Real-Time Discrimination Model for Local Earthquake Intensity Threshold Based on XGBoost
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摘要: 如何在地震中利用台站接收到的少量P波信息预测该台站处的最终烈度是否会超越6度是地震预警研究中亟待解决的关键问题. 提出了一种基于极限梯度提升树（XGBoost）的现地烈度阈值实时判别模型，该模型以由台站接收到P波后3秒内的信息计算的5种特征作为输入参数，以该台站处的最终仪器地震烈度是否会超越6度作为阈值. 选取1996—2022年日本K-NET台网记录的460次地震的4 353条加速度记录建立了基于P波前3秒信息的烈度阈值实时判别模型（XGBoost-ITD）. 结果表明，该模型对低烈度的判别准确率为93%，对高烈度的判别准确率为88%. 在相同数据集条件下，相较于支持向量机分类方法及传统方法，XGBoost方法对现地烈度阈值判别具有更高的精度.Abstract: A key challenge in earthquake early warning (EEW) research is to predict whether the final intensity at a station during an earthquake will exceed 6 degrees using only a small amount of P-wave information received by the station. In this paper, we propose a real-time intensity threshold discrimination model based on Extreme Gradient Boosting Tree (XGBoost). The model uses five features calculated from the information within 3 seconds after receiving P-waves as input features, and uses the threshold of whether the final instrumental seismic intensity at the station will exceed 6 degrees. A total of 4 353 acceleration records from 460 earthquakes recorded by the Japanese K-NET seismic network from 1996 to 2022 were used to establish the XGBoost-based real-time intensity threshold discrimination model (XGBoost-ITD). The results indicate that the model's discrimination accuracy rate is 93% for low intensity and 88% for high intensity. Compared with the support vector machine classification method and the traditional method under the same dataset, the XGBoost method shows higher discrimination accuracy.
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Key words:
- onsite warning /
- XGBoost /
- SHAP /
- machine learning /
- earthquake
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图 1 选取记录的台站分布及震中分布图
a. 训练集台站分布；b. 测试集台站分布；c. 训练集震中分布；d. 测试集震中分布
Fig. 1. Distribution of selected recorded stations and earthquake epicenters
图 2 烈度与震中距分布图
a. 训练集；b. 测试集
Fig. 2. Distribution of intensity versus epicentral distance
图 4 不同参数组合下模型的AUC变化图
Fig. 4. The AUC score of the model under different parameter combinations
图 6 模型的评价图
a. ROC曲线及AUC值；b. 不同烈度样本的判别准确率
Fig. 6. Discriminant effect of the proposed model
图 7 各种模型的判别效果
a. ROC曲线及AUC值；b. 不同烈度样本的判别准确率
Fig. 7. Discriminant effect of various models
图 8 基于SHAP值的特征全局重要性排序图
Fig. 8. Feature global importance ranking chart based on SHAP value
图 9 (a) 震例的台站及震中位置分布; (b) XGBoost-ITD模型判别结果; (c) 不同烈度震例样本的判别准确率
Fig. 9. (a) Example distribution of stations and epicenter locations, (b) The discrimination results of XG Boost-ITD model, (c) discrimination accuracy of samples with different intensity
表 1 特征参数定义简介
Table 1. Definition of feature parameters
参数类型 特征名称 计算式 公式编号 (a)幅值参数 峰值加速度$ Pa $ $ Pa=\underset{{t}_{\mathrm{o}} < t < {t}_{\mathrm{o}}+3}{\mathrm{m}\mathrm{a}\mathrm{x}}\left|a\right(t\left)\right| $ (4) 峰值速度$ Pv $ $ Pv=\underset{{t}_{\mathrm{o}} < t < {t}_{\mathrm{o}}+3}{\mathrm{m}\mathrm{a}\mathrm{x}}\left|v\right(t\left)\right| $ (5) 峰值位移$ Pd $ $ Pd=\underset{{t}_{\mathrm{o}} < t < {t}_{\mathrm{o}}+3}{\mathrm{m}\mathrm{a}\mathrm{x}}\left|d\right(t\left)\right| $ (6) (b)周期参数 最大卓越周期$ Tpd $
