Estimation of dynamic properties of sandstones based on index properties using artifcial neural network and multivariate linear regression methods

Alizadeh, Sayed Mehdi; Iraji, Amin; Tabasi, Somayeh; Ahmed, Alim Al Ayub; Motahari, Mohammad Reza

doi:10.1007/s11600-021-00705-3

Artykuł - szczegóły

Tytuł artykułu

Estimation of dynamic properties of sandstones based on index properties using artifcial neural network and multivariate linear regression methods

Autorzy

Alizadeh Sayed Mehdi , Iraji Amin , Tabasi Somayeh , Ahmed Alim Al Ayub , Motahari Mohammad Reza

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-021-00705-3

Warianty tytułu

Języki publikacji

Abstrakty

The dynamic properties of the rock are very important for the design of geotechnical structures and the modeling of deep drilling. In the present study, the velocity of compressional and shear waves (Vp and Vs) and the dynamic elastic modulus (Ed) of sandstones were estimated based on index tests using artificial neural network (ANN) and multivariate linear regression analysis (MVLRA) methods. For this purpose, petrographic, physical, mechanical and dynamic tests were performed on 54 specimens. Petrographic results showed that the samples were classified as feldspathic litharenite. The results showed that the Vp/Vs ratio was equal to 1.78. Also, the effect of mineralogy on mechanical properties was more than dynamic properties and the effect of quartz on dynamic properties was more than other minerals. The presented relationships were evaluated using R-squared (R2 ), root-mean-square error (RMSE), mean absolute relative prediction error (MARPE), variance account for (VAF) and performance index (PI). The results of the ANN to estimate the Ed, Vp and Vs showed that it is possible to estimate these parameters based on inputs with high accuracy. The accuracy of the ANN was higher than the MVLRA. Estimation of Vs, Vp and Ed by ANN showed correlation coefficients of 0.97, 0.86 and 0.92 and RMSE of 0.10, 0.31, and 3.98, respectively. The ANN was also conservative in predicting these variables, while MVLRA was conservative only in estimating the Vs and Ed of the studied sandstones.

Słowa kluczowe

physical properties mechanical properties petrography artificial neural network multivariate linear regression dynamic elastic properties

właściwości fizyczne właściwości mechaniczne petrografia sztuczna sieć neuronowa wielowymiarowa regresja liniowa

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 1

Strony

225--242

Opis fizyczny

Bibliogr. 71 poz.

Twórcy

autor

Alizadeh Sayed Mehdi

Petroleum Engineering Department, Australian College of Kuwait, West Mishref, Kuwait

autor

Iraji Amin

Engineering Faculty of Khoy, Urmia University of Technology, Urmia, Iran

autor

Tabasi Somayeh

Faculty of Industry and Mining (Khash), University of Sistan and Baluchestan, Zahedan, Iran

autor

Ahmed Alim Al Ayub

School of Accounting, Jiujiang University, 551 Qianjindonglu, Jiujiang, Jiangxi, China

autor

Motahari Mohammad Reza

m-motahari@araku.ac.ir

Department of Civil Engineering, Faculty of Engineering, Arak University, Arak, Iran

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Uwagi

Korekta artykułu w Acta Geophysica Vol. 70, no 1. Nr DOI korekty: 10.1007/s11600-022-00733-7

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

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Bibliografia

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