A new geometrical approach for fast prediction of front propagation Abstract   The challenge to predict the future is the very reason why we developed sophisticated methods to integrate data, whatever the domain of application. In the context of the subsurface flow characterisation, the 4D seismic data reflecting the reservoir timelapsed changes as a result of production, requires detailed processing before derived valuable information is incorporated in a subsurface model update. This is a highly uncertain timeconsuming process with embedded uncertainty, which is still a challenge in data assimilation procedure. In this study, we propose a new way to the problem of forecasting. A new framework is proposed to extract a proxy model for front prediction directly from timelapse seismic data. Multiple timelapsed seismic surveys present the evolution of processes happening in the subsurface. These, therefore can provide information on the propagation of saturation fronts. This paper is based on the simple idea that, different snapshots in time should be able to determine a velocity propagation of fronts and from this velocity, we can then predict their future unknown positions. This work applies a modified version of the Eikonal equation and a modified version of the fastmarching method. This new approach of forecasting a front position is a fast track prediction of fronts evolution. Also, this approach is more strongly based on selected information within the data. To test this approach, we used a water injection scenario from a North Sea reservoir, from which we generated the synthetic seismic data. We compared the results from our algorithm with results from the simulation model, showing that with few data inputs (only two fronts) and a short computing time of 90xc2xa0s, this method is able to give a good approximation of the waterflood front evolution. It functions as a rapid solution for front prediction based directly on seismic data, irrespective of the shape of the front (isolated blobs), with the only prerequisite being that the sequence of measured fronts is monotonic.

3D modeling of groundpenetrating radar data across a realistic sedimentary model Abstract   Groundpenetrating radar (GPR) is an established geophysical tool to explore a wide range of nearsurface environments. Today, the use of synthetic GPR data is largely limited to 2D because 3D modeling is computationally more expensive. In fact, only recent developments of modeling tools and powerful hardware allow for a timeefficient computation of extensive 3D data sets. Thus, 3D subsurface models and resulting GPR data sets, which are of great interest to develop and evaluate novel approaches in data analysis and interpretation, have not been made publicly available up to now.  We use a published hydrofacies data set of an aquiferanalog study within fluvioglacial deposits to infer a realistic 3D porosity model showing heterogeneities at multiple spatial scales. Assuming freshwater saturated sediments, we generate synthetic 3D GPR data across this model using novel GPUacceleration included in the opensource software gprMax. We present a numerical approach to examine 3D wavepropagation effects in modeled GPR data. Using the results of this examination study, we conduct a spatial model decomposition to enable a computationally efficient 3D simulation of a typical GPR reflection data set across the entire model surface. We process the resulting GPR data set using a standard 3D structural imaging sequence and compare the results to selected input data to demonstrate the feasibility and potential of the presented modeling studies. We conclude on conceivable applications of our 3D GPR reflection data set and the underlying porosity model, which are both publicly available and, thus, can support future methodological developments in GPR and other nearsurface geophysical techniques.