Method for predicting subsurface features from seismic using deep learning dimensionality reduction for regression

A method for training a backpropagation-enabled regression process for predicting values of an attribute of subsurface data, the method comprising the steps of:

• (a) inputting a multi-dimensional seismic data set with an input dimension, n, of at least two into a backpropagation-enabled process;
• (b) convolving the multi-dimensional seismic data set using convolutional filters learned from the backpropagation-enabled process to produce a set of input feature maps having a convolutional dimension equal to the input dimension;
• (c) downscaling the set of input feature maps to produce a downscaled array of modified feature maps using the convolutional filters learned from the backpropagation-enabled process, so that at least one, but no more than n−1, dimension of the downscaled array is one;
• (d) upscaling the downscaled array to produce an upscaled array of further modified feature maps using the convolutional filters learned from the backpropagation-enabled process, wherein the upscaled array has the same dimension of the downscaled array, and
• (e) computing a predicted value of the attribute from the upscaled array, wherein the predicted value has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.