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Seismic reflectivity inversion with mixed L1-L2 norm regularization.

Jun Yang1, Ronghuo Dai2, Yang Ma3

  • 1School of Mathematics, Zunyi Normal College, Zunyi, 563006, China. yangjunandy2025@163.com.

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|November 25, 2025
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Summary
This summary is machine-generated.

This study introduces a new mixed-norm regularization for seismic reflectivity inversion (SRI), improving accuracy and resolution in mineral resource exploration. The method enhances small reflectivity components while maintaining sparsity for better underground imaging.

Keywords:
Mineral reservoir predictionMixed L1-L2 normSeismic reflectivity inversionSparse regularization

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Area of Science:

  • Geophysics
  • Seismic data processing
  • Inversion techniques

Background:

  • Seismic reflectivity inversion (SRI) is crucial for mineral resource exploration and reservoir prediction.
  • Conventional sparse-SRI methods face challenges with residual oscillations and poor preservation of small reflectivity components.
  • Existing methods struggle to balance sparsity of large features with smoothness of small variations.

Purpose of the Study:

  • To develop an improved seismic reflectivity inversion (SRI) method addressing limitations of current sparse regularization techniques.
  • To introduce a novel mixed-norm regularization approach for enhanced resolution and accuracy in seismic data interpretation.
  • To improve the estimation of underground mineral resources by refining seismic reflectivity analysis.

Main Methods:

  • Developed a new mixed-norm regularization approach for SRI, incorporating L1-norm and L2-norm of the model.
  • Integrated the L2-norm of model gradients into the mixed-norm for enhanced vertical smoothness, accounting for gradual geological variations.
  • Tuned a weighting parameter to balance L1 and L2 regularization, controlling solution sparsity and preserving small reflectivity components.

Main Results:

  • Synthetic tests demonstrated that mixed L1-L2 norm inversion yields more compact and accurate reflectivity distributions than single-norm methods.
  • The proposed method achieved superior vertical resolution and minimized underestimation of model values compared to conventional SRI.
  • Field tests using West China seismic data confirmed the algorithm's effectiveness, showing enhanced vertical confinement of reflectivity series.

Conclusions:

  • The mixed L1-L2 norm regularization offers a robust framework for seismic reflectivity inversion, overcoming limitations of traditional sparse methods.
  • This approach effectively reconciles the sparsity of major reflectivity features with the smoothness of minor components, leading to more reliable geological interpretations.
  • The enhanced vertical resolution and accuracy make this method highly valuable for mining engineering and mineral reservoir prediction.