基于小波分析的地下流体监测数据分形特征研究

Fractal Features of Groundwater Monitoring Data Based on Wavelet Analysis

摘要: 长期以来，地震预报主要是通过观测和分析前兆数据进行的。而地下流体数据作为前兆数据的重要组成部分，对地震的孕育和发生过程有十分灵敏的前兆响应。将分形理论用于地下流体数据分析，从而可以更好的识别地下流体数据异常。本文利用小波方法对地下流体数据的分形特征进行研究，通过地下流体数据的相空间系数分布验证前兆数据自相似的分形特征。

Abstract: Earthquake prediction is mainly through observation and analysis of precursor data for a long time. The underground fluid data, as an important part of precursory data, have a very sensitive response to the process of birth and an earthquake precursor. By applying fractal theory to analyze underground fluid data, it can identify the abnormal of fluid data. This paper uses wavelet method study on fractal feature of underground fluid data, and through phase space coefficient distribution of underground fluid data, verifies fractal feature of precursor data self similarity.