Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response

Wavelet Model of Geomagnetic Field Variations and Its Application to Detect Short-Period Geomagnetic Anomalies

Geomagnetic data analysis is an important basis for the investigation of the processes in the near-Earth space, Earth magnetosphere, and ionosphere. The negative impact of geomagnetic anomalies on modern technical objects and human health determine the applied significance of the investigation and requires the creation of effective methods for timely detection of the anomalies. Priory complicated structure of geomagnetic data makes their formalization and analysis difficult. This paper proposes a wavelet model for geomagnetic field variations. It describes characteristic changes and anomalies of different amplitude and duration. Numerical realization of the model provides the possibility to apply it in online analysis. We describe the process of model identification and show its efficiency in the detection of sudden, short-period geomagnetic anomalies occurring before and during magnetic storms. Raw second data of the Paratunka and Magadan observatories and post-processed minute data were used in the paper. The question of noise effect on the proposed model results was under consideration.