The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …

We propose a new approach to the solution of the wave propagation and full waveform
inversions (FWIs) based on a recent advance in deep learning called physics‐informed …

Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …

The eikonal equation is utilized across a wide spectrum of science and engineering
disciplines. In seismology, it regulates seismic wave traveltimes needed for applications like …

To simulate seismic wavefields with a frequency-domain wave equation, conventional
numerical methods must solve the equation sequentially to obtain the wavefields for different …

Machine learned tasks on seismic data are often trained sequentially and separately, even
though they utilize the same features (ie, geometrical) of the data. We present StorSeismic …

Seismic inversion is a significant tool for exploring the structure and characteristics of the
underground. However, the conventional inversion strategy strongly depends on the initial …

The movement and deformation of the Earth's crust and upper mantle provide critical
insights into the evolution of earthquake processes and future earthquake potentials. Crustal …

Frequency-domain simulation of seismic waves plays an important role in seismic inversion,
but it remains challenging in large models. The recently proposed physics-informed neural …

Full-waveform inversion (FWI) is a popularly used high-resolution seismic inversion method.
It relies on the measure of the misfit between observed data and predicted data. Due to the …