# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
###### Abstract
[5], which had better performance, especially for the non-white noise and ambient noise obtained from actual drilling sites; and the multi-combinational adaptive tracking detection algorithm proposed by [PERSON] et al. [6], which eliminates in-band interference to some extent but still finds it difficult to remove in-band noise of large interference. In summary, all of the above methods have their own limits, and others perform poorly with regard to in-band noise. Hence, the retrieved signals are still riddled with noise, making the demodulation process either more strenuous or ineffective and limiting the effective transmission depth of EM MWD. Therefore, in this study, we focus on the demodulation of retrieved EM telemetry signals using artificial intelligence. Artificial neural networks (ANNs) have been widely applied in data processing in a way similar to that of biological neural networks, i.e., brains [7; 8]. It handles thousands of linear or non-linear inputs simultaneously and learns to process them into something meaningful. Several studies have been carried out on the artificial neural network approach to the signal decision problem of a digital communication receiver. The use of basic backpropagation techniques for the portion of EM MWD signal demodulation was proposed by FARHAD in 1992 [9], and it has since been the most widely populated artificial intelligence technique being used in EM MWD. The results indicate the potential of neural networks for such applications. However, as the signals received on the surface begin to fall below the earth's noise floor and the signal-to-noise ratio falls below useful levels, the technique becomes ineffective. Likewise, [PERSON] [10] carried out a neural network study with a multilayer perceptron, which was trained using backpropagation with gradient descent. The modulation scheme utilized in this study was limited to signal elements that belonged to a finite bidimensional constellation, which includes the multilevel amplitude shift key (ASK), the phase shift key (PSK), quadrature amplitude modulation (QAM), and moderate signal-to-noise ratio signals. The network effectively models a maximum-likelihood receiver for EM MWD signals. In another study, the backpropagation (BP) and serial recurrent networks were applied to the EM MWD noise problem by [PERSON] [5]. His study was based on the utilization of artificial neural network (ANN) receivers in an actual EM MWD communication system. First, a number of varying amounts of post-noise filtering is performed. Then, the filtered response is used as an input signal for the neural network. The performance of the trained network showed improvements over a simple correlation receiver by outperforming it in situations where the noise source was not an additive white Gaussian noise (AWGN). However, like most other techniques, the efficacy of this technique reduces with low SNR. Most importantly, the method was applied to majorly amplitude (and frequency)-based shift key signals. However, the direct application to phase-modulated signals is predictably less viable. On the other hand, [PERSON] [11] focused primarily on adapting the characteristics of the transmitted signal to improve the bit error rate. This is done by adaptively changing the transmission frequency of the BPSK-coded signals to address the problem of EM detection in the presence of non-stationary noise. The technique utilizes the noise power spectral density estimate to establish the spectral nulls at which to concentrate the power spectral density of the transmitted signal by adaptively changing the transmission frequency of the BPSK-coded signal's power spectral density. Improvement was recorded in the ability to detect the transmitted binary phase shift key (BPSK)-modulated EM signals without any action on the decoding portion of the communication system.