The statistical analysis of the main physical, mechanical and ultrasonic features of various sedimentary rock samples (limestone, calcarenite, marly calcisiltite and sandstone) is presented with the aim of finding reliable models to predict the static elastic modulus from known properties. Based on the relevance of the static elastic modulus E_stat in engineering geological applications and on the increasing need of non-destructive procedures for its estimation, this paper attempts to establish prediction models by means of single and multiple regression approaches and Artificial Neural Network (ANN). While in the first two cases rock properties are plotted against each other to study their mutual dependence, even combining more than one independent variable, the Artificial Neural Network approach allows a self-learning model capable of predicting a target value from known input variables to be built. Results show that the static elastic modulus of tested rocks is mainly related to the rock Uniaxial Compressive Strength and it varies with respect to the seismic wave velocities (both compressional Vp and longitudinal Vs) and, consequently, to the dynamic elastic modulus E_dyn calculated by ultrasonic tests. In particular, there is a numerical difference between the static and dynamic values of the elastic modulus, as the dynamic ones are generally higher than the corresponding static values. Satisfactory prediction equations were found by multiple regressions involving Vp, E_dyn and the rock bulk density, thus providing useful statistical laws for the indirect calculation of E_stat. The results arising from the ANN models are used herein to draft E_stat prediction graphs from known variables, in the perspective of a practical utility for the quick and non-destructive estimation of the static elastic property of tested sedimentary rocks.

对各种沉积岩样品（石灰石、石灰石、泥灰方解石和砂岩）的主要物理、机械和超声波特征进行了统计分析，目的是找到可靠的模型来预测已知特性的静态弹性模量。基于静态弹性模量 E _{stat的相关性}在工程地质应用中以及对无损估计程序的日益增长的需求，本文试图通过单回归方法和多元回归方法以及人工神经网络（ANN）建立预测模型。虽然在前两种情况下，将岩石特性相互绘制以研究它们的相互依赖性，甚至结合多个自变量，但人工神经网络方法允许能够根据已知输入变量预测目标值的自学习模型建成。结果表明，被测岩石的静弹性模量主要与岩石的单轴抗压强度有关，并且随地震波速度（纵波Vp和纵波Vs）而变化，因此也与动弹性模量E有关。_dyn通过超声波测试计算。特别是，弹性模量的静态值和动态值之间存在数值差异，因为动态值通常高于相应的静态值。_{通过涉及Vp、E dyn}和岩石容重的多元回归发现了令人满意的预测方程，从而为E _stat的间接计算提供了有用的统计规律。从用于快速和无损估计测试沉积岩的静态弹性特性的实用性的角度来看，从 ANN 模型产生的结果在此用于从已知变量绘制 E _{stat预测图。}