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Inversion of Rayleigh Wave Dispersion Curves via Long Short-Term Memory Combined with Particle Swarm Optimization.

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This study introduces a novel PSO-LSTM method for inverting surface wave dispersion curves, improving shear-wave velocity model accuracy. The new approach enhances subsurface stratigraphic information retrieval, outperforming traditional methods.

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Area of Science:

  • Geophysics
  • Seismology
  • Machine Learning Applications in Earth Sciences

Background:

  • Surface wave exploration relies on inverting dispersion curves to determine subsurface shear-wave velocity models and stratigraphy.
  • Traditional inversion methods, including local search and global optimization algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), face challenges with precision, local optima, and slow convergence.
  • Deep learning models offer strong nonlinear mapping capabilities suitable for complex inversion problems.

Purpose of the Study:

  • To propose and evaluate a novel PSO-optimized Long Short-Term Memory (LSTM) network (PSO-LSTM) for improved inversion of surface wave dispersion curves.
  • To enhance the accuracy and robustness of shear-wave velocity modeling and subsurface stratigraphic interpretation.
  • To compare the performance of the PSO-LSTM method against standalone LSTM and PSO algorithms.

Main Methods:

  • Development of a PSO-LSTM model integrating LSTM networks with PSO for optimizing network structure and parameters.
  • Testing the PSO-LSTM model on two theoretical geological models (Model A: noise-free, Model B: noisy data).
  • Comparative analysis using noise-free data (Model A) against standard LSTM and PSO methods, evaluating relative errors and standard deviations.

Main Results:

  • PSO-LSTM achieved significantly lower maximum relative errors (2.05% for Model A, 2.09% for Model B) compared to LSTM (5.85%) and PSO (20.76%) on Model A.
  • PSO-LSTM demonstrated lower maximum standard deviations (1.23 for Model A, 3.87 for Model B) than LSTM (1.97) and PSO (57.37) on Model A, indicating higher precision.
  • The PSO-LSTM model proved robust with noisy data (Model B) and successfully inverted real-world dispersion data from Wyoming, USA, validating its practical applicability.

Conclusions:

  • The PSO-LSTM method significantly improves the accuracy and precision of dispersion curve inversion compared to traditional LSTM and PSO techniques.
  • PSO optimization enhances LSTM network performance, leading to more reliable shear-wave velocity models and subsurface stratigraphic information.
  • The PSO-LSTM approach is a robust and effective tool for quantitative interpretation of Rayleigh wave dispersion curves in geophysical exploration.