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Pertinent Multigate Mixture-of-Experts-Based Prestack Three-Parameter Seismic Inversion | IEEE Journals & Magazine | IEEE Xplore

Pertinent Multigate Mixture-of-Experts-Based Prestack Three-Parameter Seismic Inversion


Abstract:

Seismic inversion is a method used to identify the spatial structure and obtain the physical properties of underground strata by processing seismic data. As a data-driven...Show More

Abstract:

Seismic inversion is a method used to identify the spatial structure and obtain the physical properties of underground strata by processing seismic data. As a data-driven approach, deep learning (DL) is widely used in prestack three-parameter inversion to solve its nonlinearity and ill-posed problems. However, traditional DL-based methods involve the construction of a separate network for each task and thus ignore the correlations between different tasks. Multitask learning (MTL) aims to promote the effectiveness of each task with implicit information amplification by training multiple tasks simultaneously. However, information sharing in conventional MTL may cause negative effects on parallel tasks. To solve this problem, a novel MTL method, called pertinent multigate mixture-of-experts (PMMOE), was proposed for prestack three-parameter inversion. PMMOE introduces the mixture-of-experts (MOE) structure for MTL and creatively divides experts into three special experts and a shared expert. In PMMOE, the input data of different experts are discrepant, enabling the retrieval of different features for different tasks. Experiments revealed that our proposed method has higher accuracy than other methods, and the inversion results of synthetic data and field data further demonstrate the effectiveness of our proposed method.
Article Sequence Number: 5920315
Date of Publication: 21 September 2022

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