DeepAI AI Chat
Log In Sign Up

Forecasting the 2016-2017 Central Apennines Earthquake Sequence with a Neural Point Process

by   Samuel Stockman, et al.

Point processes have been dominant in modeling the evolution of seismicity for decades, with the Epidemic Type Aftershock Sequence (ETAS) model being most popular. Recent advances in machine learning have constructed highly flexible point process models using neural networks to improve upon existing parametric models. We investigate whether these flexible point process models can be applied to short-term seismicity forecasting by extending an existing temporal neural model to the magnitude domain and we show how this model can forecast earthquakes above a target magnitude threshold. We first demonstrate that the neural model can fit synthetic ETAS data, however, requiring less computational time because it is not dependent on the full history of the sequence. By artificially emulating short-term aftershock incompleteness in the synthetic dataset, we find that the neural model outperforms ETAS. Using a new enhanced catalog from the 2016-2017 Central Apennines earthquake sequence, we investigate the predictive skill of ETAS and the neural model with respect to the lowest input magnitude. Constructing multiple forecasting experiments using the Visso, Norcia and Campotosto earthquakes to partition training and testing data, we target M3+ events. We find both models perform similarly at previously explored thresholds (e.g., above M3), but lowering the threshold to M1.2 reduces the performance of ETAS unlike the neural model. We argue that some of these gains are due to the neural model's ability to handle incomplete data. The robustness to missing data and speed to train the neural model present it as an encouraging competitor in earthquake forecasting.


page 7

page 13

page 17

page 40


Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting

Despite the advances in the field of solar energy, improvements of solar...

Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting

In this paper, we propose a new short-term load forecasting (STLF) model...

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Modeling and forecasting forward citations to a patent is a central task...

Meta Temporal Point Processes

A temporal point process (TPP) is a stochastic process where its realiza...

Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress

Earthquake forecasting and prediction have long and in some cases sordid...

Novel Compositional Data's Grey Model for Structurally Forecasting Arctic Crude Oil Import

The reserve of crude oil in the Arctic area is abundant. Ice melting is ...

Code Repositories