Method for feature analysis and intelligent recognition of infrasound signals of soil landslides

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.

[1]  Mark R. Bell,et al.  Detection of atmospheric nuclear explosions : the infrasound component of the International monitoring system , 2001 .

[2]  Tian Wei.  MEASUREMENT OF THREE-DIMENSIONAL DYNAMIC FREQUENCY SPECTRA OF INFRASONIC WAVES PRODUCED BY WINDS WITH 4-8 WIND SCALES , 2002 .

[3]  Tong Na.  Features and applications of infrasound , 2003 .

[4]  Jeffrey B. Johnson,et al.  Generation and propagation of infrasonic airwaves from volcanic explosions , 2003 .

[5]  Jerry Hamann,et al.  Results of Recent Infrasound Avalanche Monitoring Studies , 2004 .

[6]  Tim Mendenhall,et al.  Recent Studies Using Infrasound Sensors to Remotely Monitor Avalanche Activity , 2004 .

[7]  Lars Ceranna,et al.  Infrasound associated with 2004–2005 large Sumatra earthquakes and tsunami , 2005 .

[8]  V. M. Krasnov,et al.  The acoustic field in the ionosphere caused by an underground nuclear explosion , 2005 .

[9]  Peter M. Shearer,et al.  Infrasound events detected with the Southern California Seismic Network , 2006 .

[10]  Jerry Hamann,et al.  Single and multiple sensor identification of avalanche-generated infrasound , 2007 .

[11]  Milton Garces,et al.  An infrasound array study of Mount St. Helens , 2007 .

[12]  Ht Chou,et al.  Calibration of infrasound monitoring system and acoustic characteristics of debris-flow movement by field studies , 2007 .

[13]  Läslo Evers,et al.  The inaudible symphony: on the detection and source identification of atmospheric infrasound , 2008 .

[14]  Johannes Hübl,et al.  Infrasound Measurements Of Debris Flow , 2008 .

[15]  Alain Hauchecorne,et al.  Infrasound monitoring for atmospheric studies , 2009 .

[16]  He Manchao.  REAL-TIME REMOTE MONITORING AND FORECASTING SYSTEM FOR GEOLOGICAL DISASTERS OF LANDSLIDES AND ITS ENGINEERING APPLICATION , 2009 .

[17]  Nicolas Brachet,et al.  Monitoring the Earth’s Atmosphere with the Global IMS Infrasound Network , 2010 .

[18]  Douglas P. Drob,et al.  Inversion of Infrasound Signals for Passive Atmospheric Remote Sensing , 2010 .

[19]  Zhang Shucheng,et al.  Early Warning System to Debris Flow , 2010 .

[20]  Giorgio Lacanna,et al.  Observation of infrasonic and gravity waves at Soufrière Hills Volcano, Montserrat , 2010 .

[21]  M. Arattano,et al.  Analysis of debris-flow recordings in an instrumented basin , 2010 .

[22]  Yang Yichun,et al.  Observation & study of a kind of low-frequency atmospheric infrasonic waves , 2010 .

[23]  Maurizio Ripepe,et al.  Monitoring snow avalanches in Northwestern Italian Alps using an infrasound array , 2011 .

[24]  Wang Liang-wei.  Infrasound monitoring and early warning of debris flow along montanic railway line , 2012 .

[25]  Feng Haonan,et al.  Anomalous Infrasonic Waves Before a Small Earthquake in Beijing , 2012 .

[26]  Pang Xin-liang.  Analysis of the Characteristics of the Background Noise from a Nuclear Explosion Monitoring , 2013 .

[27]  Hsien-Ter Chou,et al.  Acoustic signals and geophone response of rainfall-induced debris flows , 2013 .

[28]  Fangqiang Wei,et al.  Monitoring and recognition of debris flow infrasonic signals , 2015, Journal of Mountain Science.

[29]  Fangqiang Wei,et al.  Visualized localization and tracking of debris flow movement based on infrasound monitoring , 2018, Landslides.

[30]  Kaiheng Hu,et al.  Model test: Infrasonic features of porous soil masses as applied to landslide monitoring , 2020 .