Impedance Inversion

Impedance inversion aims to reconstruct material properties from indirect measurements, finding applications in diverse fields like seismic exploration and robotics. Current research focuses on improving inversion accuracy and robustness using hybrid methods that combine deep learning (including convolutional neural networks) with traditional techniques like graph Laplacians or Bayesian inference, often addressing challenges posed by noisy or limited data. These advancements are significant for enhancing the reliability of subsurface imaging in geophysics and enabling more sophisticated control in robotic manipulation, particularly for contact-rich tasks.