SEISMIC FIRST ARRIVAL TRAVELTIME INVERSION HARNESSING PHYSICS INFORMED NEURAL NETWORKS

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2022-2-11
Yıldırım, İsa Eren
In Seismic prospecting, huge amounts of data are collected and processed to infer the structural and lithological composition of the subsurface. The key step in this procedure is velocity model building. First arrival traveltime inversion is one of the velocity model building tools commonly used for predicting near-surface velocity structures in seismic exploration. Conventionally, the inversion is carried out using ray-based methods or gradient-based algorithms. Though the gradient-based algorithms find the gradient that is needed to update the model parameters without requiring ray tracing, it can be computationally demanding. On the other hand, despite its robustness and efficiency ray-based methods suffer from complex regions as the ray theory relies on the high-frequency approximation. Instead of using these approaches for a traveltime inversion problem, we propose a machine learning based approach, specifically harnessing the physics informed neural networks exploiting the mathematical model represented by the eikonal equation to estimate the near-surface subsurface velocities. Through synthetic tests and the application of real data, we show the reliability of the physics informed machine learning based traveltime inversion which can be a potential alternative tool to the traditional tomography frameworks.

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Citation Formats
İ. E. Yıldırım, “SEISMIC FIRST ARRIVAL TRAVELTIME INVERSION HARNESSING PHYSICS INFORMED NEURAL NETWORKS,” M.S. - Master of Science, Middle East Technical University, 2022.