Skip to main content
Erschienen in: Bulletin of Engineering Geology and the Environment 5/2024

01.05.2024 | Original Paper

A flexible and efficient model coupling multi-type data for 2D/3D stratigraphic modeling

verfasst von: Wei Yan, Zheng Guan, Wan-Huan Zhou, Ping Shen

Erschienen in: Bulletin of Engineering Geology and the Environment | Ausgabe 5/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Stratigraphic modeling plays a critical role in characterizing subsurface conditions, while it faces significant uncertainty due to geological heterogeneity and sparse borehole data. Emerging multiple types of stratigraphic data provide a fresh perspective to overcome issues associated with exploiting the intricate stratigraphic relationships from sparse measurements. Presently, most interpolation methods rely solely on stratum types derived from boreholes as a single input, which struggles to efficiently utilize multi-type stratigraphic data, such as dip angle and stratigraphic image. This study addresses the problems by proposing a novel Weighted Ellipse Nearest Neighbors (WENN) approach to efficiently interpolate the stratigraphic model in 2D and 3D with uncertainty quantification. The proposed model could flexibly couple multi-type stratigraphic data without incurring additional costs to deliberately obtain specific mandatory data types. Specifically, when fed with solely stratum types, WENN can be applied in both 2D and 3D contexts with easier application, higher efficiency, and higher accuracy than existing models. The inclusion of dip angles could enhance the accuracy and engineering interpretation of 2D stratigraphic models. With a paralleled stratigraphic image available, WENN can interpolate the gaps using both stratum type, dip angles and stratigraphic image, facilitating the development of a 3D stratigraphic model. WENN offers engineers an alternative approach to handling multiple data types and is adaptable to diverse geotechnical scenarios with varying data availability.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Deng ZP, Jiang SH, Niu JT, Pan M, Liu LL (2020) Stratigraphic uncertainty characterization using generalized coupled Markov chain. Bull Eng Geol Env 79(10):5061–5078CrossRef Deng ZP, Jiang SH, Niu JT, Pan M, Liu LL (2020) Stratigraphic uncertainty characterization using generalized coupled Markov chain. Bull Eng Geol Env 79(10):5061–5078CrossRef
Zurück zum Zitat Feurer, M, Hutter, F (2019) Hyperparameter optimization. Automated machine learning: methods, systems, challenges, pp 3–33 Feurer, M, Hutter, F (2019) Hyperparameter optimization. Automated machine learning: methods, systems, challenges, pp 3–33
Zurück zum Zitat Goldsworthy J (2006). Quantifying the risk of geotechnical site investigations Goldsworthy J (2006). Quantifying the risk of geotechnical site investigations
Zurück zum Zitat Gong W, Zhao C, Juang CH, Tang H, Wang H, Hu X (2020) Stratigraphic uncertainty modelling with random field approach. Comput Geotech 125:103681CrossRef Gong W, Zhao C, Juang CH, Tang H, Wang H, Hu X (2020) Stratigraphic uncertainty modelling with random field approach. Comput Geotech 125:103681CrossRef
Zurück zum Zitat Guan Z, Wang Y (2023) Data-driven simulation of two-dimensional cross-correlated random fields from limited measurements using joint sparse representation. Reliab Eng Syst Saf 238:109408CrossRef Guan Z, Wang Y (2023) Data-driven simulation of two-dimensional cross-correlated random fields from limited measurements using joint sparse representation. Reliab Eng Syst Saf 238:109408CrossRef
Zurück zum Zitat Guan Z, Wang Y, Phoon KK (2024) Dictionary learning of spatial variability at a specific site using data from other sites. J Geotech Geoenviron Eng (in press) Guan Z, Wang Y, Phoon KK (2024) Dictionary learning of spatial variability at a specific site using data from other sites. J Geotech Geoenviron Eng (in press)
Zurück zum Zitat Gweon H, Schonlau M, Steiner SH (2019) The k conditional nearest neighbor algorithm for classification and class probability estimation. PeerJ Comput Sci 5:e194CrossRef Gweon H, Schonlau M, Steiner SH (2019) The k conditional nearest neighbor algorithm for classification and class probability estimation. PeerJ Comput Sci 5:e194CrossRef
Zurück zum Zitat Jankowski S, Hrechka A, Szymański Z, Ryżyński G (2014) Modeling engineering-geological layers by k-nn and neural networks. Commun Comput Inf Sci 440:147–158 Jankowski S, Hrechka A, Szymański Z, Ryżyński G (2014) Modeling engineering-geological layers by k-nn and neural networks. Commun Comput Inf Sci 440:147–158
