Reservoir elastic parameters play an important role in resource exploration; however, the band-limited characteristics of seismic data and the ill-posed nature of seismic inversion significantly affect inversion accuracy. To alleviate this problem, a high -reso-lution prediction method for reservoir elastic parameters based on the progressive multitask learning network (PMLN) is proposed. Our network consists of three parts: network 1 for low-frequency extension (LFE), network 2 for reservoir parameter inversion, and network 3 for image superresolution (SR). Taking the seismic fre-quency band as the link, network 1 is first used to predict the low -frequency information of seismic data. Then, the nonlinear map-ping relationship between the high-pass-filtered seismic data (and its envelope) and full-frequency seismic data is established. Sec-ond, network 2 directly predicts the reservoir elastic parameters using the seismic data after LFE. Finally, the SR of the inversion results is achieved from the image perspective based on network 3. The three networks have a progressive relationship and can share network features, which is beneficial for improving com-puting efficiency. As the features extracted by the network re-present different contributions to the prediction target, a channel attention mechanism is introduced. Furthermore, the loss function of network 2 is improved using dip constraints to obtain high-precision reservoir parameters. Synthetic and field data analyses find that all three networks are competent for their re-spective tasks, and the PMLN can obtain high-resolution predic-tion results of reservoir elastic parameters. Compared with traditional full-waveform inversion, the PMLN effectively im-proves prediction accuracy. Therefore, the PMLN is expected to become a powerful tool for predicting the elastic parameters of reservoirs.
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