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Abstract
Borehole strain monitoring has high sensitivity and is therefore widely used to study slow earthquakes, volcanic activity, earthquake precursors, and other nature phenomena. However, environmental factors seriously affect the identification of strain changes caused by crustal deformation. This paper proposes a method of anomaly detection based on variational mode decomposition (VMD) and principal component analysis (PCA). The borehole strain signal is decomposed into a number of modes simultaneously using VMD, and a new state-space model used to determine the number of the modes those are decomposed by the VMD algorithm. The influencing factors of each component are determined by spectrum analysis and comparative analysis. An example of the separation process of borehole strain data by the VMD method is presented. Then, we use PCA to calculate eigenvalues, which are used to detect anomalies associated with an earthquake, and eigenvectors, which are applied to show the spatial distribution characteristics of the data. Our method has been applied to detect borehole strain data anomalies associated with the Wenchuan earthquake; the VMD demonstrates excellent separation performance for borehole strain signals, and eigenvalues and eigenvectors together reflect the accelerated deformation of focal faults and adjacent areas before earthquake in time and space.
Highlights
Multi-component borehole strain monitoring has the advantages of high resolution, high sensitivity and long term stability [1], [2]
A method of anomaly detection based on variational mode decomposition (VMD) and principal component analysis (PCA) is proposed
Kong et al [6] detected stress changes and the anomalies in the outgoing long wave radiation(OLR)data before the Wenchuan earthquake by using CD method, and indicated that there are large CD values three months before the Wenchuan earthquake, and this is consistent with our research
Summary
Multi-component borehole strain monitoring has the advantages of high resolution, high sensitivity and long term stability [1], [2]. The VMD method is applied to adaptively decompose borehole strain signal completely from the data itself. Hsu et al [3] applied a state-space model to remove the strain response to rainfall, in addition to those due to air pressure changes and Earth tides, and investigated whether corrected strain changes are related to environmental disturbances or tectonic-original motions. Where Sn0 is the raw borehole strain data, Tn is the trend term for changes in strain data; Snc is short-period anomalous changes caused by crustal deformation; En, Pn, and Ln are induced strain by borehole pressure changes, the Earth tides changes, and borehole water-lever changes, respectively; εn is Gaussian white noise; and N is the number of observations. Short-period changes caused by crustal deformation components of S13, S24, and Sa are used to extract anomalies by PCA
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