Comparison of Different Coupling Methods for Joint Inversion of Geophysical Data: A Case Study for the Namibian Continental Margin. Issue 12 (17th December 2021)

• Record Type: Journal Article
• Authors: Franz, G.; Moorkamp, M.; Jegen, M.; Berndt, C.; Rabbel, W.
• Is Part Of: Journal of geophysical research. Volume 126:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-17
• Subjects: passive margins -- joint inversion -- Walvis Ridge -- marine magnetotellurics -- cross‐gradient coupling -- South Atlantic; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2021JB022092
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: Integration of multiple geophysical data is a key practice to reduce model uncertainties and enhance geological interpretations. Electrical resistivity models resulting from inversion of marine magnetotelluric (MT) data, often lack depth resolution of lithological boundaries and distinct information for shallow model parts. This is due to the diffusive nature of electromagnetic fields, enhanced by deficient data sampling and model regularization during inversion. Thus, integrating data or models to constrain layer thicknesses or structural boundaries is an effective approach to derive better constrained and more detailed resistivity models. We investigate the different impacts of three cross‐gradient coupled constraints on 3D MT inversion of data from the Namibian passive continental margin. The three constraints are (a) coupling with a fixed structural density model; (b) coupling with satellite gravity data; (c) coupling with a fixed gradient velocity model. Here, we show that coupling with a fixed model (a and c) improves the resistivity model the most. Shallow conductors imaging sediment cover are confined to a thinner layer in the resulting resistivity models compared to the MT‐only model. Additionally, these constraints help to suppress vertical smearing of a conductive anomaly attributed to a fracture zone, and clearly show that the seismically imaged Moho is not accompanied by a change in electrical resistivity. All of these observations help to derive an Earth model, which will form the basis for future interpretation of the processes that lead to continental break‐up during the early Cretaceous. Plain Language Summary: Jointly evaluating different geophysical measurements may improve geological interpretation. Methods measuring electromagnetic properties of the earth often have issues to resolve geological boundaries and may not provide sufficient information about the shallow subsurface. We therefore investigate three different ways to improve the interpretation of measurements conducted at the Namibian coast by integrating electromagnetic with additional information from gravity and acoustic measurements. Within this processes, we enhance the identification of geological boundaries that are compatible with both electromagnetic and gravity or alternatively with electromagnetic and acoustic observations. In our study, we were able to improve interpretation most by the combination of electromagnetic measurements with a fixed model, that is, definition of subsurface geological structures derived from gravity and acoustic measurements. The combination allows the identification of deceptive structures arising from the interpretation of electromagnetic measurements, only. Additionally, the sharpness of geological boundaries was improved by the suppression of excessive blurring. All of these findings may help to understand the mechanisms leading to the formation of the South Atlantic Ocean circa 130 million years ago. Key Points: Imaging of rift related volcanic processes at the Namibian margin through joint analysis of magnetotelluric, gravity, and seismic data Comparing the impact of three different inversion constraints on 3D inversion of marine magnetotelluric data 3D inversion of marine magnetotelluric data is most improved by cross‐gradient coupling with fixed structural density model