A Neural Network Receiver for EM-MWD Communication

Whitacre, Timothy

doi:10.15368/theses.2011.91

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…Conventional communication systems use filters to cancel noise in a received signal. Based on the previous study [5,6] and confirmation from our findings, noise sources on or near the drill rig usually have large power spectral densities over a broad frequency band including the transmitting frequency. Very low signal to noise ratio data, with noise and main signals operating in the same frequency, require highly effective data processing techniques.…”

Section: Introductionsupporting

confidence: 85%

“…Noise suppression techniques have been introduced in several signal processing studies with similar noise characteristics, where the magnitude of noise recorded is higher than recorded signals and or data with very low signal to noise ratio is recovered. This includes adaptive filtering techniques such as least mean square, recursive least square, normalized least mean square, multichannel acoustic echo canceler, nonlinear adaptive filtering and other noise reduction techniques, spectral subtraction method and neural network techniques [5,6,[10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…However, most of these methods achieve better noise cancellation only under certain conditions or by combining several denoising strategies; for example, spectral subtraction method used by reference [6] in EM MWD data processing resulted in improvement of the signal to noise ratio in the presence of typical rig surface noise. However, the improvement was limited to effective suppression of receiver-filtering related noise, with limitations faced in the retrieving of the coded signal.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Processing for EM Telemetry Signal Recovery: Field Data from Sichuan Province

Fayemi

Zhen

et al. 2020

Energies

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This paper deals with the study of multi-channel adaptive noise cancellation with a focus on its application in electromagnetic (EM) telemetry. We presented new variable step-size least mean square (LMS) techniques: regularized variable step-size LMS and regularized sigmoid variable size LMS, for electromagnetic telemetry data processing. Considering the complexity and spatial distribution of environmental noise, algorithms with multiple reference signals were used to retrieve transmitted EM signals. The feasibility of the regularized variable step size LMS algorithms with numerical simulation was analyzed and presented. The adaptive processing techniques were applied in the recovery of frequency and binary phase shift key modulated signal. The proposed multi-channel adaptive technique achieves fast convergence speed, low mean squared error and is shown to have good convergence characteristics compared to conventional methods. In addition to attaining good results from the multi-channel adaptive filter and performing the signal analysis in real-time, we implemented combined fast effective impulse noise removal techniques. The combination of median and mean filters was effective in removing a wide range of impulsive noises without distorting any other data points. Further, electromagnetic telemetry data were acquired during a drilling operation in Sichuan province, China, for real field application. Data processing workflow was designed for EM telemetry data processing based on the noise characteristics, simulation results and expected result for demodulation. To establish a comprehensive overview, a performance comparison of the acquisition array system is also provided. Conclusively, the introduced multichannel adaptive noise canceling techniques are very effective in recovering transmitted EM telemetry signals.

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Section: Introductionsupporting

confidence: 85%