Prediction of elastic compressibility of rock material with soft computing techniques

Zaobao Liu (1) , Jian-Fu Shao (1) , Weiya Xu , Yu Zhang , Hongjie Chen

1 LML - Laboratoire de Mécanique de Lille - FRE 3723

Mechanical and physical properties of sandstone are interesting scientifically and have great practical significance as well as their relations to the mineralogy and pore features. These relations are however highly nonlinear and cannot be easily formulated by conventional methods. This paper investigates the potential of the technique named as the relevance vector machine (RVM) for prediction of the elastic compressibility of sandstone based on its characteristics of physical properties. Based on the fact that the hyper-parameters may have effects on the RVM performance, an iteration method is proposed in this paper to search for optimal hyper-parameter value so that it can produce best predictions. Also, the qualitative sensitivity of the physical properties is investigated by the backward regression analysis. Meanwhile, the hyper-parameter effect of the RVM approach is discussed in the prediction of the elastic compressibility of sandstone. The predicted results of the RVM demonstrate that hyper-parameter values have evident effects on the RVM performance. Comparisons on the results of the RVM, the artificial neural network and the support vector machine prove that the proposed strategy is feasible and reliable for prediction of the elastic compressibility of sandstone based on its physical properties.

Mots clés: Mechanical parameter; Porous material; Artificial neural network; Support vector machine; Soft computing; Relevance vector machine

Zaobao Liu, Jian-Fu Shao, Weiya Xu, Yu Zhang, Hongjie Chen. Prediction of elastic compressibility of rock material with soft computing techniques. Applied Soft Computing, 2014, 22, pp.118-125. ⟨10.1016/j.asoc.2014.05.009⟩. ⟨hal-01025653⟩