Recognition of small magnitude seismic events type based on time-frequency features and machine learning
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摘要:
本研究聚焦于华北地区小震级（ML≤3.0）地震事件，利用K-近邻算法（KNN）、自适应提升算法（AdaBoost）和轻量级梯度提升机算法（LGBM）对天然地震、人工爆炸以及矿震塌陷事件进行类型识别，得到了较好的效果。对地震事件波形记录和时频谱进行分析，提取了时间、P/S幅值比、频率、过零率、峰值振幅、峰值地面加速度、能量、信号、角度及其它比值10个类别的62个特征，将这些特征作为分类的基础。采用三种分类算法分别对二分类任务和三分类任务进行模型训练，最后对测试数据的类型进行识别，所有分类模型的识别准确率均达90.0%以上，其中LGBM的综合性能最强，AdaBoost次之；不同分类任务中天然地震与矿震分类模型的表现最佳。
Abstract:The identification and classification of seismic events hold significant importance in seismic monitoring and earthquake disaster mitigation. This research primarily focuses on
1935 seismic event data with low magnitude （ML≤3.0） in the North China region, encompassing three distinct types of events: natural earthquakes, artificial explosions, and mining collapses. Preliminary analysis involved the geographical distribution examination, annual trends, and magnitude distribution of these events. Preprocessing of raw seismic data included amplitude normalization, detrending, mean removal, and band-pass filtering （0.5—20 Hz）. Additionally, short-time Fourier transform analysis was utilized to visualize waveform and spectrogram characteristics, facilitating the observation and analysis of both time and frequency domain features. Based on the analysis results, 62 features across 10 categories, including time, P/S amplitude ratio, frequency, zero-crossing rate, peak amplitude, peak ground acceleration, energy, signal characteristics, angle, and other ratios, were extracted as the foundation for classification.This research employed K-Nearest Neighbors （KNN）, Adaptive Boosting （AdaBoost）, and Light Gradient Boosting Machine （LGBM） algorithms to train models using the extracted 62 features for binary and ternary classification tasks of natural earthquakes, artificial explosions, and mining collapses. The basic principles of KNN, AdaBoost, and LGBM algorithms were initially introduced, followed by a description of the training process for the classification models. To ensure balanced sample distribution for each event type, data were selected based on uniform distribution of time and geographical location. Ultimately, 545 events for each event type, totaling
1635 seismic events, were chosen as the sample data for building the classification models. The dataset was divided into training and testing sets using a holdout method, with 75% of the data used for model construction and validation, and 25% for evaluating model performance. The training data covered the main geographical range of the North China region （109.3°—123.5°E, 34.1°—43.7°N）, ensuring the models could capture the region’s diversity and complexity. The testing data covered a slightly different geographical range （110.8°—124.1°E, 34.9°—42.7°N）.The 62 features were used to train classification models by KNN, AdaBoost, and LGBM algorithms. Models were trained with number 0 representing natural earthquakes, number 1 representing artificial explosions, and number 2 representing mining collapses. Various classification models were evaluated using KNN, AdaBoost, and LGBM, with each model trained and tested 100 times for 0−1, 0−2, 1−2, and 0−1−2 classification tasks. AdaBoost and LGBM demonstrated superior performance compared to KNN across all classification tasks, especially in 0−1 and 0−1−2 classification task. LGBM consistently exhibited the best overall performance, maintaining an accuracy of over 95% and showing high stability. In different classification tasks, 0−2 classification yielded the most outstanding results, followed by 1−2 classification.
Following the training of classification models, the focus shifted to comprehensive evaluation of these models using testing data. Each model was used to identify the event types in the testing data, yielding performance results for each model across different classification tasks. Confusion matrices were generated based on identification results, demonstrating excellent performance for each classification task, particularly in the 0−2 classification using three different classification algorithms.
Based on confusion matrices, performance evaluation metrics, including accuracy, precision, recall, and F1 score, were calculated. In the 0−1 classification task, AdaBoost performed the best, achieving an accuracy of 96.69%. In the 0−2 classification task, all three algorithms performed well, with metrics exceeding 99.26%. In the 1−2 and 0−1−2 classifications, LGBM exhibited the best performance. Overall, each classification model demonstrated excellent performance, with accuracy, precision, recall, and F1 score all exceeding 89.71%.
LGBM exhibited superior overall performance, maintaining an accuracy of over 95.90% and demonstrating high stability. KNN still has significant room for improvement, possibly due to its sensitivity to data, resulting in relatively weaker performance compared to AdaBoost and LGBM. AdaBoost’s overall performance lies between LGBM and KNN.
Finally, ROC curves were plotted to visualize the recognition of the testing dataset using three different classification algorithms （KNN, AdaBoost, LGBM）. While KNN algorithm performance for 0−1 and 1−2 classifications requires optimization, all other models performed exceptionally well in the ternary classification scenario. Confusion matrices and evaluation metrics indicate that the constructed classification models perform well on testing data, with ROC curve analysis further confirming the excellent performance of the classification models in various tasks and revealing the applicability of different algorithms in their respective tasks, providing strong support for the practical application of the models.
