A bunch of really useful codes for earthquake stuff

We use the temporal Epidemic Type Aftershock Sequence (ETAS) model to compute the number of event expected in a time widow and compare it with the number of event actually observed.

A high-performance Python package for handling ETAS DCM(Data Conversion Format) files used in engine calibration tools like INCA, MDA, EHANDBOOK, and CANape.

This is a MATLAB reproduction of the spatial-temporal ETAS model proposed by Teacher Zhuang in 2002, which is more concise and easier to understand

Free, open-source CAN / CAN FD bus analyzer for Windows

The application of a Bayesian method in the spatio-temporal ETAS model, adapted and rewritten based on Hossein's (2022) work, features more concise and efficient commands and is suitable for tasks such as earthquake prediction. Please cite Hossein's article when using it and do not use it for commercial purposes.

This is a method of MCMC sampling for the temporal ETAS model using the Bayesian approach

painel digital intuitivo que permite acompanhar, em tempo real, os níveis dos rios e os dados hidrológicos das Estações de Tratamento de Água (ETAs)

ETAS model considering short-term incompleteness

A Python/C++ earthquake catalog generator using a stationary ETAS model

A Time-Scaled ETAS (Epidemic Type Aftershock Sequence)) Model based on the works of Y. Ogata and J. Zhuang post time-scaling as per the works of J. F. Lawless, T. Duchesne

The main objective of this project is to improve the estimation and prediction of seismic events in Chile with the development of two new methods, the multiresolution-ETAS and DeepQuake, which incorporate spatio-temporal clusterization of seismic events, anisotropic wavelets and exogenous variables.

MATLAB code for identifying the anomalous foreshock phase, based on the ETAS model and its corresponding probability distribution

Global seismic series detection: 4418 earthquakes M>=6.5 (2150 BCE-2026). ETAS validation, tectonic distance, FDR, