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

机译:土壤滑坡射频信号的特征分析和智能识别方法

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摘要

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.
机译:在灾难性的故障过程中,滑坡质量发射低频速率波浪,其特征在于强大的渗透功率,低能量衰减和长传播距离,为滑坡基础信号的远程被动监测提供了基础。然而,目前的滑坡基础逆势监测技术受环境干扰噪声的影响,并且经常产生误报。为了提高滑坡的准确性,需要分析监测信号以确定它是否是滑坡基础驱动信号。为此,本研究通过实验室滑坡仿真试验在不同的整合程度下收集了不同土壤类型的不同土壤类型的失效过程中产生的许多基础信号。此外,山区各种类型的环境干扰基础信号通过现场观察收集。将这些信号随机划分为训练集和测试组,根据3:2的比率。通过训练集数据的特征分析,综述了滑坡基础驱动器的典型特征和频率域中的环境干扰突出。通过构造特征向量集和正则化过程,以及使用诸如K-CORMATION邻居(KNN)分类算法,PYTHON,MATLAB和数据库的技术装置,开发了智能滑坡基础信号识别系统。使用测试集数据验证识别系统的性能。验证结果表明,该系统具有高识别精度和计算效率,可以满足滑坡基础监测的准确性和实时要求。此外,系统的识别结果可以提供准确的信号源和可靠的信息支持,对滑坡基础暴力预警。

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