Autoregressive Modeling of Geomagnetic Data.(Dept.E)

Fatma Alzahraa Abou Chadi,M Abou El-Soud,F Ahmed

doi:10.21608/bfemu.2021.161245

Abstract

Geomagnetic (GM) data generally appear to have significant organization or structure. We attempted to determine if GM records could be modeled as an autoregressive process with a white noise excitation during the short-time period of one month. The autoregressive (ER) model is then used to synthesize GM signals using AR coefficients and a white noise excitation with the same propability distribution function as those determined from the autoregressive model of an ensemble of monthly records. Both the original and synthesized monthly records are then compared using the root-mean-square (rms) amplitude, the number of zero crossing per month. the number of peaks, and the amplitude distributions of the signals. The results of examining the synthesized GM records indicate that there are no significant differences in the values of these parameters. The use of such synthesized GM records may allow more through testing of forecasting algorithms than is possible with the present limited number of GM records.

Highlights

Studies of Eanhls magnetic field have continued at a vigorolls pace because the magnetic field is a fundamental parameter of great significance to many other applications such as mining exploration, archaeomagnetism. telecommuni cati ons, space research.... , etc. [1]E.30 Abcu-Chadt at a1.The time-varying component of the geomagnetic field at the earth's surface is composed of a primary field that is produced in the ionosphere and magnetosphere and a secondary field due to geomagnetic induction in the crust and upper mantel [2]
Later measurements at magnetic observatories showed many changes in the field that have shorter periods than these originally observed The variations may be resolved into secular changes, solar-diurnal changes, lunar-diurnal changes, and abrupt changes resulting from magnetic stonns
Isolation and quantification of three identifiable components contributing to the total geomagnetic field records using appropriate statistical and signal processing techniques was reported [3]

Summary

INTRODUCTION

Studies of Eanhls magnetic field have continued at a vigorolls pace because the magnetic field is a fundamental parameter of great significance to many other applications such as mining exploration, archaeomagnetism. telecommuni cati ons, space research.... , etc. [1]. We model geomagnetic signals as autoregressive (AR) processes during the time period of one month. After modeling the geomagnetic signals as an AR process, new synthetic geomagnetic (GM) records are generated by using the Infonnation derived from the distributions of the relevant AR parameters and the prediction error of the available records. Such synthetic records may be useful for a through investigation of the geomagnetic variability and for forecasting purposes which are not possible with limited nwnber of records. The resu1ts of comparing characteristics of the synthesized geomagnetic records to those of the true records are discussed

ELEM ENTS AND VARIABILITY OF THE GEOMAGNETIC FI ELD

DIGITAL SIMULATION OF GEOMAGNETIC RECORDS

Evaluation or Synthetic Signals

E.38 Abou-Chadl at ala