Auto measurement while drilling mud pulse signal recognition based on deep neural network

The noise intensity of drilling mud pulse signal is large. It is difficult to recognize signal at once because of the low signal to noise ratio (SNR) of the detected signals. Moreover, the SNR has a low accuracy. A method which is called stacked wavelet autoencoder (SWAE) for recognizing mud pulse signals based on deep learning is proposed in this paper. The model is composed of wavelet neural network and autoencoder, which are trained for mud pulse signal classification specifically. Combining with the drilling mud pulse signal, the recognition performance of the typical data set is analyzed and tested. SWAE enhances the SNR of output signal by using signal detection method. And then the output detected signal is considered as a signal classification attribute. Finally, experimental results show that SWAE is suitable for mud pulse signal recognition, and it has strong ability to extract features from samples automatically and robustness. Experimental results confirm that the performance of SWAE is better than the methods in the state of the art under the same SNR.