Zurück zum Zitat Juang CH, Zhang J, Shen M, Hu J (2019) Probabilistic methods for unified treatment of geotechnical and geological uncertainties in a geotechnical analysis. Eng Geol 249:148–161CrossRef Juang CH, Zhang J, Shen M, Hu J (2019) Probabilistic methods for unified treatment of geotechnical and geological uncertainties in a geotechnical analysis. Eng Geol 249:148–161CrossRef
Zurück zum Zitat Li YJ, Hicks MA, Vardon PJ (2016a) Uncertainty reduction and sampling efficiency in slope designs using 3D conditional random fields. Comput Geotech 79:159–172CrossRef Li YJ, Hicks MA, Vardon PJ (2016a) Uncertainty reduction and sampling efficiency in slope designs using 3D conditional random fields. Comput Geotech 79:159–172CrossRef
Zurück zum Zitat Li Z, Wang X, Wang H, Liang RY (2016b) Quantifying stratigraphic uncertainties by stochastic simulation techniques based on Markov random field. Eng Geol 201:106–122CrossRef Li Z, Wang X, Wang H, Liang RY (2016b) Quantifying stratigraphic uncertainties by stochastic simulation techniques based on Markov random field. Eng Geol 201:106–122CrossRef
Zurück zum Zitat Liu WF, Leung YF (2018) Characterising three-dimensional anisotropic spatial correlation of soil properties through in situ test results. Géotechnique 68(9):805–819CrossRef Liu WF, Leung YF (2018) Characterising three-dimensional anisotropic spatial correlation of soil properties through in situ test results. Géotechnique 68(9):805–819CrossRef
Zurück zum Zitat Liu H, Chen S, Hou M, He L (2019) Improved inverse distance weighting method application considering spatial autocorrelation in 3D geological modeling. Earth Sci Inf 13(3):619–632CrossRef Liu H, Chen S, Hou M, He L (2019) Improved inverse distance weighting method application considering spatial autocorrelation in 3D geological modeling. Earth Sci Inf 13(3):619–632CrossRef
Zurück zum Zitat Liu Z, Zhang Z, Zhou C, Ming W, Du Z (2021) An adaptive inverse-distance weighting interpolation method considering spatial differentiation in 3D geological modeling. Geosciences 11(2):51CrossRef Liu Z, Zhang Z, Zhou C, Ming W, Du Z (2021) An adaptive inverse-distance weighting interpolation method considering spatial differentiation in 3D geological modeling. Geosciences 11(2):51CrossRef
Zurück zum Zitat Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34(9):1044–1055CrossRef Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34(9):1044–1055CrossRef
Zurück zum Zitat Malvić T, Ivšinović J, Velić J, Rajić R (2019) Interpolation of small datasets in the sandstone hydrocarbon reservoirs, case study of the sava depression. Croatia. Geosciences 9(5):201CrossRef Malvić T, Ivšinović J, Velić J, Rajić R (2019) Interpolation of small datasets in the sandstone hydrocarbon reservoirs, case study of the sava depression. Croatia. Geosciences 9(5):201CrossRef
Zurück zum Zitat Merwade VM, Maidment DR, Goff JA (2006) Anisotropic considerations while interpolating river channel bathymetry. J Hydrol 331(3–4):731–741CrossRef Merwade VM, Maidment DR, Goff JA (2006) Anisotropic considerations while interpolating river channel bathymetry. J Hydrol 331(3–4):731–741CrossRef
Zurück zum Zitat Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423CrossRef Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423CrossRef
Zurück zum Zitat Shi C, Wang Y (2021a) Development of subsurface geological cross-section from limited site-specific boreholes and prior geological knowledge using iterative convolution XGBoost. J Geotech Geoenviron Eng 147(9):04021082CrossRef Shi C, Wang Y (2021a) Development of subsurface geological cross-section from limited site-specific boreholes and prior geological knowledge using iterative convolution XGBoost. J Geotech Geoenviron Eng 147(9):04021082CrossRef
Zurück zum Zitat Shi C, Wang Y (2022) Data-driven construction of Three-dimensional subsurface geological models from limited Site-specific boreholes and prior geological knowledge for underground digital twin. Tunn Undergr Space Technol 126:104493CrossRef Shi C, Wang Y (2022) Data-driven construction of Three-dimensional subsurface geological models from limited Site-specific boreholes and prior geological knowledge for underground digital twin. Tunn Undergr Space Technol 126:104493CrossRef
Zurück zum Zitat Shi C, Wang Y (2021b) Non-parametric and data-driven interpolation of subsurface soil stratigraphy from limited data using multiple point statistics. Can Geotech J 58(2):261–280CrossRef Shi C, Wang Y (2021b) Non-parametric and data-driven interpolation of subsurface soil stratigraphy from limited data using multiple point statistics. Can Geotech J 58(2):261–280CrossRef
