Geophysical subsurface modeling is often highly uncertain due to limited data resolution. At the same time, a wealth of geologic information, often from databases and outcrop studies, is available to state prior uncertainty on key geologic modeling parameters. In most inversion procedures, these uncertainties are ignored and only a limited number of geologic scenarios are considered. We have developed a workflow, inspired by a Popper-Bayes philosophy that aims at probabilistically updating prior geologic parameters uncertainty with data without the need for inversion or iterative data conditioning. Our approach consists of stating a wide prior model of uncertainty (in rock physics and facies), and generating a set of facies models and their corresponding geophysical responses (here the seismic response) by forward simulation. Then, a global measure of similarity is defined between any two generated seismic responses and the field data. The proposed measure, based on the wavelet transform decomposition, analyzes differences in patterns found in the seismic data and aims at comparing seismic images as a whole, avoiding any local trace-by-trace comparison. Based on this global measure of similarity, our procedure identifies the geologic interpretations that are unlikely given the observed seismic data. The proposed approach was applied and validated using seismic data from a synthetic example and an offshore reservoir in west Africa. The uncertainty included geologic parameters, such as infill channel size, proportion, and sinuosity, and geophysical parameters, such as the rock physics model and the wavelet in the forward seismic model. The synthetic example allowed for the validation of the methodology using rejection sampling. For the offshore reservoir, updated probabilities were obtained for each uncertain parameter, and some of the prior geologic interpretations were found to be inconsistent with the available seismic amplitude data cube (e.g., narrow channel width).