Method for processing seismic data

A method for efficiently processing a seismic 2-D or 3-D measurement data set comprising a large number of seismic traces, wherein each data set comprises a series of data points having amplitude values, the method comprising the steps of:

• a) determining a threshold value for the series of data points;
• b) converting the set of measured data into a set of binary data comprising a plurality of binarized data points in which each data point is assigned either a number “0” if an amplitude value is lower than said threshold value or a number “1” if an amplitude value is equal or greater than said threshold value;
• c) forming for each binarized data point a cell of a predetermined cell size from said plurality of binarized data points wherein each cell defines an environment including a central data point;
• and
• d) conducting a similarity analysis on each cell, wherein said central data point for each of said plurality of cells is assigned a similarity value reflecting the degree of similarity of binary data values in each of said plurality of separate cells and wherein said similarity value corresponds to a sum of each quantity of the greater number of data points with the same binary values in each horizontal plane of each cell.

A method for efficiently processing a seismic 2-D or 3-D measurement data set comprising a large number of seismic traces, wherein each data set comprises a series of data points having amplitude values, the method comprising the steps of:

• a) determining a threshold value for the series of data points;
• b) converting the set of measured data into a set of binary data comprising a plurality of binarized data points in which each point is assigned either a number “0” if an amplitude value is lower than said threshold value or a number “1” if an amplitude value is higher than said threshold value;
• c) forming for each binarized data point a cell of a predetermined cell size from said plurality of binarized data points wherein each cell defines an environment including a central data point;
• and
• d) conducting a similarity analysis on each cell wherein a central data point for each of said plurality of cells is assigned a similarity value reflecting the degree of similarity of binary data values in each of said plurality of cells, wherein said similarity value corresponds to a sum of each quantity of the greater number of data points with the same binary value in each trace.