A. D. Gvishiani

Application of artificial intelligence for Euler solutions clustering

Results of Euler deconvolution strongly depend on the selection of viable solutions. Synthetic calculations using multiple causative sources show that Euler solutions cluster in the vicinity of causative bodies even when they do not group densely about the perimeter of the bodies. We have developed a clustering technique to serve as a tool for selecting appropriate solutions. The clustering technique uses a methodology based on artificial intelligence, and it was originally designed to classify large data sets. It is based on a geometrical approach to study object concentration in a finite metric space of any dimension. The method uses a formal definition of cluster and includes free parameters that search for clusters of given properties. Tests on synthetic and real data showed that the clustering technique successfully outlines causative bodies more accurately than other methods used to discriminate Euler solutions. In complex field cases, such as the magnetic field in the Gulf of Saint Malo region (Brittany, France), the method provides dense clusters, which more clearly outline possible causative sources. In particular, it allows one to trace offshore the main inland tectonic structures and to study their interrelationships in the Gulf of Saint Malo. The clusters provide solutions associated with particular bodies, or parts of bodies, allowing the analysis of different clusters of Euler solutions separately. This may allow computation of average parameters for individual causative bodies. Those measurements of the anomalous field that yield clusters also form dense clusters themselves. Application of this clustering technique thus outlines areas where the influence of different causative sources is more prominent. This allows one to focus on these areas for more detailed study, using different window sizes, structural indices, etc.

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.

Mathematical Tools for Geomagnetic Data Monitoring and the Intermagnet Russian Segment

In this paper, a new approach to the detection of anomalies in geophysical records is connected with a fuzzy mathematics application. The theory of discrete mathematical analysis and collection of algorithms for time series processing constructed on its basis represents the results of this research direction. These algorithms are the consequence of fuzzy modeling of the logic of an interpreter who visually recognizes anomalies in records. They allow analyzing large data sets that are not subjected to manual processing. The efficiency of these algorithms is demonstrated in several important geophysical applications. Plans for an extension of the Russian INTERMAGNET segment are presented.

Recognition of earthquake-prone areas: Methodology and analysis of the results

We present the results of verifying the areas that were detected as prone to strong earthquakes by the pattern recognition algorithms in different regions of the world with different levels of seismicity and, therefore, different threshold magnitudes demarcating the strong earthquakes. The analysis is based on the data presented in the catalog of the U.S. National Earthquake Information Center (NEIC) as of August 1, 2012. In each of the regions considered, we examined the locations of the epicenters of the strong earthquakes that occurred in the region after the publication of the corresponding result. There were 91 such earthquakes in total. The epicenters of 79 of these events (87%) fall in the recognized earthquake-prone areas, including 27 epicenters located in the areas where no strong earthquakes had ever been documented up to the time of publication of the result. Our analysis suggests that the results of the recognition of areas prone to strong earthquakes are reliable and that it is reasonable to use these results in the applications associated with the assessment of seismic risks. The comparison of the recognition for California with the analysis of seismicity of this region by the Discrete Perfect Sets (DPS) algorithm demonstrates the agreement between the results obtained by these two different methods.

Recognition of disturbances with specified morphology in time series: Part 2. Spikes on 1-s magnetograms

Preliminary magnetograms contain different types of temporal anthropogenic disturbances: spikes, baseline jumps, drifts, etc. These disturbances should be identified and filtered out during the preprocessing of the preliminary records for the definitive data. As of now, at the geomagnetic observatories, such filtering is carried out manually. Most of the disturbances in the records sampled every second are spikes, which are much more abundant than those on the magnetograms sampled every minute. Another important feature of the 1-s magnetograms is the presence of a plenty of specific disturbances caused by short-period geomagnetic pulsations, which must be retained in the definitive records. Thus, creating an instrument for formalized and unified recognition of spikes on the preliminary 1-s magnetograms would largely solve the problem of labor-consuming manual preprocessing of the magnetic records. In the context of this idea, in the present paper, we focus on recognition of the spikes on the 1-s magnetograms as a key point of the problem. We describe here the new algorithm of pattern recognition, SPs, which is capable of automatically identifying the spikes on the 1-s magnetograms with a low probability of missed events and false alarms. The algorithm was verified on the real magnetic data recorded at the French observatory located on Easter Island in the Pacific.

