Joint physical-model- and multi-source-data-driven elastic full waveform inversion

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ID: 321340
2026
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Abstract
Summary Elastic full waveform inversion (EFWI) provides high-resolution subsurface P- and S-wave velocity models essential for formation lithology and fluid characterization. However, its strong nonlinearity makes it highly susceptible to the local-minima entrapment when low-frequency seismic data or an accurate initial model is unavailable. Furthermore, the coupled sensitivity of multi-component seismic data to distinct elastic parameters introduces a multi-parameter crosstalk effect that further degrades inversion accuracy. Although well-log data contain rich, inherently decoupled prior information of subsurface elastic parameters, a fundamental dimensional mismatch between 1D well logs and the 2D/3D inversion domain prevents their direct integration into conventional EFWI. To overcome these limitations, a joint physical-model- and multi-source-data-driven (JPM-MSDD) EFWI framework is proposed. The correlation between common mid-point (CMP) gathers and 1D vertical velocity profiles is exploited to resolve the dimensional mismatch, enabling well-log data to directly serve as training labels. A neural network is trained to map consecutive adjacent CMP gathers of multi-component seismic data to 1D P- and S-wave velocity profiles under a semi-supervised learning scheme, in which well-side CMP–log pairs form the labeled dataset while model-driven EFWI furnishes physics-consistent pseudo-labels for unlabeled gathers. In this way, the proposed method is able to reduce the reliance on large labeled datasets and enhance the physical interpretation of inversion results. To further reduce the nonlinearity of EFWI, the spatially related information of seismic data is incorporated through position embedding on seismic traces. By integrating multi-source data, including well-log data, multi-component seismic data, and spatial information of seismic traces, into EFWI process, even in the absence of low-frequency components of seismic data, a very good low-wavenumber model can be inverted for conventional model-driven EFWI to prevent the inversion from falling into local minima. Additionally, the proposed method can mitigate the crosstalk of multi-parameters to a certain extent. The synthetic and field data examples demonstrate that the proposed joint physical-model- and multi-source-data-driven EFWI can invert vp and vs models effectively.
Reference Key
openalex_W7168640841 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shuliang Wu, Jianhua Geng
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag264
URL
https://doi.org/10.1093/gji/ggag264
Keywords Keywords not found

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