Citizen seismology helps decipher the 2021 Haiti earthquake

Area of Science:

• Earth Sciences
• Seismology
• Geophysics

Background:

• The 2021 M(w) 7.2 Nippes earthquake in Haiti occurred in a seismically active fault zone, near the 2010 M(w) 7.0 event.
• Field access was restricted due to insecurity, and the national seismometer network was non-operational during the 2021 earthquake.
• A pre-existing network of citizen seismometers, installed in 2019, offered a vital alternative data source.

Purpose of the Study:

• To assess the utility of citizen seismometer data for understanding earthquake mechanisms and aftershock sequences.
• To evaluate the effectiveness of citizen science in seismic monitoring during crises.
• To test machine learning models for aftershock forecasting using citizen-science-derived data.

Main Methods:

• Deployment and utilization of a citizen seismometer network for real-time data acquisition.
• Analysis of near-field seismic data to identify aftershock clusters.
• Comparison of citizen seismometer data with conventional seismological and geodetic data inversions.
• Application of machine learning algorithms to forecast aftershocks based on citizen seismometer data.

Main Results:

• Citizen seismometer data successfully defined two distinct aftershock clusters.
• These clusters spatially correlated with areas of coseismic slip identified through traditional methods.
• Machine learning models using data from the nearest citizen seismometer achieved forecasting accuracy comparable to network-derived catalogs.

Conclusions:

• Citizen science networks can provide critical, real-time seismic data during major earthquakes, especially when conventional infrastructure is compromised.
• Citizen seismometer data is valuable for rapid analysis of earthquake mechanisms and aftershock monitoring.
• The integration of citizen science and machine learning offers a powerful approach for improving earthquake preparedness and response.