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

• URL: http://arxiv.org/abs/2301.09948v3
• Date: Mon, 2 Oct 2023 13:36:43 GMT
• Title: Forecasting the 2016-2017 Central Apennines Earthquake Sequence with a Neural Point Process
• Authors: Samuel Stockman, Daniel J. Lawson, Maximilian J. Werner
• Abstract summary: We investigate whether flexible point process models can be applied to short-term seismicity forecasting. We show how a temporal neural model can forecast earthquakes above a target magnitude threshold.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots [13.390354219940583]
  We investigate the hysteretic response of two types of tendon-actuated continuum robots. We compare three types of neural network modeling approaches with both forward and inverse kinematic mappings.
  arXiv  Detail & Related papers  (2024-04-10T17:04:06Z)
• A Three-regime Model of Network Pruning [47.92525418773768]
  We use temperature-like and load-like parameters to model the impact of neural network (NN) training hyper parameters on pruning performance. A key empirical result we identify is a sharp transition phenomenon: depending on the value of a load-like parameter in the pruned model, increasing the value of a temperature-like parameter in the pre-pruned model may either enhance or impair subsequent pruning performance. Our model reveals that the dichotomous effect of high temperature is associated with transitions between distinct types of global structures in the post-pruned model.
  arXiv  Detail & Related papers  (2023-05-28T08:09:25Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions [30.018173329118184]
  We present neural frailty machine (NFM), a powerful and flexible neural modeling framework for survival regressions. Two concrete models are derived under the framework that extends neural proportional hazard models and non hazard regression models. We conduct experimental evaluations over $6$ benchmark datasets of different scales, showing that the proposed NFM models outperform state-of-the-art survival models in terms of predictive performance.
  arXiv  Detail & Related papers  (2023-03-18T08:15:15Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Representation learning for neural population activity with Neural Data Transformers [3.4376560669160394]
  We introduce the Neural Data Transformer (NDT), a non-recurrent alternative to explicit dynamics models. NDT enables 3.9ms inference, well within the loop time of real-time applications and more than 6 times faster than recurrent baselines.
  arXiv  Detail & Related papers  (2021-08-02T23:36:39Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• The Neural Coding Framework for Learning Generative Models [91.0357317238509]
  We propose a novel neural generative model inspired by the theory of predictive processing in the brain. In a similar way, artificial neurons in our generative model predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality.
  arXiv  Detail & Related papers  (2020-12-07T01:20:38Z)
• A Bayesian Perspective on Training Speed and Model Selection [51.15664724311443]
  We show that a measure of a model's training speed can be used to estimate its marginal likelihood. We verify our results in model selection tasks for linear models and for the infinite-width limit of deep neural networks. Our results suggest a promising new direction towards explaining why neural networks trained with gradient descent are biased towards functions that generalize well.
  arXiv  Detail & Related papers  (2020-10-27T17:56:14Z)
• Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine Learning [0.0]
  We train a neural network to make a forecast of the disturbance storm time index at origin time $t$ with a forecasting horizon of 1 up to 6 hours. Inspection of the model's results with the correlation coefficient and RMSE indicated a performance comparable to the latest publications. A new method is proposed to measure whether two time series are shifted in time with respect to each other.
  arXiv  Detail & Related papers  (2020-06-08T15:14:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.