Multiresolution Recognition Algorithm for Abnormal Patterns of Continuous Gravity Observation

Based on à trous algorithm, we propose a multiresolution algorithm, using the energy of high frequency domain of wavelet decomposition as the frequency index, combining with amplitude, to search for the abnormal pattern in the signal and its wavelet decomposition results. Simulated data and actual superconducting gravimeter(SG) results verify the effectiveness of this algorithm. Results show that the algorithm can accurately identify anomalies in the simulated data with noise.Using this algorithm when processing of continuous gravity observation data can improving the quality of China gravity network observation data with significance in earthquake prediction and other applications. We found 27-minute anomalies after analyzing three SG datasets for Lhasa and Wuhan after the 2015 Nepal earthquake, with a similarity of over 90%. The reason of this phenomenon still needs further research in future.