Machine Learning–Based Digital Integration of Geotechnical and Ultrahigh–Frequency Geophysical Data for Offshore Site Characterizations
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 147, Issue 12
Abstract
Geophysical data play a vital role in offshore site characterizations, especially during the planning phase of the geotechnical investigation, in order to define the scope to be performed at the site most effectively, but also to aid interpretation once geotechnical data are acquired. Although geophysical data are advantageous in imaging the subsurface conditions over large offshore areas and can reveal important information about seabed features and the sediment depositional history, the information obtained is usually qualitative from a geotechnical perspective. In today’s practice, the engineering properties of soils for design purposes are determined by established ground-truth methods (e.g., offshore sampling, seabed drilling, and in situ testing, among others) applied directly at locations of proposed infrastructure. However, recent advances in seismic acquisition and interpretation methods, machine learning algorithms as well as computational power, provide opportunities to fully integrate geophysical and geotechnical (G&G) data in a digital format to quantitatively assess the site variability and design parameters with limited geotechnical data. Therefore, the motivations of this paper are to advance the quantitative G&G integration to benefit the broader engineering community and to provide thoughts on improving this technique for industrial applications. The scope of the paper is to demonstrate a successful G&G integration workflow that mainly consists of acoustic impedance inversion on ultrahigh–frequency () geophysical data, and the integration with cone penetration test (CPT) data using artificial neural networks to create synthetic CPT profiles. The proposed workflow has been blindly tested and satisfactory results are achieved for predicting the CPT profiles when compared with the actual data.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The following data, models, and code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions: the original geophysical and geotechnical data, the MATLAB source code for the artificial neural network model.
Acknowledgments
We would like to thank the management of Shell Global Solutions (US), Inc. and Shell International Exploration and Production Inc. for the permission to publish this paper. The views and opinions are those of the authors alone and do not necessarily reflect those of any of the sponsors or other contributors.
References
ASTM. 2019. Standard test methods for laboratory determination of water (moisture) content of soil and rock by mass. ASTM D2216-19. West Conshohocken, PA: ASTM.
Aubeny, C., R. Dutt, A. G. Young, J. D. Murff, R. B. Gilbert, and E. Doyle. 2013. “An expert panel review of geotechnical site investigation regulations and current industry state of practice.” In Proc. Offshore Technology Conf. Richardson, TX: Society of Petroleum Engineers.
Beale, M., M. Hagan, and H. Demuth. 2010. Neural network toolbox user’s guide. Natick, MA: MathWorks.
Bishop, C. M. 2006. Pattern recognition and machine learning. New York: Springer.
Campbell, K. J., S. Kinnear, and A. Thame. 2015. “AUV technology for seabed characterization and geohazards assessment.” Leading Edge 34 (2): 170–178. https://doi.org/10.1190/tle34020170.1.
Chen, J. B., L. Q. Sang, B. Willems, J. Newlin, O. Ruden, R. Sarker, and S. Hu. 2020. “A machine learning based quantitative integration of geotechnical and geophysical data for deepwater site characterization.” In Proc., Frontiers in Offshore Geotechnics IV: 4th Int. Symp. on Frontiers in Offshore Geotechnics (ISFOG 2020). Austin, TX: Deep Foundations Institute.
Claerbout, J. 2014. Geophysical image estimation by example. Morrisville, NC: NCLulu Press.
Das, B. M. 2013. Advanced soil mechanics. Boca Raton, FL: CRC Press.
Doyle, E. H. 1998. “The integration of deepwater geohazard evaluations and geotechnical studies.” In Proc., Offshore Technology Conf. Richardson, TX: Society of Petroleum Engineers.
Endler, M., R. Endler, B. Bobertz, T. Leipe, and H. W. Arz. 2015. “Linkage between acoustic parameters and seabed sediment properties in the south-western Baltic Sea.” Geo-Mar. Lett. 35 (2): 145–160. https://doi.org/10.1007/s00367-015-0397-3.
