| 講演要旨(和文) | | MT法の時系列データに含まれる周波数成分の特徴を調べるため，経験的モード分解を用いたデータ処理を実施した．新しく開発したプログラムで，シミュレーションデータを使った数値実験を行なった結果，真の解に近い複数のサイン波とバイアス成分に分離することができた．また実測データの経験的モード分解を行なった結果，狭帯域の周波数成分を持つ9または10の定常的な時系列データに分離することができた．このように，フーリエ変換を実施する前に経験的モード分解を行なえば，時系列データ中に含まれる周波数成分が詳細にわかるので，時系列データの品質チェックができる．経験的モード分解は，ＭＴ法のデータ処理のための強力なツールになり得ると思われる． |
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| | 講演要旨(英文) | | Empirical mode decomposition (EMD) is a technique, which is a part of Hilbert-Huang transform (HHT), to transform original time-series data to narrow-band time-series data called intrinsic mode function (IMF). In order to examine characteristics of MT time-series data, EMD was applied to artificial and observed MT data. As the result of numerical analysis using simulation data, time-series data can be transformed to correct sine waves. In case of time-series data with rectangular noise, it was found that low-frequency IMF was strongly affected by rectangular noise. As the result of EMD using MT data which was observed in Ito Campus of Kyushu University, ten IMFs were detected in electric field and nine IMFs were detected in magnetic field. This result means that there are at least nine spectrum in this MT data. |
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