Open Source
Elastic Wave Imaging via Kirchhoff Migration: Resolution Analysis and Inclusion Identification
arXiv cs.NA0 views
arXiv:2611.09137v1 Announce Type: new Abstract: We consider the inverse problem of determining the location, orientation, shape, and material parameters of a small elastic inclusion embedded in a homogeneous medium from full-aperture measurements of the scattered field. Using a small-volume expansion, we derive explicit leading-order expressions for four Kirchhoff migration imaging functionals, denoted by PP, PS, SP, and SS, corresponding to the four combinations of pressure (P) and shear (S) waves used for receiver- and source-side backpropagation. The resulting point-spread functions quantitatively characterize the resolution of each mode and show that the SS mode achieves the highest spatial resolution. For an elliptic inclusion, we show that the PP mode uniquely determines the elastic moment tensor (EMT) within the leading-order asymptotic model, thereby enabling the reconstruction of the material and geometric parameters of the inclusion. For a general small scatterer, the reconstruction may instead be interpreted as an effective elliptic representation determined by its leading-order EMT. For a disk, the parameter-identification problem is underdetermined; nevertheless, explicit upper and lower bounds on its volume are derived. We further analyze the model error arising from neglected cross-mode contributions when the distance from the center of the measurement configuration is not sufficiently large relative to the elastic wavelengths. Numerical experiments support the theoretical predictions and demonstrate the effectiveness of the proposed imaging and reconstruction methods.
Source:https://arxiv.org/abs/2611.09137PreviousStrong convergence rates for numerical approximations of the stochastic Burgers equation with additive rough noiseNextA Structured-Surrogate Double-Model Approach to Regularization Parameter Selection
Related posts
Fun things to do with your VM/370 CE machine – Fun with big computers
0Hacker News
Prime Agent: an open source coding and research agent for long-running work
0dev.to Rust
Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization
0arXiv stat.ML
Fine-Tuned Language Models Exhibit Improved Information Conveyance
0arXiv stat.ML
Computer-Use Agents Are Embodied Generalists
0arXiv cs.RO
Grasp-TAGS: Task-Aware Grasp Selection via Critic Distillation for Prehensile Loco-Manipulation
0arXiv cs.RO