Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

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Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer. / Rogiers, Bart; Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain.

In: PLOS ONE, Vol. 12, No. 5, 03.05.2017, p. 1-24.

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Rogiers, Bart ; Mallants, Dirk ; Batelaan, Okke ; Gedeon, Matej ; Huysmans, Marijke ; Dassargues, Alain. / Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer. In: PLOS ONE. 2017 ; Vol. 12, No. 5. pp. 1-24.

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@article{300f56234adf4b6ca51fad0e6bc2cb6a,
title = "Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer",
abstract = "Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.",
keywords = "Cone penetration testing (CPT), site characterization, soil behaviour type classification (SBT{\`a}, groundwater flow",
author = "Bart Rogiers and Dirk Mallants and Okke Batelaan and Matej Gedeon and Marijke Huysmans and Alain Dassargues",
note = "score=10",
year = "2017",
month = may,
day = "3",
doi = "10.1371/journal.pone.0176656",
language = "English",
volume = "12",
pages = "1--24",
journal = "PLOS ONE",
issn = "1932-6203",
publisher = "PLOS - Public Library of Science",
number = "5",

}

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TY - JOUR

T1 - Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer

AU - Rogiers, Bart

AU - Mallants, Dirk

AU - Batelaan, Okke

AU - Gedeon, Matej

AU - Huysmans, Marijke

AU - Dassargues, Alain

N1 - score=10

PY - 2017/5/3

Y1 - 2017/5/3

N2 - Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

AB - Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

KW - Cone penetration testing (CPT)

KW - site characterization

KW - soil behaviour type classification (SBTà

KW - groundwater flow

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/24760614

U2 - 10.1371/journal.pone.0176656

DO - 10.1371/journal.pone.0176656

M3 - Article

VL - 12

SP - 1

EP - 24

JO - PLOS ONE

JF - PLOS ONE

SN - 1932-6203

IS - 5

ER -

ID: 2554780