Method for feature analysis and intelligent recognition of infrasound signals of soil landslides
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ORIGINAL PAPER
Method for feature analysis and intelligent recognition of infrasound signals of soil landslides Dunlong Liu 1,2 & Dan Tang 1,2 & Shaojie Zhang 3 & Xiaopeng Leng 4 & Kaiheng Hu 3 & Lei He 1,2 Received: 4 July 2019 / Accepted: 26 September 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract During the catastrophic failure process, the landslide mass emits low-frequency infrasonic waves, which are characterized by strong penetrating power, low energy attenuation, and long propagation distance, providing a basis for the long-range passive monitoring of the landslide infrasound signal. However, current landslide infrasound monitoring technologies are affected by environmental interference noise and frequently produce false positives. To improve the accuracy of landslide infrasound signal recognition, the monitoring signal needs to be analyzed to determine whether it is a landslide infrasound signal. To this end, this study collected numerous infrasound signals generated in the failure processes of landslide masses of different soil types under different degrees of consolidation through laboratory landslide simulation tests. Furthermore, various types of environmental interference infrasound signals in mountainous areas were gathered by field observations. These signals were divided randomly into training sets and test sets according to a ratio of 3:2. Through the feature analysis of the training set data, the typical features of the landslide infrasound and the environmental interference infrasound in both time and frequency domains were summarized. By constructing the feature vector set and regularization process, as well as using technical means such as the K-nearest neighbor (KNN) classification algorithm, Python, Matlab, and database, an intelligent landslide infrasound signal recognition system was developed. The performance of the recognition system was verified using the test set data. The verification results showed that the system has high recognition accuracy and computational efficiency and can meet the accuracy and real-time requirements of landslide infrasound monitoring. In addition, the recognition results of the system can provide an accurate signal source and reliable information support for landslide infrasound early warning. Keywords Soil landslides . Infrasound signals . Environmental interference infrasound signals . Feature analysis . Intelligent recognition
Introduction * Dan Tang [email protected] Dunlong Liu [email protected] 1
College of Software Engineering, Chengdu University of Information and Technology, Chengdu 610225, China
2
Software Automatic Generation and Intelligent Service Key Laboratory of Sichuan Province, Chengdu 610225, China
3
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
4
College of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, China
Landslide disaster causes numerous casualties and property losses worldwide each year; among them, soil
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