This paper presents a geomagnetic detection method for pipeline defects using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet energy product (WEP) – Teager energy operator (TEO), which improves detection accuracy and defect identification ability as encountering strong inference noise. The measured signal is first subtly decomposed via CEEMDAN into a series of intrinsic mode functions (IMFs), which are then distinguished by the Hurst exponent to reconstruct the filtered signal. Subsequently, the scale signals are obtained by using gradient calculation and discrete wavelet transform and are then fused by using WEP. Finally, TEO is implemented to enhance defect signal amplitude, completing geomagnetic detection of pipeline defects. The simulation results created by magnetic dipole in a noisy environment, indoor experiment results and field testing results certify that the proposed method outperforms ensemble empirical mode decomposition (EEMD)-gradient, EEMD-WEP-TEO, CEEMDAN-gradient in terms of detection deviation, peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR).