Application of artificial intelligence for Euler solutions clustering

International audienceResults 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

Artificial intelligence and dynamic systems for geophysical applications

The book presents new clustering schemes, dynamical systems and pattern recognition algorithms in geophysical, geodynamical and natural hazard applications. The original mathematical technique is based on both classical and fuzzy sets models. Geophysical and natural hazard applications are mostly original. However, the artificial intelligence technique described in the book can be applied far beyond the limits of Earth science applications. The book is intended for research scientists, tutors, graduate students, scientists in geophysics and engineer

A Method for Recognition of Sudden Commencements of Geomagnetic Storms Using Digital Differentiating Filters

This article describes a method for recognizing sudden commencement events using digital differentiating filters. This method is applied to INTERMAGNET observatory data. Maximum amplitude derivatives for the magnetic components (X, Y, Z) and the total intensity (F) of the geomagnetic field are introduced, and the decision-making rule is formulated. The authors developed a procedure for selecting optimal digital differentiating filters. Estimates of probabilities of correct and false recognition of sudden commencements were obtained. The calculations of the probabilistic characteristics have confirmed the effectiveness of the method

Automated Hardware and Software System for Monitoring the Earth’s Magnetic Environment

The continuous growth of geophysical observations requires adequate methods for their processing and analysis. This becomes one of the most important and widely discussed issues in the data science community. The system analysis methods and data mining techniques are able to sustain the solution of this problem. This paper presents an innovative holistic hardware/software system (HSS) developed for efficient management and intellectual analysis of geomagnetic data, registered by Russian geomagnetic observatories and international satellites. Geomagnetic observatories that comprise the International Real-time Magnetic Observatory Network (INTERMAGNET) produce preliminary (raw) and definitive (corrected) geomagnetic data of the highest quality. The designed system automates and accelerates routine production of definitive data from the preliminary magnetograms, obtained by Russian observatories, due to implemented algorithms that involve artificial intelligence elements. The HSS is the first system that provides sophisticated automatic detection and multi-criteria classification of extreme geomagnetic conditions, which may be hazardous for technological infrastructure and economic activity in Russia. It enables the online access to digital geomagnetic data, its processing results and modelling calculations along with their visualization on conventional and spherical screens. The concept of the presented system agrees with the accepted ‘four Vs’ paradigm of Big Data. The HSS can increase significantly the ‘velocity’ and ‘veracity’ features of the INTERMAGNET system. It also provides fusion of large sets of ground-based and satellite geomagnetic data, thus facilitating the ‘volume’ and ‘variety’ of handled data

Structure and Density of Sedimentary Basins in the Southern Part of the East-European Platform and Surrounding Area

Modern satellite gravity missions and ground gravimetry provide operational data models that can be used in various studies in geology, tectonics, and climatology, etc. In the present study, sedimentary basins in the southern part of the East European Platform and adjoining areas including the Caucasus are studied by employing the approach based on decompensative gravity anomalies. The new model of sediments, implying their thickness and density, demonstrates several important features of the sedimentary cover, which were not or differently imaged by previous studies. We found a significant redistribution of the low-dense sediments in the Black Sea. Another principal feature is the increased thickness of relatively low-dense sediments in the Eastern Greater Caucasus. The deepest part of the South Caspian basin is shifted to the north, close to the Apsheron Trough. In its present position, it is almost joined with the Terek–Caspian depression, which depth is also increased. The thickness of sediments is significantly decreased in the eastern Pre-Caspian basin. Therefore, the new sedimentary cover model gives a more detailed description of its thickness and density, reveals new features and helps in better understanding of the evolution of the basins, providing a background for further detailed studies of the region