To improve the signal-to-noise ratio (SNR) of vibration location information in Φ-OTDR system, a variable step size normalized least mean square (VSS-NLMS) denoising method is proposed in the article. First, the mathematical principle of VSS-NLMS method is established. The method can adaptively filter the input signal and adjust the parameters. It can also update the step factor adaptively. The simulation results show that the SNR of this method is improved from -1.16 dB to 12.01 dB. Second, a heterodyne coherent optical system is built. After the detected beat signal is demodulated by I/Q quadrature demodulation, the amplitude signal and phase signal of the Rayleigh backscattered light are acquired. The amplitude signal determines the location information and the phase signal determines the vibration signal. Finally, the wavelet denoising method, the empirical mode decomposition soft threshold (EMD-soft) denoising method and the variational mode decomposition nonlinear wavelet threshold (VMD-NWT) denoising method are compared with the VSS-NLMS denoising method in the experiment. The experimental results show that the location information SNR of the VSS-NLMS denoising method is improved to 59.31 dB, 46.81 dB, 50.14 dB, 34.00 dB and 67.09 dB for 5 groups of 100 Hz - 500 Hz vibration events at the location of the 10.14 km sensing fiber. At the same time, the vibration signals of 5 groups are demodulated. This method has laid a foundation for the application of oil and gas pipelines.

We have evaluated the ensemble empirical mode decomposition (EEMD) and stacking model for borehole seismic-data denoising. The borehole records collected by distributed acoustic sensing (DAS) technology have multitype noise contamination, and it is difficult to attenuate these noises while recovering the seismic waves well. We first perform EEMD on the seismic data to obtain the signal-to-noise modal components, then extract the time and frequency information of the decomposed modes using six feature factors, and finally introduce an ensemble learning method to classify the acquired modal features effectively. Stacking is the ensemble learning technique we used in our study. This technique integrates several diverse basic ensemble models using the meta-learning strategy and constructs a highly integrated framework with superior performance and good generalization. In addition, the basic ensemble models consist of many decision tree classifiers following two different ideas of parallelization and serialization. The feature extraction process provides sufficient DAS feature data for the training process of the framework. Synthetic and real experimental results demonstrate that the stacking integration framework effectively separates the signal-to-noise modal features of the borehole DAS records. Furthermore, the EEMD-stacking method performs better than wavelet transform, intrinsic time-scale decomposition, robust principal component analysis, k-means singular value decomposition, and median filtering on the denoising task.

In recent years, distributed acoustic sensing (DAS) technology has attracted much attention and has been applied to vertical seismic profile (VSP). It has promoted the development of land exploration toward deep exploration and fine tectonic analysis. However, the acquired DAS VSP data are often disturbed by various types of noise generated by the environment and instruments. They exhibit quite different characteristics from traditional exploration noise, hindering the further development and application of DAS technology in the field of VSP. Frequency information plays an important role in the judgment and reduction of seismic noise. Since the noise and signals in DAS VSP data often have complex time-frequency relationships, it is difficult for traditional frequency-based methods to effectively separate them. Current deep learning DAS data processing methods did not consider the important frequency-domain features and affect the integrity of data feature extraction. This article presents a new deep learning algorithm for DAS VSP data processing, cosine spectral association network (CSANet). The novel algorithm considers both the original time-offset and frequency information to achieve better DAS weak signal recognition and recovery. Moreover, a feature fusion module is designed to make sure the entire network to better utilize the composite information. The experimental results show that our CSANet can obtain high-precision DAS VSP weak signal recognition and recovery results with strong practical application value.

Aiming at the frequency drift of the laser and acousto-optic modulator in the traditional phase-sensitive optical time domain reflectometric (Φ-OTDR), a Φ-OTDR system based on optical synchronous heterodyne is proposed and demonstrated. The method of optical synchronization heterodyne is used to follow the beat frequency signal to eliminate the interference of frequency shift fluctuation in the phase information and the ubiquitous residual frequency before the optical path heterodyne. The signal-to-noise ratio (SNR) is more than 31.4 dB on a 10 km sensing optical fiber with a probe light pulses of 5 kHz repetition rate and 100 ns pulse width. The error between the demodulated signal amplitude and the actual signal amplitude is less than 1%, and the maximum harmonic amplitude is about 28.3 dB less than the actual signal amplitude. While improving the real-time performance, this research greatly improves the demodulation characteristics of the system, which is of great significance to the advancement of the practical process of the phase-sensitive optical time domain reflectometric system.

Aiming at the quadrature unbalance phenomenon in the phase-sensitive optical time domain reflectometry ( $\Phi $ -OTDR) system, a method of quadrature unbalance compensation for the $\Phi $ -OTDR system based on the recursive least square (RLS) algorithm is studied and demonstrated to improve the accuracy of the demodulated phase waveform. Firstly, the DC residual term of the quadrature imbalance is eliminated by calculating the mean value of the envelope. Further, the cyclic symmetry of the AC orthogonal unbalance is tracked to complete parameter estimation, and then the orthogonal unbalance correction is completed through an adaptive filtering method. The experimental results show that under the condition of the detection light pulse with a repetition frequency of 5 KHz and a pulse width of 100 ns, the RLS compensation algorithm greatly improves the demodulation characteristics of the system and suppresses the quadrature imbalance. It is of great significance to the practical process of the $\Phi $ -OTDR system.