Study On Novel Approaches Of Data Ming For Earthquake Prediction

Earthquake prediction is a worldwide challenging problem. With the development of earthquake prediction in the past 30 years, a large amount of prior knowledge and billions of data have been accumulated in our country. The gigantic auspice data under earthquake conditions is recorded by the sensor network of seismological observatory everyday. In this paper, we introduce the advanced data mining techniques into the earthquake prediction field, and several novel approaches between data mining and seismological data analysis are studied. Meanwhile, just by using the techniques of high performance computing and parallel data mining, seismological domain knowledge hidden in the gigantic data can be efficiently discovered to support earthquake prediction, therefore the accuracy of the earthquake prediction can be improved effectively.On the basis of discussing the existing data mining algorithms, the paper mainly focuses on the domain knowledge of seismology and the traditional methods for earthquake prediction. Then, by using the relativity analysis on earthquake zones, the earthquake sequences and the rules of earthquake auspice data, it carries out several parallel data mining algorithms such as association rules based parallel mining algorithm, the seismological similarity and similarity-matching algorithm realization, and the sequential pattern mining algorithm etc. Furthermore, the earthquake auspice data processing method and a series of parallel implement algorithms are proposed based on the technique of time series analysis. Finally, the parallel seismological data mining platform is implemented, which integrates all of the algorithms proposed in this paper.The main contribution of the dissertation is shown as follows:1. By analyzing and discovering the earthquake catalogue data on the relativity of earthquake zones, a Master/Slave mode based parallel mining algorithm FPM-LP (Fast Parallel Mining of Local Pruning) is put forward by using association rules, just as well as the relative preprocessing algorithm is presented. The experimental results demonstrate that the algorithm is satisfactory to find relative earthquake zones.2. On the basis of analyzing the relative earthquake zones on the technique of time series similarity matching, the seismological similarity and similarity-matching model on the relative earthquake zones and its algorithm WSM3S (Whole Sequence Matching Based-on Seismo Similarity Support) are proposed according to the three earthquake essential factors, which are named time, space, intensity separately. Just by...