The 2‐D magnetotelluric inverse problem solved with optimization

• Published in: Geophysical journal international Vol. 184; no. 2; pp. 639 - 650
• Main Authors: Van Beusekom, Ashley E., Parker, Robert L., Bank, Randolph E., Gill, Philip E., Constable, Steven
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.02.2011
• Subjects: Geomagnetic induction; Geophysics; Inverse problems; Inverse theory; Magnetotelluric; Non‐linear differential equations; Numerical solutions; Optimization
• ISSN: 0956-540X; 1365-246X
• DOI: 10.1111/j.1365-246X.2010.04895.x

SUMMARY The practical 2‐D magnetotelluric inverse problem seeks to determine the shallow‐Earth conductivity structure using finite and uncertain data collected on the ground surface. We present an approach based on using PLTMG (Piecewise Linear Triangular MultiGrid), a special‐purpose code for optimization with second‐order partial differential equation (PDE) constraints. At each frequency, the electromagnetic field and conductivity are treated as unknowns in an optimization problem in which the data misfit is minimized subject to constraints that include Maxwell's equations and the boundary conditions. Within this framework it is straightforward to accommodate upper and lower bounds or other conditions on the conductivity. In addition, as the underlying inverse problem is ill‐posed, constraints may be used to apply various kinds of regularization. We discuss some of the advantages and difficulties associated with using PDE‐constrained optimization as the basis for solving large‐scale nonlinear geophysical inverse problems. Combined transverse electric and transverse magnetic complex admittances from the COPROD2 data are inverted. First, we invert penalizing size and roughness giving solutions that are similar to those found previously. In a second example, conventional regularization is replaced by a technique that imposes upper and lower bounds on the model. In both examples the data misfit is better than that obtained previously, without any increase in model complexity.