Strong-Earthquake-Prone Areas Recognition Based on an Algorithm with a Single Pure Training Class: I. Altai–Sayan–Baikal Region, М ≥ 6.0

Izvestiya, Physics of the Solid Earth - A new version of the Barrier algorithm is proposed for recognition of strong-earthquake prone regions based on training over a single reliable training...     

Recognition of disturbances with specified morphology in time series. Part 1: Spikes on magnetograms of the worldwide INTERMAGNET network

The International Real-time Magnetic Observatory Network (INTERMAGNET) is the world’s biggest international network of ground-based observatories, providing geomagnetic data almost in real time (within 72 hours of collection) [Kerridge, 2001]. The observation data are rapidly transferred by the observatories participating in the program to regional Geomagnetic Information Nodes (GINs), which carry out a global exchange of data and process the results. The observations of the main (core) magnetic field of the Earth and its study are one of the key problems of geophysics. The INTERMAGNET system is the basis of monitoring the state of the Earth’s magnetic field; therefore, the information provided by the system is required to be very reliable. Despite the rigid high-quality standard of the recording devices, they are subject to external effects that affect the quality of the records. Therefore, an objective and formalized recognition with the subsequent remedy of the anomalies (artifacts) that occur on the records is an important task. Expanding on the ideas of Agayan [Agayan et al., 2005] and Gvishiani [Gvishiani et al., 2008a; 2008b], this paper suggests a new algorithm of automatic recognition of anomalies with specified morphology, capable of identifying both physically- and anthropogenically-derived spikes on the magnetograms. The algorithm is constructed using fuzzy logic and, as such, is highly adaptive and universal. The developed algorithmic system formalizes the work of the expert-interpreter in terms of artificial intelligence. This ensures identical processing of large data arrays, almost unattainable manually. Besides the algorithm, the paper also reports on the application of the developed algorithmic system for identifying spikes at the INTERMAGNET observatories. The main achievement of the work is the creation of an algorithm permitting the almost unmanned extraction of spike-free (definitive) magnetograms from preliminary records. This automated system is developed for the first time with the application of fuzzy logic system for geomagnetic measurements. It is important to note that the recognition of time disturbances is formalized and identical. The algorithm presented here appreciably increases the reliability of spike-free INTERMAGNET magnetograms, thus increasing the objectivity of our knowledge of the Earth’s magnetic field. At the same time, the created system can accomplish identical, formalized, and retrospective analysis of large archives of digital and digitized magnetograms, accumulated in the system of Worldwide Data Centers. The relevant project has already been initiated as a collaborative initiative of the Worldwide Data Center at Geophysical Center (Russian Academy of Sciences) and the NOAA National Geophysical Data Center (Unite States). Thus, by improving and adding objectivity to both new and historical initial data, the developed algorithmic system may contribute appreciably to improving our understanding of the Earth’s magnetic field.     

A new approach to recognition of the strong earthquake-prone areas in the Caucasus

Clustering the epicenters of Caucasian earthquakes with magnitudes M ≥ 3.0 is carried out, and the epicentral zones of the probable earthquakes with M ≥ 5.0 areas where epicenters of earthquakes with M ≤ 5.0 may occur are recognized by the Fuzzy Clustering and Zoning (FCAZ) algorithmic system developed by the authors at the Geophysical Center of the Russian Academy of Sciences. These zones correspond well to the locations of the epicenters of earthquakes with M ≥ 5.0. The zones recognized in this study are compared with the zones previously recognized by A.D. Gvishiani et al. in 1988 by the Earthquake-Prone Areas Recognition (EPA) technique. The comparison shows that the zones identified by FCAZ are mainly located inside the EPA-zones. The FCAZ-zones are also compared with the zones previously recognized using gravimetric and geological data. The results obtained by different methods closely agree. Contrary to EPA technique FCAZ algorithmic system relies on the DPS algorithm of objective classification that requires only the information about epicenters of the earthquakes in the region under study.     

Fuzzy logic algorithms in the analysis of electrotelluric data with reference to monitoring of volcanic activity

The expert processing of monitoring data of large networks on hazardous natural phenomena becomes increasingly more complicated due to an increase in the initial data flow. An approach alternative to the visual recognition of signals is proposed. A number of recognition algorithms and results of their application to the analysis of geoelectric potential monitoring data are discussed. Data of monitoring La Fournaise Volcano (Réunion Island) obtained in the vicinity of the intense volcanic eruption of 1988 are used. The obtained results show that these algorithms are capable of recognizing anomalous segments of records and discriminating between several types of anomalies presumably associated with the effects of various physical factors (heavy atmospheric precipitation, hydrothermal processes, and so on). The algorithms proposed in this work can be used both for the automation of expert work in operating monitoring systems and in investigations aimed at the identification of typical morphologic sequences in time series of data of various origins.     

Recognition of strong earthquake–prone areas with a single learning class

This article presents a new Barrier recognition algorithm with learning, designed for recognition of earthquake-prone areas. In comparison to the Crust (Kora) algorithm, used by the classical EPA approach, the Barrier algorithm proceeds with learning just on one “pure” high-seismic class. The new algorithm operates in the space of absolute values of the geological–geophysical parameters of the objects. The algorithm is used for recognition of earthquake-prone areas with М ≥ 6.0 in the Caucasus region. Comparative analysis of the Crust and Barrier algorithms justifies their productive coherence.     

Algorithm for recognizing Pc3 geomagnetic pulsations in 1-s data from INTERMAGNET equatorial observatories

The methods are suggested for analyzing the data of three-component geomagnetic observations in order to automatically recognize time anomalies-pulsations in the geomagnetic field. These methods include preliminary bandpass filtering of the data, calculating the eigenvalues of the covariance matrix of magnetic components in a moving time window, computing the generalized variance of the eigenvalues (generalization is understood as raising to a power that is distinct from the traditional power of 2), averaging the variance, and identifying the time intervals marked by the presence of pulsations by the criterion of the averaged variance of eigenvalues to exceed a certain threshold specified by the fuzzy-logic methods.     

A Projection Method for Solving Systems of Linear Equations: Gravimetry Applications

Doklady Earth Sciences - Existing methods for solving inverse problems, such as the regularization method, look mostly for a quasi-solution that may not be a solution to the original problem, but...     

Regression Derivatives and Their Application to the Study of Geomagnetic Jerks

Geomagnetism and Aeronomy - New mathematical constructions are developed for the regression smoothing of discrete time series defined on an irregular grid. The new method is used to study secular...