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Updated: Jul 31, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

A neural network based global traveltime function (GlobeNN).

Mohammad H Taufik¹, Umair Bin Waheed², Tariq A Alkhalifah¹

¹Physical Science and Engineering Division, King Abdullah University of Science and Technology, 23955, Thuwal, Saudi Arabia.

Scientific Reports

|May 3, 2023

Summary

This summary is machine-generated.

We introduce GlobeNN, a neural network that rapidly calculates seismic traveltimes globally. This method efficiently handles data from new technologies like distributed acoustic sensing (DAS) for advanced seismological research.

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Area of Science:

Seismology
Geophysics
Machine Learning

Background:

Global traveltime modeling is crucial for seismology, aiding earthquake localization and velocity inversion.
Distributed Acoustic Sensing (DAS) offers high-density seismic data, overwhelming conventional computation methods.

Purpose of the Study:

Develop GlobeNN, a neural network-based traveltime function for efficient global seismic traveltime computation.
Enable rapid traveltime estimation for millions of receivers from DAS arrays.

Main Methods:

Trained a neural network to estimate traveltimes using the eikonal equation and automatic differentiation.
Utilized P-wave velocity from the GLAD-M25 model for training.
Trained the network on random source-receiver pairs within a global mantle model.

Main Results:

GlobeNN provides rapid global traveltime computations via a single network evaluation.
The trained network acts as an efficient storage for 3-D Earth velocity models.
Demonstrated a new approach for handling large-scale seismic data.

Conclusions:

GlobeNN is an indispensable tool for next-generation seismological advances.
The method efficiently processes vast amounts of seismic data.
Neural networks offer a powerful solution for complex geophysical modeling challenges.