Forward Modeling And Signal Processing Method Of Underground Complex Pipeline

Underground pipelines,as the lifeline of modern cities,are providing indispensable material guarantee for people's livelihood.Because of the deficiency and omissions of pipelines data,the unknown pipelines are often destroyed during the engineering construction process such as city construction,oil field piling,which leads to economic loss.To avoid such accidents,engineers need to detect whether there are underground pipelines in the construction region with the help of non-mining detection methods.The Magnetic Anomaly Detection method,as a branch of magnetic prospecting,can be applied effectively to the field of underground pipelines detecting.As for the magnetic anomaly detection of underground pipelines,its target of detection is the anomalous field produced by underground iron pipes under the effect of geomagnetic field.By inverting and handling of this anomalous field,engineers can acquire relatively accurate data of the horizontal and depth of pipelines.Besides,the Magnetic Anomaly Detection method itself is insensitive to external factors such as climate and environment,which soon makes it a research hotspot of underground pipelines detection field.In this paper,the underground pipelines magnetic anomaly detection system is the object of study.The two aspects of fast-forward algorithm implementation and noise magnetic signal processing algorithm are studied in the complex pipeline model.A new strategy of pipeline division is proposed in face with the complex pipeline model.A fast-forward program is designed on the basis of the parallel algorithm excluding the effects of measurement points and the quantity of pipes.A new noise reduction method is found out for the pipeline magnetic analysis signals.Both simulation and measurement experiments are used to analyze and verify the proposed new methods of complex pipeline detection.First of all,the research background and significance of this issue is reviewed in Chapter One Introduction.The development and research status at home and abroad of the related forward modeling and signal processing technology of Magnetic anomaly detection are described in detail.The forward modeling of pipeline magnetic anomaly and the concrete implementation means are finally introduced in this paper.Secondly,as proved in previous literature,both the finite element method(FEM)on the basis of the differential equation and the magnetic dipole reconstruction method on the basis of solving the magnetic dipole induction field can achieve the forward stimulation of magnetic anomaly of underground iron pipelines.Those two methods have different computational principles and they themselves have different characteristics.In order to explicit method selection,a comprehensive comparison between the two common methods needs to be done before the research on magnetic anomaly detection of complex pipeline system is conducted.In Chapter Two,the specific implementation steps of the two magnetic anomaly forward modeling methods are firstly introduced.The computed results,the counting nodes and computational efficiency are compared by the use of stimulation experiment measures,which provides a basis for the selection of the method for subsequent research work.Thirdly,when conducting actual pipe detection,the burial depth of complex underground pipelines is uncertain.Wrong data of burial depth are acquired when the existing magnetic anomaly method is used to conduct inversion.To solve this problem,the related problems are studied in Chapter Four.By use of the existing forward modeling methods,the forward simulation experiments of different models of buried pipes are firstly done.And causes of inversion errors are analyzed.Under the premise of summarizing the causes of the error,the new variable interval segmentation strategy based on the magnetic dipole equivalent principle is proposed according to the comparison results.The subsequent simulations and real experiments have proven that in the premise of guaranteed accuracy,the forward modeling method based on the strategy can reduce the cumulative frequency in computational process and realize steady and fast forward modeling,and ultimately realize the accurate inversion calculation of arbitrary pipeline depth.Moreover,when using magnetic anomaly detecting method to determine the horizontal position and azimuth angle of pipes,magnetic analytic signals are often applied to data processing in order to acquire accurate horizontal locating information.However,the analytic signals are easily affected by noise.The signals without noise processing cannot be used to realize locating.In traditional method,a group of standard orthogonal basis need to be structured in order to realize signal detection.While as for the magnetic analytic signals of pipes,it is hard to acquire the standard orthogonal basis,thus the traditional signal extraction method cannot be applied to signal processing.In Chapter Four,a noise suppression method based on Ensemble Empirical Mode Decomposition(EEMD)is proposed in this paper.This method uses the difference between signal and noise in energy distribution,constructs the standard for the selection of eigenmode function and uses threshold function to obtain data after noise reduction.The numerical simulation experiments and the real experiments prove that this method is suitable for magnetic analytic signal processing and can finally realize the accurate positioning of underground pipeline.Finally,when dealing with forward modeling of complex pipelines detection models,we find that the acquired magnetic anomaly response shows no pseudo dimetric characteristics due to the interaction of magnetic anomaly signals among pipelines.Therefore,one sole measure line of magnetic anomaly response is not applicable for the analysis and location of underground pipelines system.The array type measure points need to be arranged and the whole magnetic anomaly distribution of the measurement zone needs to be evaluated.But the arrangement of array-type measure points,and the increase in the number of pipelines will result in low computational efficiency and the significant increase of the elapsed time of forward calculation,which directly restricts the application effect of the magnetic dipole structure method used in complex pipeline model analysis and inversion.To solve this problem,in Chapter Five,pipeline magnetic anomaly forward modeling procedure based on the GPU parallel algorithm is designed.The design process and the kernel function of the parallel algorithm are described in detail.By comparing the forward calculation results with previous serial procedures,we have proven that the parallel algorithm based on GPU can realize fast forward calculation by ruling out the number of measuring points and the influence of the number of pipeline.The parallel algorithms are used to forward modeling in a typical complex pipe distribution models in the follow-up study.