A computer implemented method of processing seismic data, comprising: accessing seismic data representative of a plurality of seismic input traces acquired by one or more seismic sensors; stacking the plurality of seismic input traces into a stacked trace; generating, utilizing at least one processor unit, a function of similarity between at least two of the plurality of seismic input traces; the method being characterized in that generating the function of similarity comprises calculating a covariance matrix by combining each of the at least two of the plurality of seismic input traces with its corresponding stacked trace and comparing the combined traces; and scaling at least one of the seismic input traces or the stacked trace with the function of similarity; the method further comprising: generating at least one measurement of similarity comprising a mean derived from a covariance matrix for a respective one of a first plurality of sample windows and a corresponding respective one of a second plurality of sample windows; wherein generating at least one measurement of similarity further comprises: generating (204, 212) a respective covariance matrix for each of the respective first and second pluralities of sample windows using at least two of the plurality of input traces; and generating (206, 214) the respective measurement of similarity by determining a mean of each respective covariance matrix; and wherein the mean is determined by summing all of the elements of each respective covariance matrix and dividing a resulting sum by a number of elements in each respective covariance matrix; and wherein elements of the covariance matrix for each respective sample window are generated by the equation C ij = 1 N − 1 ∑ k = 1 N t ki − t k ̅ t kj − t k ̅ where C ij denotes a covariance in a sample window between a seismic input trace i and a seismic input trace j, t ki is a k th sample in seismic input trace i, N is a number of samples in the sample window, and t k is a k th sample in the stacked trace. accessing seismic data representative of a plurality of seismic input traces acquired by one or more seismic sensors; stacking the plurality of seismic input traces into a stacked trace; generating, utilizing at least one processor unit, a function of similarity between at least two of the plurality of seismic input traces; the method being characterized in that generating the function of similarity comprises calculating a covariance matrix by combining each of the at least two of the plurality of seismic input traces with its corresponding stacked trace and comparing the combined traces; and scaling at least one of the seismic input traces or the stacked trace with the function of similarity; the method further comprising: generating at least one measurement of similarity comprising a mean derived from a covariance matrix for a respective one of a first plurality of sample windows and a corresponding respective one of a second plurality of sample windows; wherein generating at least one measurement of similarity further comprises: generating (204, 212) a respective covariance matrix for each of the respective first and second pluralities of sample windows using at least two of the plurality of input traces; and generating (206, 214) the respective measurement of similarity by determining a mean of each respective covariance matrix; and wherein the mean is determined by summing all of the elements of each respective covariance matrix and dividing a resulting sum by a number of elements in each respective covariance matrix; and wherein elements of the covariance matrix for each respective sample window are generated by the equation C ij = 1 N − 1 ∑ k = 1 N t ki − t k ̅ t kj − t k ̅ where C ij denotes a covariance in a sample window between a seismic input trace i and a seismic input trace j, t ki is a k th sample in seismic input trace i, N is a number of samples in the sample window, and t k is a k th sample in the stacked trace. generating at least one measurement of similarity comprising a mean derived from a covariance matrix for a respective one of a first plurality of sample windows and a corresponding respective one of a second plurality of sample windows; wherein generating at least one measurement of similarity further comprises: generating (204, 212) a respective covariance matrix for each of the respective first and second pluralities of sample windows using at least two of the plurality of input traces; and generating (206, 214) the respective measurement of similarity by determining a mean of each respective covariance matrix; and generating (204, 212) a respective covariance matrix for each of the respective first and second pluralities of sample windows using at least two of the plurality of input traces; and generating (206, 214) the respective measurement of similarity by determining a mean of each respective covariance matrix; and wherein the mean is determined by summing all of the elements of each respective covariance matrix and dividing a resulting sum by a number of elements in each respective covariance matrix; and wherein elements of the covariance matrix for each respective sample window are generated by the equation C ij = 1 N − 1 ∑ k = 1 N t ki − t k ̅ t kj − t k ̅ where C ij denotes a covariance in a sample window between a seismic input trace i and a seismic input trace j, t ki is a k th sample in seismic input trace i, N is a number of samples in the sample window, and t k is a k th sample in the stacked trace.