NEURAL-NETWORK ANALYSES OF STRESS-INDUCED OVERPRESSURES IN THE PANNONIAN BASIN

Artificial neural networks can learn relationships between sediment ch aracteristics (burial depth, composition, coordinates and thickness of overlying Quaternary deposits) and overpressures from well data, afte r which they can interpolate and extrapolate to areas and depths not c overed by wells. We analyse data from the south-eastern part of the Pa nnonian Basin. We use a neural network for analysing fluid overpressur es because of the complex interaction of the key variables, making it difficult to derive the functional relationships required for a statis tical analysis. The optimal topology of the network (number of hidden layers and neurons) is found by minimizing the network's training and testing errors. The optimal design of the network resembles the intera ctions scheme of the key variables. The Pannonian Basin, originally fo rmed in an extensional regime, has been in a compressive state of stre ss since Late Pliocene, causing anomalous subsidence patterns. Numeric al forward modelling of compaction-driven fluid overpressures shows th at, due to an increase in the level of compressive interplate stress, the fluid overpressures in the deep subbasins have increased substanti ally since Late Pliocene, giving rise to a very high overpressure (up to 45 MPa) at present. The neural network analyses provide an independ ent estimate of the current amount of overpressuring in this basin, co mplementing the numerical forward modelling results. The overpressure profiles obtained by the two modelling approaches are in excellent agr eement, showing the same magnitude of overpressures, a reversal of the overpressure in the deepest parts of the subbasins and a general decr ease of the overpressure from SW to NE.