Depth- Time Dimension Signal Reconstruction of Transient Electromagnetic Logging using Compressed Sensing

Aiming at the problem of large amount of data in the downhole transient electromagnetic (TEM) array detection system, a compressed sensing-based for depth- time dimension signal reconstruction of TEM logging system is proposed, which can efficiently compress the amount of downhole data. Considering the high sparsity and high similarity of logging signal at different sampling times, the discrete wavelet transform (DWT) sparse transformation combined with the Bernoulli and Toeplitz measurement matrices are used to sparse and compress the depth-time dimension data. Moreover, the restructure of the sparse signals was realized by employing the convex optimization algorithm. Field experiments were conducted, and the results demonstrate that the proposed method can achieve the depth-time dimensional data compression, which can effectively reduce the data volume and improve the transmission efficiency of downhole TEM system.