1. Inversion Transforming the apparent to « real » resistivity. Find a numerical model that explains the field measurment.

2. Direct prob. / Inverse problem Measure or data Parameters or model d m

3. Problem to solve Error on measurment, sub sampling Field constraints Miss choose of relevant parameters Over simplified physical model or « law » Non unicity of the solution A priori knowledge to be included Cost in time, money …..

4. Search for solution Minimise the error (y) between the measred data (d) and the reproduction of these data ( đ ) from a synthetic model (m): Least square (norm L 2 ) : Robust inversion (norm L 1 ) : (filtering ouliers)

5. The mean square approach  Linear case : solution : Non linear case : Gauss-Newton method J = jacobian matrix : Initial model :

6. Gauss-Newton / Quasi Gauss-Newton

7. Damped inversion « damped LS » (Marquardt-Levenberg or Ridge regression) : « Smoothness constraint » : C : « smoothing matrix »

8. Damping factors

9. Initial damping factor : 0.160 – minimum damping factor : 0.015 (valeurs par défaut) Initial damping factor : 0.005 – minimum damping factor : 0.005 (inversion non amortie)

10. Smoothness constraint NO YES

11. « Blocky » inversion Taking into account sharpe changes of resistivity in the model : R m and R d : matrix ginving an independant weigth of data and model in the inversion processus.

13. Initial model

14. Model discretization for forward modelling

15. Model discretization 1 2 3 4 5

16. 1&2 1&2 + 3 4 5

17. Topographical correction