To address the challenge of separating pipeline leakage signals from complex background noise and the difficulty of extracting small leakage features, a denoising method that uses the RIME to improve VMD is proposed. This method calculates the Bubble entropy value of the denoised signal for feature extraction, and then identifies the small leakage condition of the pipeline using an improved ELM optimized by RIME. First, RIME is used to improve the selection of key parameters for VMD, achieving adaptive decomposition. The mutual information entropy value between the IMFs generated by VMD is used as the fitness function value in the parameter optimization of the Rime algorithm, establishing a denoising method for water pipeline leakage signals based on RIME-VMD. Experiments have shown that compared to other heuristic optimization algorithms, the RIME-VMD method has the highest SNR of 23.922 dB, indicating that the reconstructed signal filtered by this method has the highest proportion of effective signal. The RIME-VMD method also has the lowest MAE and MSE, at 0.187 and 0.056 respectively, indicating that the reconstructed signal contains the least noise. Second, a method for extracting features from pipeline micro leakage signals using Bubble entropy is proposed, and these features are input into a model with ELM parameters optimized by RIME for pipeline leakage detection. Ultimately, the classification accuracy of the RIME-ELM model reached 95.71%, which is a 37.4% improvement compared to directly inputting fault feature vectors into the ELM, verifying the effectiveness of the proposed method.