基于GA-XGBoost的天然地震与爆破的小样本分类  

Small sample classification of natural earthquakes and blasting based on GA-XGBoost

作　　者：李鸿儒 李夕海 牛超 张云 刘继昊 谭笑枫 Li Hongru;Li Xihai;Niu Chao;Zhang Yun;Liu Jihao;Tan Xiaofeng(School of Nuclear Engineering,Rocket Force University of Engineering,Xi’an 710025,China)

机构地区：[1]火箭军工程大学核工程学院,西安710025

出　　处：《地震学报》2025年第2期221-231,共11页Acta Seismologica Sinica

基　　金：陕西省自然科学基础研究计划项目(2023-JC-YB-221,2023-JC-YB-244)资助。

摘　　要：由于爆破数据数量有限,利用分类法识别天然地震与爆破会遇到诸多困难。鉴于此,本文建立了高维特征小样本数据集,基于XGBoost模型利用遗传算法(GA)实现对主要影响XGBoost模型分类准确率的迭代次数、最大树深和学习率等三个重要超参数的自主寻优,构建出了GA-XGBoost模型,并将该模型应用于功率谱特征样本集,结果显示:爆破与天然地震的分类准确率高达94.094%;相比于传统的GS-XGBoost模型(准确率91.787%),GA-XGBoost模型在显著提升分类准确率的同时,其运行时间也由409.26 s缩短至55.48 s,效率提高了超86%。由此可见,本文建立的GA-XGBoost模型兼顾准确率、稳定性与效率,在小样本分类任务中具有良好的应用前景。The rapid development of seismic networks and the advancement of monitoring equipment have enabled the recording of various seismic events,including natural earthquakes and man-made blasting activities.Notably,nuclear explosions can also be detected through seismic monitoring,and this detection is a crucial aspect in the verification process of the Comprehensive Nuclear Test Ban Treaty.However,distinguishing between natural seismic events and those caused by blasting is challenging.Both appear as fluctuating curves on seismic records and share a striking resemblance,making manual identification resource-intensive and prone to human error,potentially leading to misjudgments and confusion in earthquake catalogs.This issue can compromise the effectiveness of earthquake early warning systems and emergency response measures.Therefore,the automated classification and discrimination between seismic events originated from natural sources and those caused by blasting are of great significance for both earth science research and national defense.Currently automatic classification techniques predominantly rely on deep learning,which typically requires extensive labeled datasets for training.Obtaining sufficient high-quality data for nuclear explosion events can be challenging due to their unique nature,limiting the application of deep learning for this purpose.This paper focuses on the classification and discrimination of natural earthquakes and blasting with limited sample data.The test data consists of vertical component recordings of short-period natural earthquakes and nuclear explosions.These recordings are preprocessed by employing the SPA method to eliminate the trend component.Subsequently,complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)is utilized to extract a series of intrinsic mode functions.Wavelet thresholding is applied to reduce noise,and then the denoised components are reconstructed to generate the final signals.The preprocessed signals are expanded by translation and noise injec

关 键 词：地震事件 XGBoost模型 遗传算法 小样本 特征提取 

分 类 号：P315.3[天文地球—地震学]