Page 131 - Machine Learning for Subsurface Characterization

108 Machine learning for subsurface characterization
5 8 7 6 EPSI_XO_F0 COND_XO_F0 (MM/M) DTC_BHP (us/ft) RLA1 (OHMM) 80. 0.8 1000. 0. 160. 40. 2000. EPSI_XO_F1 COND_XO_F1 (MM/M) DTS_BHP (us/ft) RLA2 (OHMM) 80. 0.8 1000. 0. 160. 40. 2000. EPSI_XO_F2 COND_XO_F2 (MM/M) RLA3 (OHMM) 80. 0.8 1000. 0. 2000. EPSI_XO_F3 COND_XO_F3 (MM/M) 80. 0.8 1000. 0. Fea
0.2 0.2 0.2 Track
3. 0.5 6. RLA3);

4 RHOZ (G/C3) VCL_BHP (V/V) PEFZ (B/E) and RLA2,

2. 0. 0.
0.4 0.4 (RLA1,

3 DPHZ (CFCF) NPOR (CFCF) investigation

0. 0. of
150. depths
GR_EDTC (GAPI) six

2 the

0. of out
XX00 XX00
1 FIG. 4.2 three at