Novel Approach In Static Shift Correction For Magnetotellurics Data Using 2d Electrical Resistivity Imaging

A Rahman, Muhammad Nazrin (2022) Novel Approach In Static Shift Correction For Magnetotellurics Data Using 2d Electrical Resistivity Imaging. PhD thesis, Universiti Sains Malaysia.

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Magnetotellurics (MT) method utilizes lightning activities and solar wind interactions as electromagnetic sources in measuring resistivity distribution to great depths. Static shift is a common problem in MT where actual MT data is shifted higher or lower in logarithmic scale but maintains the same apparent resistivity (ρA) curve. It arises from voltage distortion, current distortion and topography effect. Currently, MT static shift is corrected using vertical electrical sounding (VES) and transient electromagnetic (TEM) data, which give ρA data in 1D that does not depicts subsurface materials precisely. In contrast, 2D electrical resistivity imaging (2D ERI) provides data in lateral and vertical directions; therefore, better images the ground with higher sensitivity to inhomogeneity. Additionally, true resistivity (ρT) obtained after inversion provides a better representation of the Earth than ρA. Therefore, this study develops a new static shift correction using 2D ERI ρT data. The study was carried out in 2020 where nine MT stations were conducted in Lahad Datu, Sabah. 2D ERI lines were conducted across MT stations to obtain the best resistivity model and extract ρA and ρT values beneath MT stations as two data groups for static shift correction. Resistivity data from 2D ERI was employed to constrained MT phase inversion to obtain the actual MT response for every MT station. Resistivity curve from MT survey was then shifted to the actual response to achieve its true resistivity level. The results obtained from ρA and ρT shifts yielded RMS values of <11.3 and <8.1 respectively.

Item Type: Thesis (PhD)
Subjects: Q Science > QC Physics > QC1 Physics (General)
Divisions: Pusat Pengajian Sains Fizik (School of Physics) > Thesis
Depositing User: Mr Noor Azizan Abu Hashim
Date Deposited: 23 Jan 2024 02:07
Last Modified: 23 Jan 2024 02:07

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