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

1. 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.

2. The method of claim 1 , wherein the input dimension is at least 2, and the prediction dimension is 1.

3. The method of claim 1 , wherein the input dimension is at least 3, and the prediction dimension is selected from the group consisting of 1 and 2 dimensions.

4. The method of claim 1 , wherein the input dimension is at least 4, and the prediction dimension is selected from the group consisting of 1, 2 and 3 dimensions.

5. The method of claim 1 , wherein the input dimension is at least 5, and the prediction dimension is selected from the group consisting of 1, 2, 3 and 4 dimensions.

6. The method of claim 1 , wherein the multi-dimensional seismic data is selected from the group consisting of 2D seismic data and 2D data extracted from seismic data of 3 or more dimensions.

7. The method of claim 1 , wherein the multi-dimensional seismic data set is selected from the group consisting of 3D seismic data and 3D data extracted from seismic data of 4 or more dimensions.

8. The method of claim 1 , wherein the multi-dimensional seismic data set is selected from the group consisting of 4D seismic data and 4D data extracted from seismic data of 5 or more dimensions.

9. The method of claim 1 , wherein the multi-dimensional seismic data set comprises multiple attributes.

10. The method of claim 9 , wherein the multiple attributes comprise 3 color channels.

11. The method of claim 1 , wherein the predicted value is made on a 1D seismic array from an input dimension of at least of 2.

12. The method of claim 1 , wherein the predicted value is made on a 2D seismic array from an input dimension of at least 3.

13. The method of claim 1 , wherein the predicted value is made on a 3D seismic array from an input dimension of at least 4.

14. The method of claim 1 , wherein the predicted value is made on a 4D seismic array from an input dimension of at least 5.

15. The method of claim 1 , wherein the predicted value is made on a 5D seismic array from an input dimension of at least 6.

16. The method of claim 1 , wherein the backpropagation-enabled process is a deep learning process.

17. The method of claim 1 , wherein the backpropagation-enabled process is a supervised regression process, comprising the step of comparing attributes computed in a conventionally computed technique with the ones from a supervised regression technique.

18. The method of claim 1 , wherein the backpropagation-enabled process is selected from the group consisting of supervised, semi-supervised, unsupervised processes and combinations thereof.

19. The method of claim 1 , wherein the multidimensional seismic data set is comprised of seismic data selected from the group consisting of field-acquired data, synthetic data, and combinations thereof.