## 1 Introduction  
Long-distance pipelines are commonly used in the oil and gas industries. Pipeline defects due to corrosion, natural damage, third-party damage, pipeline materials, and other factors damage the integrity of pipelines, resulting in a reduced service period for each pipeline, and this can even cause a variety of accidents, affecting normal pipeline operation. Therefore, to eliminate the risks to safety and the environment caused by defects, it is necessary to detect these defects using pipeline signals. In the process of pipeline defect detection, when the original defect signal is directly analyzed, high levels of background noise often affect the detection performance and seriously interfere with the final detection result [1]. The detection of pipeline signals is often affected by noise, which reduces the accuracy of pipeline defect diagnosis [2]. Therefore, a precise noise reduction method is needed to suppress noise in pipeline signals and ensure the safe operation of pipelines, which is important in practice. However, the natural gas in a gas pipeline contains impurities and corrosive substances and is affected by external forces, which will continuously wash the pipe body and corrode the pipe wall, resulting in the pipeline pressure distribution being uneven, and pipe wall cracks will appear. When the pipeline has defects, it is easy for natural gas leakage to cause environmental pollution, major accidents, and huge losses of life and property. In order to prevent the occurrence of unsafe pipeline accidents, it is necessary to carry out the comprehensive and accurate detection of pipeline defects. Although existing non-destructive testing technology has many advantages, the pipeline will be disturbed by a lot of noise signals in the process of collecting defect signals. If the pipeline signal is not denoised, effective pipeline defect information cannot be obtained directly from the signal. Therefore, it is necessary to propose an effective signal processing method to reduce the noise of pipeline defect signals, so as to improve the emergency management of pipelines. This is of great significance for the identification, quantification, disaster prevention, and emergency management of pipeline defects [3]. Natural gas pipelines are limited by long laying times, design and operation standards, changes in ground environment facilities, and other factors, and are prone to various defects, leading to pipeline leakage. In order to detect pipeline defects in advance and prevent pipeline disasters, pipeline noise reduction should be carried out before pipeline inspection. However, the traditional pipeline noise reduction method is relatively simple, has poor accuracy, and cannot effectively improve the emergency intensity and emergency level of pipeline operation and maintenance management. However, with the development of science and technology, artificial intelligence is gradually being applied to the noise reduction in natural gas pipelines. For example, deep learning and machine learning methods are applied to the pipeline field to reduce the noise of pipeline signals and assist emergency management departments in making decisions and formulating emergency plans and on-site processing programs [4]. Scholars have carried out a lot of research on noise reduction in the field of transportation [5; 6]. Especially in the field of pipeline transportation, scholars have made a lot of contributions to the noise reduction method of pipeline signals. The main traditional noise reduction algorithms are Fourier transform, Wiener filtering, and wavelet transform [7]. Fourier transform is a transform that maps time-domain data to frequency-domain data. Wiener filtering is only suitable for stationary linear signals. Although wavelet transform is suitable for non-stationary signals, it lacks adaptability [8; 9]. Many researchers and experts have proposed different approaches to noise reduction.