On the Use of High-Resolution and Deep-Learning Seismic Catalogs for Short-Term Earthquake Forecasts: Potential...

Area of Science:

• Seismology
• Earthquake forecasting
• Geophysics

Background:

• Enhanced earthquake catalogs offer detailed seismic sequence insights.
• Real-time data availability is crucial for timely earthquake forecasting.

Purpose of the Study:

• To assess if enhanced seismic catalogs improve short-term earthquake forecasting.
• To evaluate the impact of catalog completeness and resolution on forecasting models.

Main Methods:

• Utilized three enhanced catalogs from the 2016-2017 Central Italy sequence.
• Employed Coulomb Rate-and-State (CRS) and Epidemic-Type Aftershock Sequence (ETAS) models.
• Compared forecast performance against real-time catalog benchmarks using likelihood metrics.

Main Results:

• Incorporating small magnitude events from enhanced catalogs benefited forecasts.
• Models using enhanced catalogs did not significantly outperform near real-time benchmarks.
• Spatial discretizations and catalog parameter differences impacted forecast evaluation.

Conclusions:

• Enhanced catalogs offer potential for improved earthquake forecasting.
• Model design and evaluation need refinement to leverage next-generation seismic data.
• Addressing spatial resolution and catalog consistency is key for future forecasting accuracy.