Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity

Table 5

Performance comparison with different models.

Training algorithm	Training set				Testing set
	R^2	MSE	MAE	RMSE	R^2	MSE	MAE	RMSE
Linear-1	0.890	300.245	11.913	17.328	0.886	299.977	11.952	17.320
Linear-3	0.917	225.643	10.154	15.021	0.902	257.637	10.420	16.051
Linear-5	0.924	206.982	9.754	14.387	0.903	255.466	10.218	15.983
RF-1	0.920	217.056	10.228	14.733	0.913	228.440	10.607	15.114
RF-3	0.929	192.336	9.636	13.869	0.918	214.787	10.165	14.656
RF-5	0.936	173.793	9.156	13.183	0.924	199.431	9.741	14.122
SVR-1	0.931	188.328	9.051	13.723	0.926	193.703	9.324	13.918
SVR-3	0.960	109.210	6.756	10.450	0.950	131.479	7.404	11.466
SVR-5	0.975	67.824	5.392	8.236	0.962	100.597	6.346	10.030
XGB-1	0.981	50.370	4.980	7.097	0.946	142.733	8.004	11.947
XGB-3	0.990	27.193	3.831	5.215	0.953	122.514	7.358	11.069
XGB-5	0.994	17.238	3.062	4.152	0.964	93.628	6.460	9.676
ANN-1	0.925	204.841	9.811	14.312	0.919	213.549	10.164	14.613
ANN-3	0.952	129.916	7.791	11.398	0.943	150.871	8.327	12.283
ANN-5	0.962	103.634	6.938	10.180	0.948	135.592	7.604	11.644