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We also calculate a tradeoff curve between the data misfits and model roughness.
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The spatial and temporal damping parameters are found from trade-off curves between the data misfit and model length.
The trade-off between the data misfit χ2(m), and the regularization R2(m) in Eq. (9) is governed by the choice of the regularization parameter λ.
The data misfit χ2(m) measures the difference between the observed data, and data predicted by forward modelling for given model m.
Therefore, the data misfit corresponds to a dimensionless, weighted L2 norm of differences between the predicted and observed magnetic fields over the Earth's surface and time.
The first term of (6) is the data misfit functional, determined as the square norm of the difference between the observed and predicted data, and the second term is a stabilizing functional, the stabilizer.
The minimization loops are repeated until the data misfit is small enough.
Taking the total derivative of the data misfit in Eq. (15) yields (23 where means real part.
Then, it is not easy to get the data misfit small.
The data misfit and model recovery are given in Table 7.
(alpha ^2) is adjusted to achieve the desired trade-off between data misfit and model complexity.
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