For the average state of the normal distribution parameter estimation, regular normal distribution parameter gives an estimation, but the carbonation depth of influence factors is more of a parameter estimation, shooting low deficiencies; therefore, putting forward application in the carbonation depth of the normal distribution parameter is estimated. A normal distribution parameter estimation model is constructed, and a normal distribution parameter estimation model framework is constructed by using the least squares method to determine the expression of normal distribution parameters. Based on the linear deviation calculation of normal distribution parameters and the determination of the maximum similar value of parameters, the parameter estimation is realized by using the Bayesian function of carbonization depth. The parameter estimation of network signal based on carbonization depth is proposed. Parameter estimation can play an important role in the intelligent analysis of big data, and it is also an important basic guarantee for machine learning algorithms. Using the integrity test results and error rate test result, variable parameters calculated from measured parameters, substitution shooting parameters calculation formula of parameter estimation is put forward by the conventional parameter estimation methods, which shot up to 22.12%, is suitable for the carbonation depth of the normal distribution parameter estimation.

对于正态分布参数估计的平均状态，正态正态分布参数给出了估计，但影响因素的碳化深度更多是参数估计，缺陷少；因此，估计正态分布参数在碳化深度方面的应用。通过最小二乘法确定正态分布参数的表达式，构造了正态分布参数估计模型，并构造了正态分布参数估计模型框架。基于正态分布参数的线性偏差计算和参数最大相似值的确定，利用碳化深度的贝叶斯函数实现参数估计。提出了基于碳化深度的网络信号参数估计。参数估计可以在大数据智能分析中发挥重要作用，也是机器学习算法的重要基础保证。利用完整性测试结果和差错率测试结果，利用常规参数估计方法，提出了由实测参数计算出的可变参数，参数估计的替代射击参数计算公式，其射出率高达22.12％，适用于碳酸盐岩的碳化深度。正态分布参数估计。