基于核混合效应回归模型的地震数据预测

针对地震观测数据难以准确预测的难题，提出基于核混合效应回归模型。为验证该算法模型的可行性，结合湖北地震台站地球物理仪器产出数据开展仿真实验，并与传统的神经网络算法作对比。结果表明，该模型能准确预测地震地球物理观测数据且性能优于其他神经网络算法，对水温、水位数据的预测相对误差低于0.05%及0.48%。该研究为地震监测预报人员积累、分析地震基础数据提供了全新思路，同时也为较复杂的深度学习类算法框架模型的构建提供了实践基础。

Seismic Data Prediction Based on Regression Model of Nuclear Mixed Effects

Aiming at the difficulty of accurate prediction of seismic observation data, we propose a regression model based on nuclear mixed effects. In order to verify the feasibility of the algorithm model, we perform a simulation experiment with the output data of the geophysical instrument of the Hubei Seismic Station and compare it with the traditional neural network algorithm. The results show that the model can accurately predict the seismic and geophysical observation data and the performance is better than other neural network algorithms. The relative error of the water temperature and water level data prediction is less than 0.05% and 0.48%. The proposed model provides a new research idea for earthquake monitoring and forecasting personnel to accumulate and analyze basic earthquake data. At the same time, it also provides practical foundation and research possibilities for more complex deep learning algorithm framework models.