Foresee, F. D., and M. T. Hagan. 1997. “Gauss-Newton approximation to Bayesian learning.” In Proc., Int. Conf. on Neural Networks (ICNN’97), 1930–1935. New York: IEEE.
Hamilton, E. L. 1972. “Compressional-wave attenuation in marine sediments.” Geophysics 37 (4): 620–646. https://doi.org/10.1190/1.1440287.
Hamilton, E. L., and R. T. Bachman. 1982. “Sound velocity and related properties of marine sediments.” J. Acoust. Soc. Am. 72 (6): 1891–1904. https://doi.org/10.1121/1.388539.
Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
Hernández-Lobato, J. M., and R. Adams. 2015. “Probabilistic backpropagation for scalable learning of Bayesian neural networks.” In Vol. 37 of Proc., Int. Conf. on Machine Learning, 1861–1869. London: Proceedings of Machine Learning Research.
ISO. 2021. Petroleum and natural gas industries specific requirements for offshore structures—Part 10: Marine geophysical investigations. ISO 19901-10. Geneva: ISO.
Jeanjean, P., W. Berger III, E. Liedtke, and D. Lanier. 2006. “Integrated studies to characterize the Mad Dog Spar anchor locations and plan their installation.” In Proc., Offshore Technology Conf. Richardson, TX: Society of Petroleum Engineers.
Lunne, T., J. J. Powell, and P. K. Robertson. 2002. Cone penetration testing in geotechnical practice. Boca Raton, FL: CRC Press.
Medina-Cetina, Z., J. Son, and M. Moradi. 2019. “Bayesian stratigraphy integration of geophysical, geological, and geotechnical surveys data.” In Proc., Offshore Technology Conf. Richardson, TX: Society of Petroleum Engineers.
Neto, A. A., N. Joana de Teixeira Mendes, J. M. G. de Souza, M. Redusino Jr., and R. Leandro Bastos Pontes. 2013. “Geotechnical influence on the acoustic properties of marine sediments of the Santos Basin, Brazil.” Mar. Georesour. Geotechnol. 31 (2): 125–136. https://doi.org/10.1080/1064119X.2012.669815.
Newlin, J. 2003. “Suction anchor piles for the Na Kika FDS mooring system part 1: Site characterization and design.” In Proc., Deepwater Mooring Systems: Concepts, Design, Analysis, and Materials, 28–54. Reston, VA: ASCE.
Pendrel, J. 2001. “Seismic inversion—The best tool for reservoir characterization.” CSEG Recorder 26 (1): 18–24.
Provenzano, G., M. E. Vardy, and T. J. Henstock. 2017. “Pre-stack full waveform inversion of ultra-high-frequency marine seismic reflection data.” Geophys. J. Int. 209 (3): 1593–1611. https://doi.org/10.1093/gji/ggx114.
Richardson, M. D., and K. B. Briggs. 2004. “Empirical predictions of seafloor properties based on remotely measured sediment impedance.” In Proc., AIP Conf. Proc., 12–21. College Park, MD: American Institute of Physics.
Sauvin, G., M. Vanneste, M. E. Vardy, R. T. Klinkvort, and F. Carl Fredrik. 2019. “Machine learning and quantitative ground models for improving offshore wind site characterization.” In Proc., Offshore Technology Conf. Richardson, TX: Society of Petroleum Engineers.
Shen, Y., K. Bao, M. Vissinga, J. Shen, and O. Rodina. 2020. “A novel wavelet extraction method from seismic data without well information.” In Proc., SEG Technical Program Expanded Abstracts 2020, 2241–2245. Tulsa, OK: Society of Exploration Geophysicists.
Vardy, M., M. Clare, M. Vanneste, C. Forsberg, and J. Dix. 2018 “Seismic inversion for site characterization: When, where and why should we use it?” In Proc., Offshore Technology Conf. Richardson, TX: Society of Petroleum Engineers.
Vardy, M. E. 2015. “Deriving shallow-water sediment properties using post-stack acoustic impedance inversion.” Near Surf. Geophys. 13 (2): 143–154. https://doi.org/10.3997/1873-0604.2014045.
