A method for determining the geometry of one or multiple real, examined defects of a metallic and in particular magnetizable object, in particular a pipe or a tank, by means of at least two reference data sets of the object generated on the basis of different, nondestructive measurement methods, - wherein the object is represented in an EDP unit at least partially on an or by an at least two-dimensional, preferably three-dimensional object grid, - comprising a determination of at least an initial defect geometry as a starting defect geometry, - determining respective prediction data sets as starting prediction data sets on the basis of the starting defect geometry by simulation or assignment of a measurement matching the respective reference data set, - iteratively adapting the starting defect geometry to the geometry of the real defect(s) by means of the EDP unit and by means of at least one, preferably multiple expert routines, which in particular run in competition and furthermore in particular run in parallel to one another, - wherein a respective expert defect geometry is generated in the respective expert routines(s) by means of at least one intrinsic algorithm and on the basis of the starting defect geometry, - on the basis of the respective expert defect geometry, respective expert prediction data sets are determined by simulation or assignment of a measurement matching the respective reference data set, - and the expert defect geometry underlying the respective expert prediction data sets is then provided to at least one, in particular all of the expert routines as a new starting defect geometry for further adaptation to the geometry of the real defect(s), if the expert prediction data sets of a respective expert routine are more similar to the respective reference data sets than the starting prediction data sets and/or a fitness function taking into consideration the at least two expert prediction data sets is improved, - and subsequently the expert prediction data sets associated with the new starting defect geometry are used as the new starting prediction data sets, - wherein the iterative adaptation by means of the expert routines takes place until a stop criterion is fulfilled, - and a data set on the basis of an MFL, eddy current, EMAT, or ultrasonic measurement method is used as the first reference data set and a data set generated on the basis of a further and different measurement method originating from this group of measurement methods is used as the further reference data set. - wherein the object is represented in an EDP unit at least partially on an or by an at least two-dimensional, preferably three-dimensional object grid, - comprising a determination of at least an initial defect geometry as a starting defect geometry, - determining respective prediction data sets as starting prediction data sets on the basis of the starting defect geometry by simulation or assignment of a measurement matching the respective reference data set, - iteratively adapting the starting defect geometry to the geometry of the real defect(s) by means of the EDP unit and by means of at least one, preferably multiple expert routines, which in particular run in competition and furthermore in particular run in parallel to one another, - wherein a respective expert defect geometry is generated in the respective expert routines(s) by means of at least one intrinsic algorithm and on the basis of the starting defect geometry, - on the basis of the respective expert defect geometry, respective expert prediction data sets are determined by simulation or assignment of a measurement matching the respective reference data set, - and the expert defect geometry underlying the respective expert prediction data sets is then provided to at least one, in particular all of the expert routines as a new starting defect geometry for further adaptation to the geometry of the real defect(s), if the expert prediction data sets of a respective expert routine are more similar to the respective reference data sets than the starting prediction data sets and/or a fitness function taking into consideration the at least two expert prediction data sets is improved, - and subsequently the expert prediction data sets associated with the new starting defect geometry are used as the new starting prediction data sets, - wherein the iterative adaptation by means of the expert routines takes place until a stop criterion is fulfilled, - and a data set on the basis of an MFL, eddy current, EMAT, or ultrasonic measurement method is used as the first reference data set and a data set generated on the basis of a further and different measurement method originating from this group of measurement methods is used as the further reference data set.