Earthquake event classification and recognition method based on pseudo-label algorithm
This paper introduces the pseudo-label algorithm for earthquake type recognition and develops a pseudo-label neural network program to classify three types of earthquake events,namely,natural earthquakes,explosions,and collapses,occurring in the Shandong region from 2019 to 2021,with a magnitude above ML 1.5.The algorithm uses a pseudo-labeling strategy to predict labels for unlabeled samples based on a selected set of labeled data.Once the unlabeled samples are assigned pseudo-labels,they are incorporated into the joint training process.The pa-per also compares the performance of the pseudo-label algorithm with traditional back propaga-tion(BP)neural networks and support vector machines to preliminarily assess its feasibility and applicability,particularly under conditions of limited labeled data.Experimental results show that the classification performance of the pseudo-label neural network method is primarily influenced by the number of labeled samples and the proportion of pseudo-labeled samples.The optimal rec-ognition performance is achieved when the number of labeled samples is between 60 and 120 and the proportion of pseudo-labeled samples is between 20％and 30％.Under small sample condi-tions,the recognition rate of the pseudo-label neural network method is increased by 2％-8％compared to traditional BP neural network methods,and the difference in recognition rate with the support vector machine method is generally within±4％.Therefore,the pseudo-label algo-rithm can help compensate for the shortage of sample data in certain areas,enabling earthquake type recognition under small sample conditions with practical application value.
pseudo-label algorithmearthquake type recognitionneural network methodsmall samples
万方数据
维普
