some trials

Today, I tried some new methods to try to improve the performance of ANN model prediction. But none of them show good results.

Summary:

What I do today:
1. set regularization for the performance function to avoid overfitting, but the performance does not improve.
2. set 10-fold cross validation for the training function to select the best one, but 10 of them all show similar results.
3. delete outliers of every logging data, the number of depths is deleted from 3030 to 2667 in total. Alos, I delete one logging data (from 23 to 22). but the performance of model does not improve much.
4. I discussed with Gawtham today. He has some data which are categories such as if it belongs to an anticline at some depths (1 for yes and 0 for no). So he can create qualitative inputs. But for now i did not have categories, so i cannot apply this method. (Tomorrow i will check IP to see if i can add more logging data like that.)

The above is the comparison plot. it does not improve much compared with results days before.

Tomorrow, I plan to think about how to deal with the 6 parameters. I think the ANN model cannot recognize the 6 parameters' physical meaning. So it cannot perform well.
In addition, I will try to check IP to add some logging data, but maybe it will not help a lot.