3-Cyano-6-(2-thienyl)-4-trifluoromethylpyridine-2(1 H)-one (1) and its thiono analog 2
were prepared by the reaction of (2-thenoyl)- x, x, x-trifluoroacetone with cyanoacetamide or
cyanothioacetamide, respectively. Interaction of compound 1 with ethyl chloroacetate or
chloroacetamide led to the regioselective formation of O-alkylated pyridines 3 and 10. The latter
compounds underwent some successive reactions to furnish the promising furopyridines (4 and
7–9) and pyrazolopyridines (12–15). The reaction of 2 with chloroacetamides or chloroacetonitrile
furnished 2-functionalized 3-amino-6-(2-thienyl)-4-trifluoromethyl-thieno[2,3-b]pyridines (16a, b)
which were used as key intermediates in the synthesis of the title thienopyridines. Structures of
the newly synthesized compounds were established on the basis of their elemental and spectral
(IR, 1H NMR and mass) analyses.

3-Cyano-6-(2-thienyl)-4-trifluoromethylpyridine-2(1 H)-one (1) and its thiono analog 2
were prepared by the reaction of (2-thenoyl)- x, x, x-trifluoroacetone with cyanoacetamide or
cyanothioacetamide, respectively. Interaction of compound 1 with ethyl chloroacetate or
chloroacetamide led to the regioselective formation of O-alkylated pyridines 3 and 10. The latter
compounds underwent some successive reactions to furnish the promising furopyridines (4 and
7–9) and pyrazolopyridines (12–15). The reaction of 2 with chloroacetamides or chloroacetonitrile
furnished 2-functionalized 3-amino-6-(2-thienyl)-4-trifluoromethyl-thieno[2,3-b]pyridines (16a, b)
which were used as key intermediates in the synthesis of the title thienopyridines. Structures of
the newly synthesized compounds were established on the basis of their elemental and spectral
(IR, 1H NMR and mass) analyses.

We consider the inverse problem of identifying large-scale subsurface structures using the controlled source electromagnetic method. To identify structures in the subsurface where the contrast in electric conductivity can be small, regularization is needed to bias the solution towards preserving structural information. We propose to combine two approaches for regularization of the inverse problem. In the first approach we utilize a model-based, reduced, composite representation of the electric conductivity that is highly flexible, even for a moderate number of degrees of freedom. With a low number of parameters, the inverse problem is efficiently solved using a standard, second-order gradient-based optimization algorithm. Further regularization is obtained using structural prior information, available, e.g., from interpreted seismic data. The reduced conductivity representation is suitable for incorporation of structural prior information. Such prior information cannot, however, be accurately modeled with a gaussian distribution. To alleviate this, we incorporate the structural information using shape priors. The shape prior technique requires the choice of kernel function, which is application dependent. We argue for using the conditionally positive definite kernel which is shown to have computational advantages over the commonly applied gaussian kernel for our problem. Numerical experiments on various test cases show that the methodology is able to identify fairly complex subsurface electric conductivity distributions while preserving structural prior information during the inversion.