Sensitivity and depth of investigation from Monte Carlo ensemble statistics

For many geophysical measurements, such as direct current or electromagnetic induction methods, information fades away with depth. This has to be taken into account when interpreting models estimated from measurements. that reason, a measurement sensitivity analysis and determining the depth of investigation are standard steps during data processing. In deterministic gradient-based inversion, most used measure, differential sensitivity, is readily available since these inversions require computation Jacobian matrices. contrast, may not in Monte Carlo inversion methods do necessarily include linearization forward problem. Instead, prior ensemble simulate an responses. Then, updated according Bayesian inference. We propose use covariance between response for constructing measures. approaches, estimation this does additional computations model. Normalizing by variance ensemble, one obtains simplified regression coefficient. investigate differences coefficient using simple models. linear models, equal except influences sampling error correlation structure distribution. non-linear case, behaviour measure analysed model frequency-domain Differential similar intervals on which approximately linear. Differences two measures increase degree non-linearity range. Additionally, we measure. Correlation yields normalized version inversions.