A modified wavelet transform domain adaptive FIR filtering algorithm for removing the SPN in the MFL data

With the widespread application and fast development of gas and oil pipeline network, the pipeline inspection technology has been used more extensively. The magnetic flux leakage (MFL) method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise (SPN). SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. This paper presents a modified wavelet transform domain adaptive FIR filtering algorithm for removing the SPN in the MFL data. The advantage of the proposed algorithm is that it converges faster than the time domain adaptive SPN filtering algorithm. Results from application of the modified algorithm to the MFL data from field tests show that the modified algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.