Appraisal of rock dynamic, physical, and mechanical properties and forecasting shear wave velocity using machine learning and statistical methods

Direct determination of shear wave velocity requires time, cost, and high accuracy due to the complexity of the rock texture. In current research statistical and intelligent approaches have been used to predict the shear wave velocity of rock samples. Also, a new correlation between dynamic and static rock properties was established and the shear wave velocity was estimated based on index tests using Gaussian process regression, multivariate linear regression, feedforward back-propagation artificial neural network, and K-nearest neighbor methods. In total, 120 data related to limestone and sandstone samples of the main projects were used for modeling. Water absorption, compressional wave velocity, and density were used as inputs. The outcomes revealed that the PW/SW ratio is equal to 1.69. Various statistics were used to check the method results. The statistical results showed that it is possible to forecast Ed, and SW with high accuracy. Also, the precision of the GPR was higher than the FBP-ANN, statistical analysis, and KNN. Estimation of SW by GPR showed R of 0.992, and RMSE of 0.06, respectively. These four methods were able to estimate the SW with a mean variation percentage of +0.19%. It's important to consider that these models are best suited for predicting SW when the predictor indicators fall within the same range as this study.