Prediction of elastic compressibility of rock material with soft computing techniques

• The relevance vector machine is evaluated for estimation of rock compressibility based on physical properties.
• An iteration strategy is proposed to optimize the hyper-parameters of the relevance vector machine.
• The parameter effect is demonstrated on the performance of the relevance vector machine.
• The adaptive relevance vector machine is compared to the artificial neural networks and the support vector machine in the estimation.

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.

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