陆相页岩气资源评价中人工智能算法的探索——以苏北盆地溱潼凹陷为例
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谈迎, 杨伟松, 李振生
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The tentative application of artificial intelligence algorithm to evaluating continental shale gas resources: A case study of Qintong sag in Subei (North Jiangsu) Basin
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Ying TAN, Wei-Song YANG, Zhen-Sheng LI
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表3 溱潼凹陷阜二段评价时主因子得分矩阵 |
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样品区号 | 主因子 | F1 | F2 | F3 | F4 | F5 | F6 | 1 | -1.21 | -0.09 | -0.12 | -0.49 | -0.80 | -0.58 | 2 | -1.25 | -0.13 | -0.25 | -0.20 | 0.11 | -0.25 | 3 | -1.09 | 0.12 | -0.11 | -0.80 | 0.72 | -1.01 | 4 | 0.81 | 0.01 | 1.49 | -0.25 | 0.33 | 0.93 | 5 | 0.88 | 0.47 | 0.52 | 0.01 | -0.35 | 1.39 | 6 | -1.16 | -0.42 | 0.91 | 0.17 | 1.11 | 0.07 | 7 | 0.62 | -0.08 | 1.26 | -1.15 | 0.69 | 0.02 | 8 | 1.31 | 0.82 | 0.31 | -1.99 | -0.87 | -1.78 | 9 | 0.66 | 1.42 | -2.53 | -0.73 | 1.62 | 1.49 | 10 | 0.24 | -0.80 | 1.27 | 0.63 | 0.48 | 1.09 | 11 | 0.48 | -0.03 | -0.40 | -0.04 | -2.06 | 0.68 | 12 | 0.61 | 2.18 | 0.42 | 2.69 | 0.44 | -1.41 | 13 | -0.61 | 0.33 | 0.08 | 0.32 | -1.71 | 0.74 | 14 | -1.00 | 0.26 | -0.72 | 0.10 | -1.22 | -0.47 | 15(全零值) | -1.36 | -0.32 | -0.42 | 0.21 | -0.57 | 0.34 | 16(全中值) | 0.57 | -1.66 | -0.97 | 1.08 | 0.25 | 0.10 | 17(全优值) | 1.50 | -2.11 | -0.98 | 0.45 | 0.20 | -1.36 |
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