Here, denotes the th spatial window (corresponding to th trace) with a radius of , denotes the flattened local spatial window, denotes the SVD denoised local spatial window, denotes the averaged local spatial window, and denotes the output data using SOSVD.

As we can see from the workflow, the key step that distinguishes SOSVD with other types of SVD approaches is the flattening in the local spatial window. The flattening corresponds to applying a flattening operator to the data (here we use a prediction operator according to local slope) so that the output data have horizontal events:

where is the th flattening operator. Here, the flattening operator is chosen as a plane-wave prediction operator related with the local slope. Equation 7 has the following detailed form:

Here, denotes the prediction operator from trace to trace in th spatial window, which is connected with the local slope of th trace. Prediction of a trace consists of shifting the original trace along dominant event slopes (Fomel, 2010). Prediction of a trace from a distant neighbour can be accomplished by simple recursion (Liu et al., 2010), i.e., predicting trace from trace is simply

The prediction operator is a numerical solution of the local plane differential equation

for local plane wave propagation in the direction.

The dominant slopes are estimated by solving the following least-square minimization problem using regularized least-squares optimization:

where is the destruction operator defined as

2020-03-09