2020
DOI: 10.1016/j.cageo.2019.104404
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3D stochastic modeling framework for Quaternary sediments using multiple-point statistics: A case study in Minjiang Estuary area, southeast China

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Cited by 53 publications
(15 citation statements)
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“…Sedimentary Layers e Quaternary sediments are slightly or not influenced by the tectonic movement [20], but the distribution of sedimentary strata is fragmented [21], as the phenomenon of lenses and strata pinch-out is common. Compared to investigations such as Quaternary surveying and hydrogeological surveying, EGS-GIS is the finest and most accurate surveying method in the field of sedimentary layers to reflect the engineering properties of Quaternary sediments.…”
Section: Basic Characteristics Of Engineeringmentioning
confidence: 99%
“…Sedimentary Layers e Quaternary sediments are slightly or not influenced by the tectonic movement [20], but the distribution of sedimentary strata is fragmented [21], as the phenomenon of lenses and strata pinch-out is common. Compared to investigations such as Quaternary surveying and hydrogeological surveying, EGS-GIS is the finest and most accurate surveying method in the field of sedimentary layers to reflect the engineering properties of Quaternary sediments.…”
Section: Basic Characteristics Of Engineeringmentioning
confidence: 99%
“…In view of this, the birth of the multiple-point geostatistical simulation method could overcome the shortcomings of the two-point geostatistics more effectively. It equips the advantages of two-point geostatistics and stochastic simulation at the same time and it has been widely used in the eld of earth scienti c research [33][34][35][36][37][38] .…”
Section: Multiple-point Geostatisticsmentioning
confidence: 99%
“…3D geological modeling is an ideal solution to these issues [30][31][32]. Høyer et al [33] constructed a 3D geological model using AEM resistivity data; Lau et al [34] established a 3D geologic model to research the structure of Quaternary deposits; Chen et al [35] constructed a 3D stochastic model to simulate the characterization of the internal attributes of sedimentary strata; Erharter et al [36] developed a 3D stochastic model to represent the sediment bodies; and there are also many technical approaches, such as machine learning, that have been applied to optimize the construction of 3D geological models, especially when fractures and fracture networks are involved [37][38][39]. While the research mentioned above has presented excellent approaches for determining the spatial structure or inner attributes of Quaternary loose sedimentary strata, they mainly focused on the 3D visual interpretation of boreholes [40] and geochemical data [41] (mainly seismic data [42,43]), or 3D simulation on a small scale with a strict hypothesis, and there are few reports on the relevance of the construction of 3D geological models of Quaternary loose sedimentary strata based on identifying the spatial distribution in the whole study area by deep mining little pieces of Quaternary geological field data.…”
Section: Introductionmentioning
confidence: 99%