Decomposition of Magnetotelluric Impedance into Amplitude and Phase Tensors

What is it about?

The Phase Tensor is a common tool to interpret Magnetotelluric data, because it is not affected by galvanic distortion. However, the Phase Tensor only constitutes half of the measured data and the second half is often discarded. In order to quantify and illustrate the commonly discarded information caused by the sole use of the phase tensor, we define a new matrix decomposition that yields phase and amplitude tensors from the MT data such that the amplitude tensor exactly represents the lost information. The phase tensor is systematically compared to the amplitude tensor on synthetic examples. We conclude that, although the phase tensor may be theoretically sufficient to infer about the subsurface at large, the amplitude tensor can help to identify conductivity or thickness of conductive anomalies more accurately than the phase tensor alone, especially if the MT data are sparsely sampled over period due to presence of noise or data gaps.

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Why is it important?

Heightened cost awareness, increasingly difficult and deeper energy and mineral resources, promote the Magnetotelluric (MT) method as an important geophysical exploration tool. MT excels in the detection and characterization of subsurface fluids and hydrothermal systems because of their strong effect on the subsurface's electric conductivity. However, interpretation of sparse MT data can be hindered by a physical phenomenon, the galvanic distortion of the electric field, that deflects and/or channels electric currents due to anomalies at very near surface that are not the intended target. In practice, this adverse effect can be circumvented by sacrificing half of the acquired MT data, discarding all amplitude information and interpreting solely phase tensor information. Nevertheless, the phase tensor definition involves complex algebra that prevented the user to quantify what has been sacrificed until now. Furthermore, it is noteworthy that the derived Amplitude-Phase decomposition is an entirely new algebraic matrix decomposition and therefore, may hold untapped potential for other disciplines that interpret complex matrices or second rank tensors.