Survey of Geomagnetic Observations Made in the Northern Sector of Russia and New Methods for Analysing Them

An overview of the geomagnetic observations made in the northern part of Russia is presented from a historical perspective. Several stations were deployed on the territory of the former Soviet Union during the International Geophysical Year, 1957–1958, with the active participation and guidance of the Interagency Geophysical Committee which is inherited by the Geophysical Center of the Russian Academy of Sciences (GC RAS). In the 1990s, the majority of these stations, especially those in the remoter regions, were closed. Nowadays, the geomagnetic network, including the observatories of the INTERMAGNET program, has been restored. Examples of high-latitude geomagnetic variations in the Russian longitudinal sector are shown, and maps and trends of the secular variation over the territory of Russia presented. Particular attention is paid to the automated processing of data and to the analysis methods used. To process the growing amount of high-resolution geomagnetic data, sophisticated mathematical methods based on the fuzzy logic approach and new discrete mathematical analysis algorithms have been developed. The formal methods and algorithms for recognizing both artificial and natural disturbances in the magnetograms are described.

Automated recognition of spikes in 1 Hz data recorded at the Easter Island magnetic observatory

In the present paper we apply a recently developed pattern recognition algorithm SPs to the problem of automated detection of artificial disturbances in one-second magnetic observatory data. The SPs algorithm relies on the theory of discrete mathematical analysis, which has been developed by some of the authors for more than 10 years. It continues the authors’ research in the morphological analysis of time series using fuzzy logic techniques. We show that, after a learning phase, this algorithm is able to recognize artificial spikes uniformly with low probabilities of target miss and false alarm. In particular, a 94% spike recognition rate and a 6% false alarm rate were achieved as a result of the algorithm application to raw one-second data acquired at the Easter Island magnetic observatory. This capability is critical and opens the possibility to use the SPs algorithm in an operational environment.

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.

Structure and evolution of the Molucca Sea area: constraints based on interpretation of a combined sea-surface and satellite gravity dataset

The paper presents an interpretation of the complete Bouguer gravity anomaly for the Molucca Sea area (northeast of Indonesia) in order to investigate the structure and interrelation of the main tectonic units of the region. Data on the gravity field and topography incorporate all available shipboard and satellite-derived data, including data collected during a 1994 R/V L’Atalante cruise in the Molucca Sea (MODEC). These data were compiled by weighted interpolation of surface and satellite data. The anomalous gravity field of the area contains components of different wavelengths, which we separated into regional and local anomalies using a spherical analogue of Kolmogorov^Wiener optimal (mean-square) filtering. Position and depth of the shallow lithospheric gravity sources were then estimated from the local field component by applying a new approach to Euler solution selection based on a recently developed fuzzy logic clustering method, called RODIN. The spatial distribution and depth of Euler solutions provide new information on the tectonic structure of the upper lithosphere resulting from the convergence of the Philippine Sea, Eurasian and Australian plates. The local Bouguer anomalies and dense clusters of Euler solutions make it easy to trace the Sangihe Trench further north, up to 5.5‡N, joining it to the Pujada and Miangas ridges and to trace the Miangas Ridge southwards to its junction with the Central Ridge. Seismic data revealing compressive structure and dense shallow clusters of Euler solutions suggest that the Pujada Ridge overthrusts the Miangas Ridge from the west. Clusters of Euler solutions also clearly outline an ophiolite body of the Talaud Archipelago, show main thrust zones bounding it, and trace the southern termination of the Philippine Fault horsetail structure up to 5.5^6‡N in the area southeast of Mindanao Island. Our results support the hypothesis that the Talaud Archipelago was formed in situ as an uplifted Central Ridge block. We suggest that the structure of the Archipelago and of the area to the east

FCaZm intelligent recognition system for locating areas prone to strong earthquakes in the Andean and Caucasian mountain belts

The fuzzy clustering and zoning method (FCAZm) of systems analysis is suggested for recognizing the areas of the probable generation of the epicenters of significant, strong, and the strongest earthquakes. FCAZm is a modified version of the previous FCAZ algorithmic system, which is advanced by the creation of the blocks of artificial intelligence that develop the system-forming algorithms. FCAZm has been applied for recognizing areas where the epicenters of the strongest (M ≥ 73/4) earthquakes within the Andes mountain belt in the South America and significant earthquakes (M ≥ 5) in the Caucasus can emerge. The reliability of the obtained results was assessed by the seismic-history type control experiments. The recognized highly seismic zones were compared with the ones previously recognized by the EPA method and by the initial version of the FCAZ system. The modified FCAZm system enabled us to pass from simple pattern recognition in the problem of recognizing the locations of the probable emergence of strong earthquakes to systems analysis. In particular, using FCAZm we managed to uniquely recognize a subsystem of highly seismically active zones from the nonempty complement using the exact boundary.