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

Li, Zihang; Chen, Xiaohong; Li, Jingye; Zhang, Jian

doi:10.1109/tgrs.2022.3208226

Cited by 4 publications

(1 citation statement)

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“…Multi-task learning aims to empower the network to concurrently learn multiple tasks, enhance the generalization ability by leveraging diverse data from each task to extract more reliable features [38], improve the performances of individual tasks, and mitigate overfitting. Due to the interconnection between seismic data and elastic parameters [39], it is advantageous to integrate multi-task learning into the multi-parameter inversion of seismic data [40][41][42][43][44]. In the multi-task network, multiple tasks utilize a shared representation simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Seismic Elastic Parameter Inversion via a FCRN and GRU Hybrid Network with Multi-Task Learning

Zheng,

Wei,

Yan

et al. 2023

Applied Sciences

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Seismic elastic parameter inversion translates seismic data into subsurface structures and physical properties of formations. Traditional model-based inversion methods have limitations in retrieving complex geological structures. In recent years, deep learning methods have emerged as preferable alternatives. Nevertheless, inverting multiple elastic parameters using neural networks individually is computationally intensive and can lead to overfitting due to a shortage of labeled data in field applications. Multi-task learning can be employed to invert elastic parameters simultaneously. In this work, a hybrid network that leverages the fully convolutional residual network (FCRN) and the gated recurrent unit network (GRU) is designed for the simultaneous inversion of P-wave velocity and density from post-stack seismic data. The FCRN efficiently extracts local information from seismic data, while the GRU captures global dependency over time. To further improve the horizontal continuity and inversion stability, we use a multi-trace to single-trace (M2S) inversion strategy. Consequently, we name our proposed method the M2S multi-task FCRN and GRU hybrid network (M2S-MFCRGRU). Through anti-noise experiments and blind well tests, M2S-MFCRGRU exhibits superior anti-noise performance and generalization ability. Comprehensive experimental inversion results also showcase the excellent lateral continuity, vertical resolution, and stability of the M2S-MFCRGRU inversion results.

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