A method comprising: training a machine learning process using a training data set, the training data set comprising a plurality of image sections of a first seismic cube, wherein an image section comprises a two-dimensional image of a portion of the first seismic cube; receiving a second seismic cube (402a, 408), the second seismic cube (402a, 408) comprising a three-dimensional image of a portion of a subsurface area; providing the second seismic cube (402a, 408) to the machine learning process, the machine learning process comprising one or more neural networks used for predicting a location of a subsurface seismic layer in the second seismic cube (402a, 408); and receiving, from the machine learning process, the prediction of the location of the subsurface seismic layer in the second seismic cube (402a, 408), wherein the training the machine learning process comprises: generating the training data set, wherein generating the training data set comprises: receiving the first seismic cube; determining a first image section of the first seismic cube; iteratively generating intermediate image sections (404b-404n) based on the first image section using a tomographic system, characterized in that a data generation module iteratively generates the one or more intermediate image sections (404b-404n) until a predetermined threshold is satisfied, each intermediate image section generated based on the first image section and one or more previously generated intermediate image sections, and further characterized in generating a final image for the training data set based on the first image section and the intermediate image sections (404b-404n), wherein the final image comprises a plurality of channels for each pixel of the final image, each channel of a pixel comprising a value corresponding to a pixel value of the first image section and each of the intermediate image sections used to generate the final image. training a machine learning process using a training data set, the training data set comprising a plurality of image sections of a first seismic cube, wherein an image section comprises a two-dimensional image of a portion of the first seismic cube; receiving a second seismic cube (402a, 408), the second seismic cube (402a, 408) comprising a three-dimensional image of a portion of a subsurface area; providing the second seismic cube (402a, 408) to the machine learning process, the machine learning process comprising one or more neural networks used for predicting a location of a subsurface seismic layer in the second seismic cube (402a, 408); and receiving, from the machine learning process, the prediction of the location of the subsurface seismic layer in the second seismic cube (402a, 408), wherein the training the machine learning process comprises: generating the training data set, wherein generating the training data set comprises: receiving the first seismic cube; determining a first image section of the first seismic cube; iteratively generating intermediate image sections (404b-404n) based on the first image section using a tomographic system, characterized in that a data generation module iteratively generates the one or more intermediate image sections (404b-404n) until a predetermined threshold is satisfied, each intermediate image section generated based on the first image section and one or more previously generated intermediate image sections, and further characterized in generating a final image for the training data set based on the first image section and the intermediate image sections (404b-404n), wherein the final image comprises a plurality of channels for each pixel of the final image, each channel of a pixel comprising a value corresponding to a pixel value of the first image section and each of the intermediate image sections used to generate the final image. receiving the first seismic cube; determining a first image section of the first seismic cube; iteratively generating intermediate image sections (404b-404n) based on the first image section using a tomographic system, characterized in that a data generation module iteratively generates the one or more intermediate image sections (404b-404n) until a predetermined threshold is satisfied, each intermediate image section generated based on the first image section and one or more previously generated intermediate image sections, and further characterized in generating a final image for the training data set based on the first image section and the intermediate image sections (404b-404n), wherein the final image comprises a plurality of channels for each pixel of the final image, each channel of a pixel comprising a value corresponding to a pixel value of the first image section and each of the intermediate image sections used to generate the final image.