Measurement While Drilling (MWD) Signal Analysis, Optimization and Design

Chin, Wilson C.; Su, Yinao; Sheng, Limin; Li, Lin; Bian, Hailong; Shi, Rong

doi:10.1002/9781118831724

Cited by 9 publications

(2 citation statements)

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“…The extracted features are inputted to the softmax classifier to achieve the classification recognition function. X which are got by adding the sample data X and noise by denoising coefficient as model's input data to train the network parameters of the first hidden layer, and the output 1 Y of the first hidden layer is calculated by using the trained network parameters; Step3: We use ' i Y which are got by adding the ( 2) i i  th output and noise by denoising coefficient as 1 i  th layer's input data, and use the same method as Step2 to train the network parameters of the 1 i  th layer, this step is repeated until all the hidden layers are trained completely;…”

Section: Training Steps Of Sdamentioning

confidence: 99%

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SDA-based Neural Network Approach for MWD Mud Pulse Signal Recognition

Zhang¹,

Guo²,

Zhang³

et al. 2018

dtcse

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Abstract. The low signal to noise ratio (SNR) of the detected mud pulse signal leads to difficult to recognize signal at once. The recognize accuracy is low. So a stacked denoising autoencoder recognition model was constructed. Combining with the drilling mud pulse signal, the recognition performance of the typical data set is analyzed and tested. The proposed method of recognizing mud pulse signal enhances the SNR of output signal by using signal detection method. And then we take the output detected signal as signal classification attribute. Test results also show that the proposed method is suitable for mud pulse signal recognition, and it has strong ability to extract features from samples automatically and robustness. Performance is better than pattern matching and support vector machine and other recognition methods under the same SNR.

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Section: Training Steps Of Sdamentioning

confidence: 99%

“…Then the equipment on the ground receives a series of transmitted signals according to the prior agreed protocol and processes the signals to extract the final useful parameters. So, it is essential to process signals, which determines the decision of the expert guidance, but also is now the focus of research and difficulty problem [1,2].…”

Section: Introductionmentioning

confidence: 99%