4.3 Article

Joint inversion of receiver functions, surface wave dispersion, and magnetotelluric data

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AMER GEOPHYSICAL UNION
DOI: 10.1029/2009JB006369

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资金

  1. HPC [RII3-CT-2003-506079]
  2. European Community
  3. Natural Environment Research Council (NERC) [NE/G000859/1]
  4. NERC [NE/G000859/1] Funding Source: UKRI
  5. Natural Environment Research Council [NE/G000859/1] Funding Source: researchfish

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We present joint inversion of magnetotelluric, receiver function, and Raleigh wave dispersion data for a one-dimensional Earth using a multiobjective genetic algorithm (GA). The chosen GA produces not only a family of models that fit the data sets but also the trade-off between fitting the different data sets. The analysis of this trade-off gives insight into the compatibility between the seismic data sets and the magnetotelluric data and also the appropriate noise level to assume for the seismic data. This additional information helps to assess the validity of the joint model, and we demonstrate the use of our approach with synthetic data under realistic conditions. We apply our method to one site from the Slave Craton and one site from the Kaapvaal Craton. For the Slave Craton we obtain similar results to our previously published models from joint inversion of receiver functions and magnetotelluric data but with improved resolution and control on absolute velocities. We find a conductive layer at the bottom of the crust, just above the Moho; a low-velocity, low-resistivity zone in the lithospheric mantle, previously termed the Central Slave Mantle Conductor; and indications of the lithosphere-asthenosphere boundary in terms of a decrease in seismic velocity and resistivity. For the Kaapvaal Craton both the seismic and the MT data are of lesser quality, which prevents as detailed and robust an interpretation; nevertheless, we find an indication of a low velocity low-resistivity zone in the mantle lithosphere. These two examples demonstrate the potential of joint inversion, particularly in combination with nonlinear optimization methods.

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