Vardy, M. E., M. Vanneste, T. J. Henstock, M. A. Clare, C. F. Forsberg, and G. Provenzano. 2017. “State-of-the-art remote characterization of shallow marine sediments: The road to a fully integrated solution.” Near Surf. Geophys. 15 (4): 387–402. https://doi.org/10.3997/1873-0604.2017024.
Vardy, M. E., M. Vanneste, T. J. Henstock, E. C. Morgan, and L. Pinson. 2015 “Can high-resolution marine geophysical data be inverted for soil properties?” In Vol. 37 of Proc., Institute of Acoustics. New York: Curran Associates.
Wang, H., X. Wang, J. F. Wellmann, and R. Y. Liang. 2019. “A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data.” Can. Geotech. J. 56 (8): 1184–1205. https://doi.org/10.1139/cgj-2017-0709.
Weber, M. E., F. Niessen, G. Kuhn, and M. Wiedicke. 1997. “Calibration and application of marine sedimentary physical properties using a multi-sensor core logger.” Mar. Geol. 136 (3–4): 151–172. https://doi.org/10.1016/S0025-3227(96)00071-0.
Yilmaz, Ö. 2001. Seismic data analysis: Processing, inversion, and interpretation of seismic data. Tulsa, OK: Society of Exploration Geophysicists.
Young, A. G., B. B. Bernard, B. D. Remmes, L. Babb, and J. M. Brooks. 2011. “CPT Stinger-An innovative method to obtain CPT data for integrated geoscience studies.” In Proc., Offshore Technology Conf. Richardson, TX: Society of Petroleum Engineers.
Information & Authors
Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 147 • Issue 12 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 8, 2021
Accepted: Aug 17, 2021
Published online: Sep 30, 2021
Published in print: Dec 1, 2021
Discussion open until: Feb 28, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Stephan Gelinsky, Quantitative Interpretation in Support of the Energy Transition, Day 2 Tue, May 03, 2022, 10.4043/32073-MS, (2022).
- Nina Stark, Brendan Green, Nick Brilli, Emily Eidam, Kevin W. Franke, Kaleb Markert, Geotechnical Measurements for the Investigation and Assessment of Arctic Coastal Erosion—A Review and Outlook, Journal of Marine Science and Engineering, 10.3390/jmse10070914, 10, 7, (914), (2022).
- Haibin Di, Aria Abubakar, Semi-Supervised Learning for Geotechnical Soil Property Estimation in Offshore Windfarm Sites, Day 1 Mon, October 31, 2022, 10.2118/211836-MS, (2022).
- Sergio Chávez-Pérez, Stephan Gelinsky, Exploring to discover — Thoughts on our exploration geophysics ‘road ahead’, Second International Meeting for Applied Geoscience & Energy, 10.1190/image2022-3751621.1, (3092-3094), (2022).
- Jinbo Chen, Shuang Hu, Marianne Vissinga, Yi Shen, Stephan Gelinsky, The role of geophysics in offshore geotechnical site characterizations in the energy transition era: From oil and gas to renewables, Second International Meeting for Applied Geoscience & Energy, 10.1190/image2022-3750540.1, (3059-3060), (2022).
- Mingyuan Wang, Sunjuexu Pan, Yuanqin Tao, Honglei Sun, Xinyi Li, Hierarchical Bayesian modelling of quasi-region-specific soil porosity, Ocean Engineering, 10.1016/j.oceaneng.2022.113052, 266, (113052), (2022).
- Peiyuan Lin, Xianying Chen, Mingjie Jiang, Xugen Song, Meijuan Xu, Sheng Huang, Mapping shear strength and compressibility of soft soils with artificial neural networks, Engineering Geology, 10.1016/j.enggeo.2022.106585, 300, (106585), (2022).
- Bruno Zuada Coelho, Marios Karaoulis, Data fusion of geotechnical and geophysical data for three-dimensional subsoil schematisations, Advanced Engineering Informatics, 10.1016/j.aei.2022.101671, 53, (101671), (2022).