Forecasting Recurrent Large Earthquakes From Paleoearthquake and Fault Displacement Data

• Published in: Journal of geophysical research. Solid earth Vol. 130; no. 2
• Main Authors: Griffin, Jonathan D., Wang, Ting, Stirling, Mark W., Gerstenberger, Matthew C.
• Format: Journal Article
• Language: English
• Published: 01.02.2025
• Subjects: Bayesian methods; earthquake forecasting; paleoearthquake; slip rates
• DOI: 10.1029/2024JB029671
• ISSN: 2169-9313

Long recurrence intervals of large earthquakes relative to the historical record mean that geological data are often utilized to inform forecasts of future events. Geological data from any particular fault may constrain the timing of past earthquakes (paleoearthquake data), or simply the time period over which a certain amount of fault displacement has occurred due to one or more earthquakes. These data are typically subject to large uncertainties, and available records often only constrain the timing of a few events. Variability in earthquake inter‐event times (aperiodicity) has been observed for many faults, particularly in low seismicity regions, further hampering the utilisation of small data sets for developing forecasts. A challenge for earthquake forecasting therefore concerns how best to utilize all of the limited available data while fully considering uncertainties. Here we present a concise Bayesian model for developing time‐dependent earthquake forecasts from geological data. Using the additive property of the Brownian passage time distribution, we make inference on the model parameters jointly from paleoearthquake and fault displacement data. Monte Carlo Markov Chain methods are used to sample the posterior distribution of model parameters, which is subsequently used to forecast future earthquake probabilities. The method incorporates data uncertainties and does not rely on a priori assumptions of quasiperiodic earthquake recurrence, allowing application in a wide range of tectonic settings. We demonstrate the method using data from two reverse faults in Otago, southern Aotearoa New Zealand, a region in which aperiodic earthquake recurrence has previously been observed. Plain Language Summary Because large earthquakes typically have recurrence intervals that are long relative to the historical record, geological data about past earthquakes are often used to forecast the chance of a future earthquake occurring. Paleoearthquake data constrains the time at which past earthquakes occurred, while fault displacement data constrains how far a fault has slipped over a period of time, usually attributed to more than one earthquake. In this study we present a new statistical method for combining both paleoearthquake and fault displacement data to estimate the probability of future earthquakes on individual faults. Our methods consider all the uncertainties inherent in the data, and allow time‐dependent forecasts to be made based on the timing of the most recent earthquake. Our methods are flexible and do not rely on strong prior beliefs about the earthquake process, allowing application in a wide range of tectonic settings. Key Points A new Bayesian model for forecasting earthquakes from combined paleoearthquake and fault displacement data is presented The Brownian passage time distribution is used to model the distribution of earthquake inter‐event times considering data uncertainties The method does not rely on strong prior beliefs about the earthquake process, allowing application in diverse tectonic settings