Strongest Earthquake-Prone Areas in Kamchatka

The paper continues the series of our works on recognizing the areas prone to the strongest, strong, and significant earthquakes with the use of the Formalized Clustering And Zoning (FCAZ) intellectual clustering system. We recognized the zones prone to the probable emergence of epicenters of the strongest (M ≥ 74/3) earthquakes on the Pacific Coast of Kamchatka. The FCAZ-zones are compared to the zones that were recognized in 1984 by the classical recognition method for Earthquake-Prone Areas (EPA) by transferring the criteria of high seismicity from the Andes mountain belt to the territory of Kamchatka. The FCAZ recognition was carried out with two-dimensional and three-dimensional objects of recognition.     

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.     

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.