TWO-DIMENSIONAL INVERSION MODELING OF MAGNETOTELLURIC (MT) SYNTHETIC DATA OF A GRABEN STRUCTURE USING SimPEG

IF 0.1 Q4 GEOSCIENCES, MULTIDISCIPLINARY Riset Geologi dan Pertambangan Pub Date : 2021-06-30 DOI:10.14203/risetgeotam2021.v31.1121
I. Muttaqien, J. Nurjaman
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引用次数: 1

Abstract

The magnetotelluric (MT) method is a passive exploration method in geophysics that utilizes natural electromagnetic waves as a signal source. MT operates in the frequency range of 10 -5 - 10 6 Hz , designed to study the structure of the conductivity below the earth's surface with a depth range from several tens of meters to the upper mantle. In this paper, 2-dimensional inversion modeling is performed on MT synthetic data using the SimPEG software. First, forward modeling is done by making a 2-dimensional conductivity model in the form of a valley (graben), which aims to produce MT synthetic data in TE (transverse electric) mode, TM (transverse magnetic) mode, and a combination of TE mode and TM mode. Next, an inversion modeling is performed on the MT synthetic data by adding a 5% Gaussian noise and a  10 -5 floor as data uncertainty to obtain a 2-dimensional conductivity inversion model. The final result can be validated by comparing the true model and the inversion model and between observational data (synthetic) and predictive predicted data. The results of this study provide a significant fit of the model and suitability of the data. The inversion quality is validated with an RMS Error for TE mode of 0.349%, TM mode of 0.348%, and a combination of TE and TM mode of 0.249%.
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利用SimPEG对地堑构造大地电磁合成资料进行二维反演
大地电磁法是地球物理中一种利用自然电磁波作为信号源的被动勘探方法。MT的工作频率范围为10 -5 - 10 6 Hz,用于研究地表以下几十米至上地幔的电导率结构。本文利用SimPEG软件对MT合成数据进行了二维反演建模。首先,进行正演建模,制作谷(地堑)形式的二维电导率模型,生成TE(横电)模式、TM(横磁)模式以及TE模式与TM模式相结合的MT合成数据。接下来,对MT合成数据进行反演建模,加入5%高斯噪声和10 -5层作为数据不确定性,得到二维电导率反演模型。将真实模型与反演模型进行比较,将观测资料(合成)与预测预报资料进行比较,验证最终结果。本研究的结果提供了一个显著的拟合模型和适用性的数据。结果表明,TE模式的RMS误差为0.349%,TM模式的RMS误差为0.348%,TE和TM联合模式的RMS误差为0.249%。
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Riset Geologi dan Pertambangan
Riset Geologi dan Pertambangan GEOSCIENCES, MULTIDISCIPLINARY-
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