Predicting microseismic sensitive feature data using variational mode decomposition and transformer

IF 1.6 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Xingli Zhang, Duanduan Hou, Qian Mao, Zhihui Wang
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引用次数: 0

Abstract

Rock burst is one of the major disasters that endanger coal safety production. If a rock burst occurs, it will cause terrible casualties and significant property losses. Therefore, this article proposes to predict the sensitive characteristics of microseisms, which can achieve the prediction and early warning of rock burst disasters to a certain extent. To effectively improve the prediction accuracy and robustness of microseismic sensitive feature data, a hybrid model called VMD-Transformer is suggested in this study for predicting time series of microseismic sensitive features. This model is based on the variational mode decomposition (VMD) and transformer model and aims to predict future eigenvalue from the historical data of sensitive features. To a certain extent, the transformer model is used to predict the future eigenvalue, while the VMD is used to extract the features of the time series data at various frequency domain scales, which solves the problem of non-stationary time series data being difficult to predict accurately due to high fluctuations. This study extracts sensitive features from microseismic events that the same source registered by a certain geophone after locating, decomposes the time series of the sensitive features using VMD, predicts each component of the decomposition separately using the transformer model, and then combines the component prediction results to produce the final prediction results. Experimental results indicate that our method has the features of a simple algorithm, strong adaptivity, and high prediction accuracy and can effectively predict time series of sensitive features extracted from microseismic signals.

Abstract Image

利用变异模式分解和变压器预测微震敏感特征数据
岩爆是危及煤炭安全生产的重大灾害之一。一旦发生岩爆,将造成可怕的人员伤亡和重大财产损失。因此,本文提出对微震敏感特征进行预测,可在一定程度上实现岩爆灾害的预测预警。为有效提高微震敏感特征数据的预测精度和鲁棒性,本研究提出了一种名为 VMD-Transformer 的混合模型,用于预测微震敏感特征的时间序列。该模型基于变模分解(VMD)和变换器模型,旨在从敏感地物的历史数据中预测未来特征值。在一定程度上,变分模型用于预测未来特征值,而 VMD 则用于提取不同频域尺度的时间序列数据特征,解决了非平稳时间序列数据因波动大而难以准确预测的问题。本研究从某一地震检波器定位后记录的同一震源的微震事件中提取敏感特征,利用 VMD 对敏感特征的时间序列进行分解,利用变压器模型对分解后的各分量分别进行预测,然后将各分量预测结果进行组合,得出最终预测结果。实验结果表明,我们的方法具有算法简单、适应性强、预测精度高等特点,能有效预测从微地震信号中提取的敏感特征时间序列。
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来源期刊
Journal of Seismology
Journal of Seismology 地学-地球化学与地球物理
CiteScore
3.30
自引率
6.20%
发文量
67
审稿时长
3 months
期刊介绍: Journal of Seismology is an international journal specialising in all observational and theoretical aspects related to earthquake occurrence. Research topics may cover: seismotectonics, seismicity, historical seismicity, seismic source physics, strong ground motion studies, seismic hazard or risk, engineering seismology, physics of fault systems, triggered and induced seismicity, mining seismology, volcano seismology, earthquake prediction, structural investigations ranging from local to regional and global studies with a particular focus on passive experiments.
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