Can temporally and spatially varying b-values improve earthquake forecasts? Insights from a machine-learning-enhanced catalog in central Italy.

The b-value of the Gutenberg-Richter law is crucial for modern hazard models and seismicity forecasting. It quantifies the relative frequency of small earthquakes vs. infrequent large events. A growing number of studies suggest that the b-value changes with factors such as time (Gulia et al., 2018), differential stress (Scholz, 2015), and thermal regime (Nishikawa and Ide, 2014). However, translating the knowledge of such b-value variation into measurable  improvements of earthquake forecasting capabilities has not been convincingly achieved yet (e.g., Iturrieta et al., 2024).

In this work, we investigate whether a temporally changing b-value can improve our ability to forecast future magnitudes. For this, we implement a method to estimate temporally and spatially changing b-values, with a given time- and length-scale, together with a measure of how strong the variation is (b-significant, Mirwald et al., 2024). Further, we develop a method to evaluate the information gain (IG) that is more robust in the presence of short-term aftershock incompleteness.

We apply these methods to the 2016-2017 central Italy earthquake sequence, using a machine-learning-enhanced earthquake catalog containing >900k events (Tan et al., 2021). Specifically, we first estimate the optimal temporal, spatial, and combined spatiotemporal scales for forecasting future seismicity using the first half of the dataset. Using the second half of the dataset, we then assess pseudoprospectively if a varying b-value, estimated with the parameters obtained in the first step , results in a positive information gain compared to a stationary reference model.

References

Gulia, L., Rinaldi, A.P., Tormann, T., Vannucci, G., Enescu, B., Wiemer, S., 2018. The Effect of a Mainshock on the Size Distribution of the Aftershocks. Geophysical Research Letters 45, 13,277-13,287. https://doi.org/10.1029/2018GL080619

Iturrieta, P., Bayona, J.A., Werner, M.J., Schorlemmer, D., Taroni, M., Falcone, G., Cotton, F., Khawaja, A.M., Savran, W.H., Marzocchi, W., 2024. Evaluation of a Decade-Long Prospective Earthquake Forecasting Experiment in Italy. Seismological Research Letters 95, 3174–3191. https://doi.org/10.1785/0220230247

Mirwald, A., Mizrahi, L., Wiemer, S., 2024. How to b -Significant When Analyzing b -Value Variations. Seismological Research Letters. https://doi.org/10.1785/0220240190

Nishikawa, T., Ide, S., 2014. Earthquake size distribution in subduction zones linked to slab buoyancy. Nature Geosci 7, 904–908. https://doi.org/10.1038/ngeo2279

Scholz, C.H., 2015. On the stress dependence of the earthquake b value. Geophysical Research Letters 42, 1399–1402. https://doi.org/10.1002/2014GL062863

Tan, Y.J., Waldhauser, F., Ellsworth, W.L., Zhang, M., Zhu, W., Michele, M., Chiaraluce, L., Beroza, G.C., Segou, M., 2021. Machine-Learning-Based High-Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated during the 2016–2017 Central Italy Sequence. The Seismic Record 1, 11–19. https://doi.org/10.1785/0320210001