A computer-implemented method for geophysical data mining, the method comprising: a) receiving, by a processor, a first survey data representative of a subsurface volume of interest and a second survey data representative of the subsurface volume of interest, wherein the first survey data and the second survey data are acquired at different survey times; wherein each of the first survey data and the second survey data comprise a plurality of data-points, wherein the plurality of data-points are arranged in a data matrix with N W columns and N S rows, each data-point of the plurality of data-points is associated to a j-th column of the data matrix representing a position x j along a x-axis and to an i-th row of the data matrix representing a position t i along a t-axis; b) estimating, by a processor, an error distribution matrix for the plurality of data-points of the first survey data, wherein each element of the matrix is based on a standard deviation matrix and a correlation matrix of each data-point of the first survey data; c) generating, by a processor, a current error model by partitioning the error distribution matrix in a number of rectangular cells, wherein the current model is defined by error model parameters, and wherein the error model parameters comprise the number of rectangular cells and, for each rectangular cell of the number of rectangular cells, a position on the x-axis of the center of the each rectangular cell, a size in the x-axis of the each rectangular cell, a position on the t-axis of the center of the each rectangular cell, a size in the t-axis of the each cell, and a weight of each cell, wherein the weights define a likelihood function of the second survey data with respect to the first survey data; d) modifying, by a processor, the error model parameters of the current error model to generate a candidate error model based on a probability condition, wherein the probability condition defines a probability of occurrence of a way of modifying the error model parameters of the current error model; e) storing, in a memory, the error model parameters of the candidate error model as one element of a Markov Chain based on a result of comparing a likelihood value of the current error model and a likelihood value of the candidate error model, wherein both likelihood values are based on a difference between a magnitude of the data points of first survey and the second survey; f) repeating, by a processor, steps d) and e) as the candidate error model being the current error model, until convergence of the Markov Chain; and g) generating, by a processor, a probability weight map using a plurality of partition models generated by assigning a weight to each of the plurality of data-points based on the error model parameters of the candidate error models which form the Markov Chain. a) receiving, by a processor, a first survey data representative of a subsurface volume of interest and a second survey data representative of the subsurface volume of interest, wherein the first survey data and the second survey data are acquired at different survey times; wherein each of the first survey data and the second survey data comprise a plurality of data-points, wherein the plurality of data-points are arranged in a data matrix with N W columns and N S rows, each data-point of the plurality of data-points is associated to a j-th column of the data matrix representing a position x j along a x-axis and to an i-th row of the data matrix representing a position t i along a t-axis; b) estimating, by a processor, an error distribution matrix for the plurality of data-points of the first survey data, wherein each element of the matrix is based on a standard deviation matrix and a correlation matrix of each data-point of the first survey data; c) generating, by a processor, a current error model by partitioning the error distribution matrix in a number of rectangular cells, wherein the current model is defined by error model parameters, and wherein the error model parameters comprise the number of rectangular cells and, for each rectangular cell of the number of rectangular cells, a position on the x-axis of the center of the each rectangular cell, a size in the x-axis of the each rectangular cell, a position on the t-axis of the center of the each rectangular cell, a size in the t-axis of the each cell, and a weight of each cell, wherein the weights define a likelihood function of the second survey data with respect to the first survey data; d) modifying, by a processor, the error model parameters of the current error model to generate a candidate error model based on a probability condition, wherein the probability condition defines a probability of occurrence of a way of modifying the error model parameters of the current error model; e) storing, in a memory, the error model parameters of the candidate error model as one element of a Markov Chain based on a result of comparing a likelihood value of the current error model and a likelihood value of the candidate error model, wherein both likelihood values are based on a difference between a magnitude of the data points of first survey and the second survey; f) repeating, by a processor, steps d) and e) as the candidate error model being the current error model, until convergence of the Markov Chain; and g) generating, by a processor, a probability weight map using a plurality of partition models generated by assigning a weight to each of the plurality of data-points based on the error model parameters of the candidate error models which form the Markov Chain.