Characteristics of the Magnetotelluric response functions

Abstract

The interpretation of electrical resistivity and conductivity structures derived from magnetotelluric data is fundamentally influenced by galvanic distortion caused by small-scale near-surface heterogeneities. These distortions modify the observed electric fields without significantly affecting the magnetic fields, thereby biasing the impedance tensor and introducing ambiguity in subsurface imaging. A rigorous understanding of the intrinsic characteristics of MT response functions under such conditions is therefore essential for reliable interpretation. In this study, a synthetic three-dimensional Earth model incorporating localized near-surface conductivity anomalies is developed to investigate the fundamental behaviour of MT transfer functions. Through controlled forward modelling, broadband electromagnetic fields are simulated, and the full impedance tensor is evaluated over a wide period range. This framework enables a clear separation between inductive and galvanic effects, allowing systematic analysis of their influence on MT response characteristics. From a theoretical perspective, galvanic distortion is described as a frequency-independent, real-valued transformation of the impedance tensor that preserves key invariants, including phase relationships and dimensionality indicators. Particular attention is given to the behaviour of principal MT observables—apparent resistivity, phase, tipper response, and rotational invariants under varying structural complexity. The results demonstrate that although galvanic distortion can significantly alter apparent resistivity and induce tensor anisotropy, invariant quantities such as phase tensor parameters remain stable and provide robust indicators of subsurface structure. These findings highlight the importance of invariant-based interpretation and physically consistent modelling. This work establishes a comprehensive theoretical framework for understanding the characteristics of MT response functions in the presence of near-surface distortion, providing a foundation for improved interpretation strategies and the development of advanced correction methodologies, including both physics-based and data-driven approaches.