NMR sequential prediction

1. column d is prediction of every group in one ANN model separately.
2. column e is sequential prediction.

Column a is accuracy of predicting 64 bins together and measuring accuracy of every group while normalized using maximum and minimum values of 64 bins. Column b is the ranking result. Column c is the number of bins in every group.

From the results, we can see that the two methods that we discussed yesterday do not perform better than predicting 64 bins together. The reason may be:

1. 64 bins together represent the distribution of pore size.
2. some group just means some pore size, which cannot be closely correlated to those input logs. The concentration of pore size at every depth is different from each other.
3. what matters is the dominated pore size at every depth, which is different from each other.

I think the sequential prediction for NMR and DD logs are different. 8 DD logs are kind of independent of each other at different frequencies, each representing conductivity or permittivity of the reservoir. However, 64 bins and 8 groups at every depth represent pore size distribution together, which cannot be divided. I think that is why the two methods cannot predict NMR t2 distribution separately with high accuracy.

Tomorrow, I will select inputs and do k-fold cross validation.