# Ground motion prediction at gravitational wave observatories using archival seismic data

## TL;DR

This paper presents a machine learning approach to predict ground motion at gravitational wave observatories using archival seismic data, improving operational stability during earthquakes.

## Contribution

It introduces a novel ML-based prediction scheme that significantly enhances ground motion forecasting accuracy compared to previous models.

## Key findings

- Error scatter reduced from 5x to 2.5x
- Enables control adjustments to prevent lock loss
- Applicable to seismic hazard early warning systems

## Abstract

Gravitational wave observatories have always been affected by tele-seismic earthquakes leading to a decrease in duty cycle and coincident observation time. In this analysis, we leverage the power of machine learning algorithms and archival seismic data to predict the ground motion and the state of the gravitational wave interferometer during the event of an earthquake. We demonstrate improvement from a factor of 5 to a factor of 2.5 in scatter of the error in the predicted ground velocity over a previous model fitting based approach. The level of accuracy achieved with this scheme makes it possible to switch control configuration during periods of excessive ground motion thus preventing the interferometer from losing lock. To further assess the accuracy and utility of our approach, we use IRIS seismic network data and obtain similar levels of agreement between the estimates and the measured amplitudes. The performance indicates that such an archival or prediction scheme can be extended beyond the realm of gravitational wave detector sites for hazard-based early warning alerts.