Seismically Informed Reference Models Enhance AI‐Based Earthquake Prediction Systems

Abstract

AbstractGiven the robust nonlinear regression capabilities of Artificial Intelligence (AI) technology, its commendable performance in numerous geophysical tasks is expected. Yet, AI technology suffers from (a) its “black box” nature and (b) the fact that some complicated artificial neural networks (ANNs) claiming superior performance do not surpass some simple geophysical models that clearly describe the underlying physical processes. Numerous reports rely on standard machine learning metrics, often using a spatially uniform Poisson (SUP) distribution as their reference. A good performance just means that the artificial neural network (ANN) outperforms this basic reference, potentially offering little novelty to the scientific community. Worse, this can lead to spurious inference. We demonstrate this by using the monthly average human‐made Nighttime Light Map and the cumulative energy of earthquakes in various space‐time units as inputs for an Long short‐term memory model. The goal is to predict earthquakes with a magnitude of M ≥ 5.0 across the entire Chinese Mainland. With the SUP reference model, the ANN concludes that human‐made Nighttime Light possesses substantial earthquake prediction capability. This is evidently flawed reasoning. We show that this stems from the poor reference model and this spurious inference disappears when using a better benchmark consisting of a spatially varying Poisson (SVP) model informed from statistical seismology. This is implemented by weighting the punishments/rewards of our ANN associated with failed/successful predictions by prior probabilities provided by the stronger SVP model. Scores obtained with the time‐space Molchan diagram demonstrate the strong performance improvement obtained by training ANN with a better reference model.