Year: 2025
Type: article
Abstract: High‐resolution seismic models of the Earth's lithosphere are critical for understanding its structure and evolution, yet current global models lack the details that can be provided by ambient noise data. A primary bottleneck is reliably extracting phase velocities from the vast, often noisy data sets produced by ambient noise cross‐correlations (noise correlation functions). To address this, we introduce AkiNet , a Physics‐Informed Neural Network designed as a “zero‐shot” solver for this difficult inverse problem. Unlike supervised learning approaches, AkiNet operates without pre‐training or labeled data by directly embedding the governing physics of wave propagation (Aki's theoretical framework) into its loss function. When compared against a modern waveform‐fitting algorithm, AkiNet yields more reliable dispersion estimates, particularly for Love wave data with low signal‐to‐noise ratios. AkiNet provides a robust and scalable tool that presents a feasible pathway toward the construction of a global, high‐resolution, and noise‐derived dispersion model.
DOI: 10.1029/2025jh000932
Authors: Siyu Xue, Tolulope Olugboji
Institutions: Cornell University, University of Rochester
Language: English
Topic: Seismic Waves and Analysis
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W4416824734