Based On Time Series Similarity Matching Algorithm For Earthquake Prediction Research

China is earthquake-prone countries. High-frequency seismic activity, strength, wide distribution, light source, a very serious earthquake. To the country and the people brought huge losses. Because many factors that caused the earthquake and a variety of factors have highly uncertain nonlinear relationship. Using data mining techniques can be more systematic, in-depth, comprehensive, detailed research on earthquake prediction analysis play a role in promoting. This paper focuses on earthquake prediction in time series data mining algorithms magnitude theories, methods and practical applications. In this paper, the magnitude of time series data mining started a series of earthquake prediction research. The purpose of the study is based on the characteristics of seismic data, the classic time-series data mining methods and high-performance computer technology combined with studies for earthquake prediction in data mining algorithms to find the law behind the seismic data, identify potential and valuable knowledge of earthquake prediction.Similarity matching of earthquake-related areas of work include the following parts:1. Preprocessing of seismic data, seismic catalog data from the earthquake time, epicenter location, magnitude, composition data and other information, if the management of direct rule mining, excavation The result is that some of the relationship between points, this denoising of seismic data, block, round, discrete cluster, etc. and the main class, the seismic into the seismic data format we need.2. According to the seismic area of knowledge, time and magnitude of the definition of sequence similarity, is proposed based on seismic sequence similarity similarity matching algorithm. The algorithm introduces time, magnitude two-dimensional threshold matching, can quickly match the sequence similarity to the earthquake in the earthquake sequence found in relevant areas.3. Constraint Rule metric model of sequential pattern matching similarity association algorithm, which matches the definition of similarity can be divided into two parts:coarse-grained similar to the match, that the earthquake source directory to find the difference in the number of seismic section a certain threshold margin of seismic area, simply, in a period of time, an earthquake occurred in an area project, another region of the tens of thousands of items of Article earthquake, then the two regions have the possibility of similar the minimum of; fine-grained similarity matching, on the basis of similarity in the rough, the time, magnitude, earthquake location and other information into two-dimensional threshold to support the number of earthquake sequences, need to check on the seismic sequence and seismic data warehouse The earthquake sequence records were compared to find sequences with high similarity of the earthquake. When a higher degree of similarity, the two areas is bound to reflect the occurrence of earthquakes have certain rules on the relationship.4. Realized the cluster system based on parallel data mining platform for earthquake prediction. In the platform of the massive data preprocessing filter time based on similarity matching further increased the horizontal and vertical, multi-regional and multi-time matching; and different time difference, the match threshold, and through a large number of experiments repeated validation of the model, the earthquake in China's earthquake-prone areas in recent decades the history matching experimental data analysis, made more credible experimental results verify the sequence similarity to match the effectiveness of control strategies, practical and algorithm.