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

Abstract

[1] 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 lithosphereasthenosphere 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 lowvelocity low-resistivity zone in the mantle lithosphere. These two examples demonstrate the potential of joint inversion, particularly in combination with nonlinear optimization methods.Citation: Moorkamp, M., A. G. Jones, and S. Fishwick (2010), Joint inversion of receiver functions, surface wave dispersion, and magnetotelluric data,

Integrating Electromagnetic Data with Other Geophysical Observations for Enhanced Imaging of the Earth: A Tutorial and Review

“…Adding a fourth layer to the inversion allows to satisfy the constraint and fit the data to the same misfit level as the unconstrained inversion. This kind of analysis is similar to the procedure discussed in the tutorial above and in Moorkamp et al (2010). It again highlights the importance of performing individual inversions before attempting any kind of joint inversion or constrained inversion, as it is necessary to have a benchmark misfit.…”

“…As for the MT case in Fig. 2 only the properties of the middle layer (marked in red) are considered unknown Surv Geophys ( ) 38:935-962 939 et al 2007Zevallos et al 2009;Moorkamp et al 2010;Roux et al 2011;Juhojuntti and Kamm 2015). Figure 4 shows the ranges of acceptable models for both data sets for our simple example when assuming coincident boundaries.…”

“…Thus, plotting combinations of misfits for the different data sets and the constraints reveals if they are compatible with each other or minimizing one comes at the cost of deteriorating the match for the other. In practical inversion, applications such plots can be constructed by varying the weight for the different terms of the objective function (Thompson et al 2016) or using specially designed multi-objective minimization algorithms (Kozlovskaya et al 2007;Moorkamp et al 2007Moorkamp et al , 2010Roux et al 2011;Schnaidt and Heinson 2015;Niri and Lumley 2016).…”

Why Curvature in L-Curve: Combining Soft Constraints

“…In such cases, when we have an L-shaped curve, it is reasonable to select the turning point as the point corresponding to the solution. This idea often leads to a good solution; see, e.g., [1,2].…”

Bootstrapping as a Means of Solution Ensemble Based Uncertainty Analysis in Geophysical Inversion Modelling

“…Some of the first attempts on ensemble creation have been made by Press (1968), as well as Wiggins (1969), but it is only now that those techniques have become feasible as the computing power necessary for the ensemble creation is regularly available. Thus, multisolution approaches have recently become more widely used, for example by Sambridge & Drijkoningen (1992), Lomax & Snieder (1995), Moorkamp et al (2010).…”