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Register a new userIdentification of earthquake precursors in the hydrogeochemical and geoacoustic data for the Kamchatka peninsula by flicker-noise spectroscopy
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A phenomenological systems approach for identifying potential precursors in multiple signals of different types for the same local seismically active region is proposed based on the assumption that a large earthquake may be preceded by a system reconfiguration (preparation) at different time and space scales. A nonstationarity factor introduced within the framework of flicker-noise spectroscopy, a statistical physics approach to the analysis of time series, is used as the dimensionless criterion for detecting qualitative (precursory) changes within relatively short time intervals in arbitrary signals. Nonstationarity factors for chlorine-ion concentration variations in the underground water of two boreholes on the Kamchatka peninsula and geacoustic emissions in a deep borehole within the same seismic zone are studied together in the time frame around a large earthquake on October 8, 2001. It is shown that nonstationarity factor spikes (potential precursors) take place in the interval from 70 to 50 days before the earthquake for the hydrogeochemical data and at 29 and 6 days in advance for the geoacoustic data.
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We propose a new type of earthquake precursor based on the analysis of correlation dynamics between geophysical signals of different nature. The precursor is found using a two-parameter cross-correlation function introduced within the framework of flicker-noise spectroscopy, a general statistical physics approach to the analysis of time series. We consider an example of cross-correlation analysis for water salinity time series, an integral characteristic of the chemical composition of groundwater, and geoacoustic emissions recorded at the G-1 borehole on the Kamchatka peninsula in the time frame from 2001 to 2003, which is characterized by a sequence of three groups of significant seismic events. We found that cross-correlation precursors took place 27, 31, and 35 days ahead of the strongest earthquakes for each group of seismic events, respectively. At the same time, precursory anomalies in the signals themselves were observed only in the geoacoustic emissions for one group of earthquakes.
This review presents the fundamentals of Flicker-Noise Spectroscopy (FNS), a general phenomenological methodology in which the dynamics and structure of complex systems, characterized by nonlinear interactions, dissipation, and inertia, are analyzed by extracting information from various signals with stochastically varying components generated by the systems. The basic idea of FNS is to treat the correlation links present in sequences of different irregularities, such as spikes, jumps, and discontinuities in derivatives of different orders, on all levels of the spatiotemporal hierarchy of the system under study as main information carriers. The tools to extract and analyze the information are power spectra and difference moments (structural functions) of various orders. Presently, FNS can be applied to three types of problems: (1) determination of parameters or patterns that characterize the dynamics or structural features of complex systems; (2) finding precursors of abrupt changes in the state of various complex systems based on a priori information about the dynamics of the systems; and (3) determination of flow dynamics in distributed systems based on the analysis of dynamic correlations in stochastic signals that are simultaneously measured at different points in space. Examples of FNS applications to such problems as parameterization of the images produced with atomic force microscopy (AFM), determination of precursors for electric breakdowns and major earthquakes, and analysis of electric potential fluctuations in electromembrane systems, as well as to some other problems in electrochemistry and medicine are discussed.
We use flicker-noise spectroscopy (FNS), a phenomenological method for the analysis of time and spatial series operating on structure functions and power spectrum estimates, to identify and study harmful chatter vibrations in a regenerative turning process. The 3D cutting force components experimentally measured during stainless steel turning are analyzed, and the parameters of their stochastic dynamics are estimated. Our analysis shows that the system initially exhibiting regular vibrations associated with spindle rotation becomes unstable to high-frequency noisy oscillations (chatter) at larger cutting depths. We suggest that the chatter may be attributed to frictional stick-and-slip interactions between the contact surfaces of cutting tool and workpiece. We compare our findings with previously reported results obtained by statistical, recurrence, multifractal, and wavelet methods. We discuss the potential of FNS in monitoring the turning process in manufacturing practice.
The problem of information extraction from discrete stochastic time series, produced with some finite sampling frequency, using flicker-noise spectroscopy, a general framework for information extraction based on the analysis of the correlation links between signal irregularities and formulated for continuous signals, is discussed. It is shown that the mathematical notions of Dirac and Heaviside functions used in the analysis of continuous signals may be interpreted as high-frequency and low-frequency stochastic components, respectively, in the case of discrete series. The analysis of electroencephalogram measurements for a teenager with schizophrenic symptoms at two different sampling frequencies demonstrates that the power spectrum and difference moment contain different information in the case of discrete signals, which was formally proven for continuous signals. The sampling interval itself is suggested as an additional parameter that should be included in general parameterization procedures for real signals.
An increasing demand of environmental radioactivity monitoring comes both from the scientific community and from the society. This requires accurate, reliable and fast response preferably from portable radiation detectors. Thanks to recent improvements in the technology, $gamma$-spectroscopy with sodium iodide scintillators has been proved to be an excellent tool for in-situ measurements for the identification and quantitative determination of $gamma$-ray emitting radioisotopes, reducing time and costs. Both for geological and civil purposes not only $^{40}$K, $^{238}$U, and $^{232}$Th have to be measured, but there is also a growing interest to determine the abundances of anthropic elements, like $^{137}$Cs and $^{131}$I, which are used to monitor the effect of nuclear accidents or other human activities. The Full Spectrum Analysis (FSA) approach has been chosen to analyze the $gamma$-spectra. The Non Negative Least Square (NNLS) and the energy calibration adjustment have been implemented in this method for the first time in order to correct the intrinsic problem related with the $chi ^2$ minimization which could lead to artifacts and non physical results in the analysis. A new calibration procedure has been developed for the FSA method by using in situ $gamma$-spectra instead of calibration pad spectra. Finally, the new method has been validated by acquiring $gamma$-spectra with a 10.16 cm x 10.16 cm sodium iodide detector in 80 different sites in the Ombrone basin, in Tuscany. The results from the FSA method have been compared with the laboratory measurements by using HPGe detectors on soil samples collected in the different sites, showing a satisfactory agreement between them. In particular, the $^{137}$Cs isotopes has been implemented in the analysis since it has been found not negligible during the in-situ measurements.
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