Zurück zum Zitat Tomczak M (1998) Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW)-cross-validation/jackknife approach. J Geogr Inf Decis Anal 2(2):18–30 Tomczak M (1998) Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW)-cross-validation/jackknife approach. J Geogr Inf Decis Anal 2(2):18–30
Zurück zum Zitat Vessia G, Di Curzio D, Castrignanò A (2020) Modeling 3D soil lithotypes variability through geostatistical data fusion of CPT parameters. Sci Total Environ 698:134340CrossRef Vessia G, Di Curzio D, Castrignanò A (2020) Modeling 3D soil lithotypes variability through geostatistical data fusion of CPT parameters. Sci Total Environ 698:134340CrossRef
Zurück zum Zitat Wang X, Li Z, Wang H, Rong Q, Liang RY (2016) Probabilistic analysis of shield-driven tunnel in multiple strata considering stratigraphic uncertainty. Struct Saf 62:88–100CrossRef Wang X, Li Z, Wang H, Rong Q, Liang RY (2016) Probabilistic analysis of shield-driven tunnel in multiple strata considering stratigraphic uncertainty. Struct Saf 62:88–100CrossRef
Zurück zum Zitat Wang X, Wang H, Liang RY (2017) A method for slope stability analysis considering subsurface stratigraphic uncertainty. Landslides 15(5):925–936CrossRef Wang X, Wang H, Liang RY (2017) A method for slope stability analysis considering subsurface stratigraphic uncertainty. Landslides 15(5):925–936CrossRef
Zurück zum Zitat Wei X, Wang H (2022) Stochastic stratigraphic modeling using Bayesian machine learning. Eng Geol 307:106789CrossRef Wei X, Wang H (2022) Stochastic stratigraphic modeling using Bayesian machine learning. Eng Geol 307:106789CrossRef
Zurück zum Zitat Wu CY, Mossa J, Mao L, Almulla M (2019) Comparison of different spatial interpolation methods for historical hydrographic data of the lowermost Mississippi River. Ann GIS 25(2):133–151CrossRef Wu CY, Mossa J, Mao L, Almulla M (2019) Comparison of different spatial interpolation methods for historical hydrographic data of the lowermost Mississippi River. Ann GIS 25(2):133–151CrossRef
Zurück zum Zitat Yan W, Shen P, Zhou WH, Ma GW (2023a) A rigorous random field-based framework for 3D stratigraphic uncertainty modelling. Eng Geol 323:107235CrossRef Yan W, Shen P, Zhou WH, Ma GW (2023a) A rigorous random field-based framework for 3D stratigraphic uncertainty modelling. Eng Geol 323:107235CrossRef
Zurück zum Zitat Yan W, Zhou WH, Shen P (2023b) An uncertainty-driven peak-integration (UP) strategy for 3D borehole layout planning. Comput Geotech 156:105280CrossRef Yan W, Zhou WH, Shen P (2023b) An uncertainty-driven peak-integration (UP) strategy for 3D borehole layout planning. Comput Geotech 156:105280CrossRef
Zurück zum Zitat Yang H, Chu J, Qi X, Wu S, Chiam K (2023a) Bayesian evidential learning of soil-rock interface identification using boreholes. Comput Geotech 162:105638CrossRef Yang H, Chu J, Qi X, Wu S, Chiam K (2023a) Bayesian evidential learning of soil-rock interface identification using boreholes. Comput Geotech 162:105638CrossRef
Zurück zum Zitat Yang H, Chu J, Qi X, Wu S, Chiam K (2023b) Stochastic simulation of geological cross-sections from boreholes: a random field approach with Markov Chain Monte Carlo method. Eng Geol 327:107356CrossRef Yang H, Chu J, Qi X, Wu S, Chiam K (2023b) Stochastic simulation of geological cross-sections from boreholes: a random field approach with Markov Chain Monte Carlo method. Eng Geol 327:107356CrossRef
Zurück zum Zitat Zhang JZ, Liu ZQ, Zhang DM, Huang HW, Phoon KK, Xue YD (2022) Improved coupled Markov chain method for simulating geological uncertainty. Eng Geol 298:106539CrossRef Zhang JZ, Liu ZQ, Zhang DM, Huang HW, Phoon KK, Xue YD (2022) Improved coupled Markov chain method for simulating geological uncertainty. Eng Geol 298:106539CrossRef
Zurück zum Zitat Zhao C, Gong W, Li T, Juang CH, Tang H, Wang H (2021) Probabilistic characterization of subsurface stratigraphic configuration with modified random field approach. Eng Geol 288:106138CrossRef Zhao C, Gong W, Li T, Juang CH, Tang H, Wang H (2021) Probabilistic characterization of subsurface stratigraphic configuration with modified random field approach. Eng Geol 288:106138CrossRef
Metadaten
Titel
A flexible and efficient model coupling multi-type data for 2D/3D stratigraphic modeling
verfasst von
Wei Yan
Zheng Guan
Wan-Huan Zhou
Ping Shen
Publikationsdatum
01.05.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
Bulletin of Engineering Geology and the Environment / Ausgabe 5/2024
Print ISSN: 1435-9529
Elektronische ISSN: 1435-9537
DOI
https://doi.org/10.1007/s10064-024-03677-y

Weitere Artikel der Ausgabe 5/2024

Bulletin of Engineering Geology and the Environment 5/2024 Zur Ausgabe