Lithological identification based on high-frequency vibration signal analysis

Lithology changes affect drilling efficiency and safety during drilling. At present, lithology is usually identified by analyzing logging data in engineering applications. There is a certain lag due to the limitation of logging instrument installation location. This paper proposes a new rock formation identification method, which bases on high-frequency measurement sensors to record the vibration of drilling tools, and extracts the time and frequency-domain features of data. Then neural network is used to establish the lithology recognition model, so as to identify the rock formation change by using vibration signal. The method has been verified by field experiment. A lithology identification model is established by using the features of vibration signal. And average recognition accuracy of the model is 89.57%. The model accurately identifies the Soil layer, Sandstone, Strongly weathered siltstone and Medium-weathered siltstone. The identification results are in good agreement with the geological information.