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With the improvement of intelligence, the drilling industry ’s demand for real-time identification of lithology while drilling was becoming more and more urgent. An intelligent inversion method of lithology while drilling is proposed based on the acoustic signal and vibration signal ( acoustic vibration signal ) of broken rock during drilling. Firstly, the original signal samples were obtained by drilling seven different types of rocks through indoor micro-drilling experiments. During the acquisition process, the drilling parameters ( drilling speed, rotation speed, bit size ) were changed and the corresponding signal data were obtained. According to the characteristics of the collected acoustic vibration signal, the time-frequency image with signal characteristics was obtained by short-time Fourier transform. On this basis, an improved VGG16 convolutional neural network model was constructed to realize the intelligent identification of lithology, and the training, evaluation and tuning of the model are realized by hyperparameter optimization. Then, the transfer learning training strategy is introduced, and different drilling parameters were used as data labels. According to the parameter values, the source domain and the target domain were divided to realize the rapid identification of the small sample target domain. The experimental results show that the transfer learning results of the model are different with the change of drilling parameters. The lithology inversion model based on acoustic-vibration signal training has high prediction accuracy and strong generalization ability. The accuracy of the acoustic signal test set is up to 99%, and the accuracy of the vibration signal test set is up to 100%. Under the change of penetration rate, the acoustic and vibration signals are least affected, which can achieve more excellent results when used as data labels for lithology inversion, and the accuracy of lithology inversion is the highest when the penetration rate is small as the target domain. In the process of lithology inversion, different signal types are suitable for different rocks. Among them, the sound signal has the highest applicability to coarse yellow sandstone, and the vibration signal is more suitable for granite. The research results have certain reference value for improving the intelligent degree of working face drilling.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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