（详见Hildyard and Rietbrock, 2010）$ Tpd=\mathrm{m}\mathrm{a}\mathrm{x}\left(Tp{d}^{i}\right) $ (7) $ Tp{d}^{i}=2\mathrm{\pi }\sqrt{\frac{{X}_{i}}{{D}_{i}+{D}_{s}}} $ (8) (c)能量参数 累积绝对加速度$ CAV $ $ CAV={\int }_{{t}_{o}}^{{t}_{o}+3}\left|a\left(t\right)\right|\mathrm{d}t $ (9) Arias烈度$ Ia $ $ Ia=\frac{\mathrm{\pi }}{2g}{\int }_{{t}_{\mathrm{o}}}^{{t}_{\mathrm{o}}+3}{a}^{2}\left(t\right)\mathrm{d}t $ (10) 速度平方积分$ IV2 $ $ \mathrm{I}\mathrm{V}2={\int }_{{t}_{c}}^{{t}_{c}+3}{v}^{2}\left(t\right)\mathrm{d}t $ (11) (d)功率参数 破坏烈度$ DI $ $ DI=\mathrm{l}\mathrm{g}|a\cdot v| $ (12) (e)频谱参数 傅里叶谱幅值$ {A}_{\mathrm{m}\mathrm{a}\mathrm{x}} $ $ F\left(\omega \right)=\mathcal{F}\left[a\left(t\right)\right] $ (13) $ {A}_{\mathrm{m}\mathrm{a}\mathrm{x}}=\mathrm{m}\mathrm{a}\mathrm{x}\left|F\left(\omega \right)\right| $ (14) 
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表 2 模型的超参数调优细节
Table 2. Model hyperparameter optimization details
参数名称 说明 超参数取值范围 搜索步长 n-estimators 决策树的数量 [1, 300] 1 max-depth 树的最大深度 [1, 10] 1 min-child-weight 每个节点的最小权重和 [1, 10] 1 learning-rate 学习率 [0.01, 0.3] 0.05
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表 3 混淆矩阵
Table 3. Confusion matrix
混淆矩阵 预测(负例)地震烈度$ < 6 $ 预测(正例)地震烈度$ \ge 6 $ 真实(负例)地震烈度$ < 6 $ $ {T}_{\mathrm{N}} $ $ {F}_{\mathrm{P}} $ 真实(正例)地震烈度$ \ge 6 $ $ {F}_{\mathrm{N}} $ $ {T}_{\mathrm{P}} $
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表 4 模型评价指标的定义
Table 4. Definition of model evaluation index
评价指标 计算式 编号 精确率
(Precision)$ \mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}=\frac{{T}_{\mathrm{P}}}{{T}_{\mathrm{P}}+{F}_{\mathrm{P}}} $ (18) 召回率/真正率
(Recall/TPR)$ \mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}=\mathrm{T}\mathrm{P}\mathrm{R}=\frac{{T}_{\mathrm{P}}}{{T}_{\mathrm{P}}+{F}_{\mathrm{N}}} $ (19) F1得分
(F1score)$ \mathrm{F}1\mathrm{s}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{e}=\frac{2\times \mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\times \mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}}{\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}+\mathrm{R}\mathrm{e}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{l}} $ (20) 真负率(TNR) $ \mathrm{T}\mathrm{N}\mathrm{R}=\frac{{T}_{\mathrm{N}}}{{T}_{\mathrm{N}}+{F}_{\mathrm{P}}} $ (21) 假正率(FPR) $ \mathrm{F}\mathrm{P}\mathrm{R}=\frac{{F}_{\mathrm{P}}}{{T}_{\mathrm{N}}+{F}_{\mathrm{P}}} $ (22)
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表 5 模型超参数取值表
Table 5. Table for model hyperparameters values
超参数名称 超参数取值 n-estimators 64 max-depth 3 min-child-weight 1 learning-rate 0.21
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表 6 XGBoost-ITD模型评价指标表
Table 6. Evaluation result table of XGBoost-ITD model
数据集类型 精确率
(Precision)召回率/真正率
(Recall/TPR)F1得分
(F1score)真负率
(TNR)假正率
(FPR)训练集 0.836 1 0.913 8 0.873 2 0.884 2 0.115 8 测试集 0.902 8 0.877 0 0.889 7 0.934 3 0.065 7
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表 7 各模型预测结果对比表
Table 7. Comparison of the prediction results of each model
模型类型 精确率
(Precision)召回率/真正率
(Recall/TPR)F1得分
(F1score)真负率
(TNR)假正率
(FPR)Pd 0.548 0 0.946 5 0.694 1 0.457 2 0.542 8 SVM-linear 0.842 3 0.752 2 0.794 7 0.902 1 0.097 9 SVM-rbf 0.790 6 0.511 6 0.621 2 0.905 8 0.094 2 SVM-poly-2 0.963 3 0.187 2 0.313 4 0.995 0 0.005 0 SVM-poly-3 0.941 6 0.229 9 0.369 6 0.990 1 0.009 9 SVM-sigmoid 0.378 6 0.233 5 0.288 9 0.733 6 0.266 4 XGBoost-ITD 0.902 8 0.877 0 0.889 7 0.934 3 0.065 7
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