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This study presents a location method for buried polyethylene (PE) pipelines based on the double-tree complex wavelet cross-correlation delay. Initially, the dual-tree complex wavelet transform (DTCWT) is applied to denoise the acquired signal, followed by extracting the delay time through the cross-correlation function to locate the buried pipeline. A simulation model is established to analyze the peak values of the time-domain signals in both asymmetric and symmetric sensor layouts using COMSOL, determining the relationship between the signal time differences and pipeline positions. Then, an experimental test system is set up, and experiments are carried out under the conditions of asymmetric and symmetrical sensors and different excitation points. The results indicate that the maximum error is 4.6% for asymmetric arrangements and less than 1% for symmetric arrangements. In practical applications, the pipeline’s position can be inferred from the delay time, with higher accuracy observed as the excitation point approaches the sensor. This method addresses the limitations of existing pipeline locating techniques and provides a foundation for the development of pipeline positioning technology.

This study proposes a buried PE gas pipeline positioning method based on the elliptical method of an acoustic signal analysis. The cross-correlation time delay positioning technology is combined with the elliptical equation, forming an effective mechanism for pipeline depth positioning. First, a dual-tree complex wavelet transform is employed to denoise the collected signals, enhancing the quality and accuracy of the data. Subsequently, the cross-correlation function is utilized to extract the delay times between the signals. The obtained delay times are then substituted into the elliptical equation to calculate the depth of the buried PE pipeline. Based on this theoretical framework, a simulation model is established in COMSOL, and positioning simulation analyses are conducted under three different conditions: pipeline depth, relative sensor positions, and distances between sensors and excitation points. The simulation results indicate that a clear correlation exists between the signal delay time and the pipeline position, with simulation errors controlled within 5%, thus validating the theoretical feasibility of the method. To further assess the effectiveness of this approach, an experimental testing system is constructed. The experimental study was carried out under four different conditions: pipeline burial depth, relative sensor positions, distances between sensors and excitation points, and excitation frequencies. The experimental results demonstrate that these factors significantly affect the pipeline depth positioning. The comparison results show that the method has a high accuracy in depth positioning, with experimental errors controlled within 10%. This study proves that accurate positioning of pipeline depth could be achieved by substituting signal delay times into the elliptical equation, thereby validating the method’s feasibility in practical applications. The proposed method effectively addressed the shortcomings of existing pipeline depth positioning technologies, providing important theoretical support and a practical reference for future pipeline positioning research.

Arrival time difference or time delay estimation is critically important for detection of leak location in buried water supply pipes. Because the exact leak locating depends upon the precision of the arrival time difference estimation between leak signals measured by sensors and the propagation speed of the leak-related elastic wave, the research on the estimation of time delay has been one of the key issues in leak locating. The arrival time difference was estimated with the peak time of cross correlation functions of the measured signals. In this study six different window functions, including the basic rectangular, Roth, Wiener, SCOT, PHAT and maximum likelihood windows were applied. Experimental results against an actual buried pipe made of cast iron showed that the introduction of the window functions improved the precision of time delay estimation. In this paper, a new statistical approach, that combines all results of each window function, is suggested for better leak locating. Apart from the experiment, an intensive theoretical analysis in terms of signal processing is described.