.
本文引用 [1]
[40]
SINGH H, SEOL Y, MYSHAKIN E M. Prediction of gas hydrate saturation using machine learning and optimal set of well-logs[J]. Computational Geosciences, 2021, 25(1): 267-283.
本文引用 [1]
[41]
LI C, LIU X. Research on the estimate of gas hydrate saturation based on LSTM recurrent neural network[J]. Energies, 2020, 13(24): 6536.
本文引用 [1]
[42]
LEE J, BYUN J, KIM B, et al. Delineation of gas hydrate reservoirs in the Ulleung Basin using unsupervised multi-attribute clustering without well log data[J]. Journal of Natural Gas Science and Engineering, 2017, 46: 326-337.
本文引用 [1]
[43]
DUTKIEWICZ A, JUDGE A, MÜLLER R D. Environmental predictors of deep-sea polymetallic nodule occurrence in the global ocean[J]. Geology, 2020, 48(3): 293-297.
本文引用 [4]
[44]
HARI V N, KALYAN B, CHITRE M, et al. Spatial modeling of deep-sea ferromanganese nodules with limited data using neural networks[J]. IEEE Journal of Oceanic Engineering, 2018, 43(4): 997-1014.
本文引用 [3]
[45]
JULIANI C, JULIANI E. Deep learning of terrain morphology and pattern discovery via network-based representational similarity analysis for deep-sea mineral exploration[J]. Ore Geology Reviews, 2021, 129: 103936.
本文引用 [4]
[46]
MIMURA K, NAKAMURA K, TAKAO K, et al. Automated detection of hydrothermal emission signatures from multi-beam echo sounder images using deep learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 2703-2710.
本文引用 [3]
[47]
GAZIS I Z, GREINERT J. Importance of spatial autocorrelation in machine learning modeling of polymetallic nodules, model uncertainty and transferability at local scale[J]. Minerals, 2021, 11(11): 1172.
本文引用 [4]
[48]
SCHOENING T, KUHN T, NATTKEMPER T. Estimation of poly-metallic nodule coverage in benthic images[C]//Proceedings of the 41st conference of the Underwater Mining Institute (UMI). Shanghai: UMI, 2012: 15-20.
本文引用 [1]
[49]
邓君兰, 董澧辉, 宋伟, 等. 基于Sea-thru和Mask R-CNN的深海多金属结核图像处理[J]. 矿冶工程, 2022, 42(2): 9-13.
本文引用 [1]
[50]
PRABHAKARAN K, VARSHNEY N, RAMESH R, et al. Underwater image and video processing to detect polymetallic nodule abundance using haar-cascade and template matching feature[C]//Proceedings of OCEANS 2022-Chennai. Chennai: IEEE, 2022: 1-6.
本文引用 [1]
[51]
GAZIS I Z, SCHOENING T, ALEVIZOS E, et al. Quantitative mapping and predictive modeling of Mn nodules’ distribution from hydroacoustic and optical AUV data linked by random forests machine learning[J]. Biogeosciences, 2018, 15(23): 7347-7377.
本文引用 [1]
[52]
HU G, ZHAO H M, HAN F L, et al. Recognition of cobalt-rich crusts based on multi-classifier fusion in seafloor mining environments[J]. Marine Georesources & Geotechnology, 2021, 39(10): 1205-1214.
本文引用 [1]
[53]
HONG F, HUANG M Y, FENG H H, et al. First demonstration of recognition of manganese crust by deep-learning networks with a parametric acoustic probe[J]. Minerals, 2022, 12(2): 249.
本文引用 [1]
[54]
NEETTIYATH U, SATO T, SANGEKAR M, et al. Identification of manganese crusts in 3D visual reconstructions to filter geo-registered acoustic sub-surface measurements[C]//Proceedings of OCEANS 2015-MTS/IEEE Washington. Washington: IEEE, 2015: 1-6.
本文引用 [1]
[55]
NEETTIYATH U, THORNTON B, SANGEKAR M, et al. An AUV based method for estimating hectare-scale distributions of deep sea cobalt-rich manganese crust deposits[C]//Proceedings of OCEANS 2019-Marseille. Marseille: IEEE, 2019: 1-6.
本文引用 [1]
[56]
LIU L S, LU J L, TAO C H, et al. Fuzzy forest machine learning predictive model for mineral prospectivity: a case study on southwest Indian ridge 48. 7°E-50. 5°E[J]. Natural Resources Research, 2022, 31(1): 99-116.
本文引用 [1]
[57]
ZHANG X N, SONG S J, LI J B, et al. Robust LS-SVM regression for ore grade estimation in a seafloor hydrothermal sulphide deposit[J]. Acta Oceanologica Sinica, 2013, 32(8): 16-25.
本文引用 [1]
[58]
ZHANG X N, SONG S J, WU C. Robust Bayesian classification with incomplete data[J]. Cognitive Computation, 2013, 5(2): 170-187.
本文引用 [1]

Comments

PDF(3090 KB)

Accesses

Citation

Detail
Sections
Recommended
Copyright © Earth Science Frontiers, All Rights Reserved.
Powered by Beijing Magtech Co. Ltd.

/

〈 〉