Abstract: The invention concerns a method for the determination of local similarity values for geological units in the subsurface from a seismic 3-D dataset, which consists of a multitude of traces, each of which being formed by a sequence of data points that carry amplitude values, and especially a method for the determination of local dip-dependent similarity values, in each weighted environment of an analysis point, determining the maximum similarity value, which is assigned, together with the corresponding dip angle and dip azimuth, to the respective analysis point.

Abstract: The invention concerns a method for the determination of local similarity values for geological units in the subsurface from a seismic 3-D dataset, which consists of a multitude of traces, each of which being formed by a sequence of data points that carry amplitude values, or seismic attributes derived thereof, with the following steps: Assigning a weighted environment to a respective analysis point, whereby weight factors according to a weight function are assigned to the data points in the environment of the analysis point, and calculating similarity values in each weighted environment, whereby the weight function is included in the similarity measure; and especially a method for the determination of local dip-dependent similarity values with the steps: Assigning a weighted environment to a respective analysis point, whereby weight factors according to a weight function are assigned to the data points in the environment of the analysis point, calculating similarity values in each weighted environment, where

Abstract: A method is provided for processing a seismic 3-D measurement data set, consisting of a multitude of seismic traces, each having a sequence of amplitude values or data points provided with acoustic impedances. The method consists of the following: selecting a reference section at a predetermined location and depth which comprises neighboring trace sections of several seismic traces; determining the similarity between the selected reference section and localized sections of seismic data from the measurement data set; creating a volume of data which corresponds to the measurement data record, using the similarity value which has been determined and allocated to each data point as the attribute. During processing of the seismic data, the sub-surface images are able to be classified by an absolute comparison of the measurement data with a reference sample section as the means of interpretation.

Abstract: The invention relates to a method for processing a 3-D measurement data set provided with seismic attributes, by means of a neural network, whereby a self-organizing map is trained with selected training data, and the data to be examined are classified according to the self-organizing map. In this connection, the measurement data are processed by way of sub-volumes comprising a spatial environment of each sample. The classification is stored in a result data set, and displayed, for example in a two-dimensional map (FIG. 3).

Abstract: The invention relates to a method for processing a seismic 2-D or 3-D measurement data set comprised of a multitude of seismic traces each comprising a series of data points provided with amplitude values. The inventive method is characterized by the following steps: Converting the measurement data set into a binary data set in which either the number “0” is assigned to each data point when an amplitude value is less than a predetermined threshold value, or else the number “1” is assigned to each data point; including a vicinity which is located around each binarized data point and which is defined by a predetermined cell size in a similarity analysis, hereby a value is assigned to each data point.