Research Article Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification Longjun Dong, 1 Xibing Li, 1 and Gongnan Xie 2 1 School of Resources and Safety Engineering, Central South University, Changsha 410083, China 2 School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710129, China Correspondence should be addressed to Longjun Dong; rydong001@csu.edu.cn Received 26 December 2013; Accepted 16 January 2014; Published 26 February 2014 Academic Editor: Carlo Cattani Copyright © 2014 Longjun Dong et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. he discrimination of seismic event and nuclear explosion is a complex and nonlinear system. he nonlinear methodologies including Random Forests (RF), Support Vector Machines (SVM), and Na¨ ıve Bayes Classiier (NBC) were applied to discriminant seismic events. Twenty earthquakes and twenty-seven explosions with nine ratios of the energies contained within predetermined “velocity windows” and calculated distance are used in discriminators. Based on the one out cross-validation, ROC curve, calculated accuracy of training and test samples, and discriminating performances of RF, SVM, and NBC were discussed and compared. he result of RF method clearly shows the best predictive power with a maximum area of 0.975 under the ROC among RF, SVM, and NBC. he discriminant accuracies of RF, SVM, and NBC for test samples are 92.86%, 85.71%, and 92.86%, respectively. It has been demonstrated that the presented RF model can not only identify seismic event automatically with high accuracy, but also can sort the discriminant indicators according to calculated values of weights. 1. Introduction he problems of seismic source locations and identiications are two of the most important and fundamental issues in earthquake monitoring, microseismic monitoring, analyses of active tectonics, and assessment of seismic hazards [1–4]. Seismic analysts identify seismic signals from those of explosions or blasts by visual inspection and by calculating some characteristics of seismogram. As recorded quarry blasts or nuclear explosions can mislead scientists interpret- ing the active tectonics and lead to erroneous results in the analysis of seismic hazards in the area; an event classiication task is an important step in seismic signal processing. Such task analyses data in order to ind to which class each recorded event belongs. Such work supposes a great deal of workload for seismic analysts. herefore, an automatic classiication tool is neces- sary to be developed for reducing dramatically this arduous task, turning classiication as reliable, as well as removing errors associated with tedious evaluations and change of personnel. Most discrimination methods are designed for a partic- ular source region and a particular distance of the recording station from the epicenter [5]. Some of them heavily depend on the heterogeneity of the uppermost crust in the sense that they might be efective only for a given region. he widely used methods for discriminators include simulating explosion spectra in order to predict spectral details indicative of explosions and not of earthquakes or single-event explosions [6, 7]; examining compressional and shear-wave ratios (amplitude and spectral) between all types of explosions and earthquakes, in an attempt to apply the basic physical conclusion that explosions excite more compressional waves than earthquakes relative to shear waves [8–11]; diferences in high frequency S-to-P ratios between all types of explosions and earthquakes [12–14]; Hindawi Publishing Corporation Abstract and Applied Analysis Volume 2014, Article ID 459137, 8 pages http://dx.doi.org/10.1155/2014/459137