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图 1 华北地区训练数据（a）和测试数据（b）的地震事件分布图
Figure 1. Distribution map of train （a） and test （b） data of seismic events in North China
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图 2 2008—2016年华北地区地震事件年度分布
Figure 2. Annual distribution of seismic events of North China area during 2008−2016
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图 3 训练数据（a）和测试数据（b）地震事件震级分布统计
Figure 3. Magnitude distribution statistics of train （a） and test data （b） seismic events
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图 4 天然地震（a）、人工爆炸（b）、矿震塌陷（c）的波形记录和时频图
Figure 4. Waveform records and time-frequency spectrograms of natural earthquake （a），artificial explosion （b） and mining collapse （c），respectively
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图 5 特征分布图
（a） 阈值之上的平均下降频率特征分布；（b） 滤波波段为9.5—10.5 Hz的P/S振幅比特征分布；（c） 1—20 Hz频带的P/S振幅比特征分布
Figure 5. Distribution of features
（a） Distributions of features of the decay average frequency above the threshold；（b） The P/S amplitude ratio distribution at 9.5—10.5 Hz filter band;（c） The P/S amplitude ratio distribution at the frequency band from 1 to 20 Hz
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图 6 采用KNN （a），AdaBoost （b），LGBM （c）对分类模型训练100次的准确率
Figure 6. The accuracy of classification models trained 100 times using KNN （a）， AdaBoost （b）， and LGBM （c），respectively
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图 7 分类模型对测试数据的识别结果混淆矩阵
Figure 7. Confusion matrix of identification results of test data by classification model （a）—（d） KNN；（e）—（h） AdaBoost；（i）—（l） LGBM
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图 8 不同分类模型的ROC曲线
（a） 测试集0−1，0−2，1−2；（b） 测试集0−1−2
Figure 8. ROC curves of different classification models
（a） 0−1，0−2，1−2 classification models；（b） 0−1−2 classification model
表 1 所提取的特征表
Table 1 Table of extracted features
特征 物理意义 数量 时间 地震波形从起始点到达波峰所需的时间；从波峰到达结束点所需的时间；从起始点到结束点所经历的总时间；地震波形超过设定阈值的持续时间；地震波形在超过设定阈值后到达波峰所需的时间；地震波形在超过设定阈值后从波峰到结束点所需的时间；地震波形在超过设定阈值前波形的上升、下降时间；两个相邻的波峰或波谷之间的时间间隔，即两个相邻波峰或波谷之间的周期长度（Kim et al，2 021；薛思敏等，2 022）。 9 P/S幅值比 P波与S波峰值振幅之比（Yıldırım et al，2 011）；对P波、S波进行傅里叶变换，滤波波段为1—20 Hz时振幅之比（Wang et al，2 021）。 21 频率 地震信号中波形每秒振动的次数，为周期的倒数（Levshin et al，1995）；中心频率，地震信号在频率域中的中心位置；主频率，地震信号中振幅最大的频率；平均频率，地震信号频谱的加权平均频率；地震波形在上升或下降阶段的平均频率；波形上升、下降时，地震信号在超过设定阈值的情况下的平均频率；地震信号复倒频谱的实部（魏富胜，黎明，
2 003）。9 过零率 地震波形从正向值变为负向值，或从负向值变为正向值的次数；地震信号在超过设定阈值的情况下的过零率；峰值振幅前、后的过零率；地震信号在超过设定阈值的情况下的最大振幅前、后的过零率（Dargahi-Noubary，1998）。 6 峰值振幅 地震波形中振幅达到的最大值（Horasan et al，2009；Badawy et al，2019）。 1 峰值地面加速度 地震信号中垂直地面方向的最大加速度值（Goforth et al，2006）。 1 能量 地震信号总能量；峰值振幅前吸收能量、峰值振幅后衰减能量（刘莎等，2012）。 3 信号 地震信号强度；信号均方根（Laasri et al，2015；Saad et al，2019）。 2 其它比值 地震波形的上升时间、下降时间与峰值振幅之比（the ratio of rise time to amplitude，缩写为RA；the ratio of decay time to amplitude，缩写为DA）；阈值之上的上升、下降时间和振幅的比值；RA，DA与地震波形的平均频率之比（ratio of RA to average frequency，缩写为RA/AF；ratio of DA to average frequency，缩写为DA/AF）（吴顺川等，2020）。 6 角度 地震波形的上升、下降角度，为RA，DA的反正切函数（Ma et al，2015）；地震信号在超过设定阈值的情况下的上升、下降角度。 4 
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表 2 不同分类模型准确率平均值、最大值、最小值
Table 2 Average，maximum and minimum accuracy of different classification model
分类模型 0−1准确率 0−2准确率 1−2准确率 0−1−2准确率 平均值 最大值 最小值 平均值 最大值 最小值 平均值 最大值 最小值 平均值 最大值 最小值 KNN 89.66% 91.22% 87.80% 98.84% 99.61% 98.05% 94.73% 95.90% 93.07% 89.19% 90.68% 87.30% AdaBoost 96.99% 97.95% 94.63% 99.28% 99.80% 98.24% 98.12% 99.51% 97.07% 95.17% 96.35% 93.94% LGBM 97.03% 98.05% 95.90% 99.10% 99.71% 98.34% 97.95% 98.73% 96.98% 97.01% 97.98% 96.16%
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表 3 不同分类模型对不同分类任务的评价指标
Table 3 Evaluation metrics of different classification models for different classification task
分类模型 评价指标 0−1 0−2 1−2 0−1−2 KNN 准确率 90.81% 99.63% 94.49% 91.75% 精度 91.73% 100.00% 94.81% 93.15% 召回率 89.71% 99.26% 94.12% 93.03% F1分数 90.71% 99.63% 94.47% 93.03% AdaBoost 准确率 96.69% 99.63% 98.16% 95.10% 精度 94.41% 100.00% 98.52% 96.11% 召回率 99.26% 99.26% 97.79% 98.99% F1分数 96.75% 99.63% 98.15% 97.52% LGBM 准确率 96.32% 99.26% 98.53% 97.30% 精度 93.75% 99.26% 98.53% 97.31% 召回率 99.26% 99.26% 98.53% 97.30% F1分数 96.44% 99.26% 98.53% 97.30%
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