Summary During the course of the German continental deep drilling project (KTB) two scientific drill holes were drilled, the KTB Vorbohrung down to 4 km and the KTB Hauptbohrung down to 9.1 km, both intersecting several cataclastic shear‐zones. As few drill cores were available in the KTB Hauptbohrung, most of the petrophysical and geochemical data are based on drill cuttings investigations. We present an analysis of drill cuttings data, addressing the question of what relationship between cataclastic shear‐zones and petrophysical and geochemical data can be revealed. For that purpose we developed a regression model with the amount of cataclastic rocks in drill cuttings as a dependent variable and the petrophysical and geochemical variables as regressors. We use depth related data from two sections of the KTB Hauptbohrung with cataclastic shear‐zones in gneiss (1738–2380 m) and in metabasite (4524–4908 m). The variables are selected by estimating and testing a linear regression model taking into account the autocorrelation of the data due to the depth structure. The variables which characterize the cataclastic shear‐zones in gneiss according to our model are the contents of carbon and crystal water and the thermal conductivity, each with positive coefficients. This model explains, in total, 57 per cent of the variance of the observed data. For cataclastic shear‐zones in metabasite the content of crystal water and the magnetic susceptibility with positive coefficients and the content of chromium with a negative coefficient are the significant variables. The explained variance in this model is 60 per cent. Being significant in both lithologies, the content of crystal water is an important variable for cataclastic shear‐zones. The prediction of shear zones is feasible by our methods, but the results of our study should be confirmed and widened by investigations of other data sets.

Markov Chain Monte Carlo, Bayesian semiparametric binary regression, Bayesian P-splines, Patent opposition,

In this paper we present a nonparametric Bayesian approach for fitting unsmooth or highly oscillating functions in regression models with binary responses. The approach extends previous work by Lang et al. (2002) for Gaussian responses. Nonlinear functions are modelled by first or second order random walk priors with locally varying variances or smoothing parameters. Estimation is fully Bayesian and uses latent utility representations of binary regression models for efficient block sampling from the full conditionals of nonlinear functions.

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