Rock Burst Monitoring and Early Warning Based on Incremental Learning Method with SVM

Chunfang Wu,Ruisheng Jia,Tao Qiu

doi:10.19026/rjit.5.5797

Abstract

The rock burst hazard is a common geological hazard. In this study, we investigate an approach for classification of rock burst situation. This study relies on support vector machine classifier which in the case of less prior knowledge, still has the ability of classification. First we describes the current research work on rock burst monitoring and early warning and reasons for the introduction of support vector machines and later propose support vector machines algorithm and its improvement strategies. The results illustrate that incremental learning method for support vector machine not only requires less prior knowledge, but also without affecting the performance at the same time and training time will be substantially reduced. The method for rock burst monitoring and early warning has exhibited remarkable detection and generalization performance.

Highlights

Recent years, China's coal mine accidents showed high momentum
Along with the increasing depth of coal mining and the complex of geological conditions, the rock burst hazards has become an important factor in triggering mine geological disaster
Support vector machine will be introduced into the field of rock burst monitoring which can be seen as a classification problem, that is, for a given sensor data: what kind of data is normal, what kind of data anomalies

Summary

INTRODUCTION

Along with the increasing depth of coal mining and the complex of geological conditions, the rock burst hazards has become an important factor in triggering mine geological disaster. The reason that cause the rock burst hazard is extraordinarily complex (Pan et al, 2012; Qi et al, 2011). Support vector machine will be introduced into the field of rock burst monitoring which can be seen as a classification problem, that is, for a given sensor data: what kind of data is normal, what kind of data anomalies. By introducing the support vector machine to the field of rock burst monitoring, we can make the system in the case of a less priori knowledge, still has good generalization ability

OVERVIEW OF SUPPORT VECTOR MACHINE CLASSIFIERS

Decision making system

Using the third part of the introduction of the ACKNOWLEDGMENT

Findings

CONCLUSION