Seismic Discrimination between Earthquakes and Explosions Using CART Algorithm

Abstract

The distinction between earthquake and explosion signals is an essential issue in seismic signal analysis. We propose a machine learning model of decision tree (DT) applied to discriminate between earthquakes and explosions. The amplitudes of the P-wave and the S-wave are selected as feature vectors and built into the database. Classification and regression trees (CART) algorithm is used in our method, which is built through a greedy approach by the Gini impurity. The performance of the DT model using the CART algorithm is evaluated with the ROC curve. The results show the advantages of DT according to various evaluation indexes based on confusion matrix, and demonstrate that DT is efficient in seismic signal discrimination due to the nonparametric model characteristics of DT model.

Analysis of the ammunition explosion seismic wave propagation law

“…Many damage parameters will be produced in the ammunition explosion process, and seismic wave is one of the main damage parameters [1]. Clarifying the explosive seismic wave propagation attenuation law is of great significance to accurately evaluate the ammunition damage power and guide the ammunition structure design [2][3][4][5]. Therefore, in this study, the measured test research is carried out for the ammunition explosive seismic wave, and the propagation attenuation law of explosive seismic wave is analyzed.…”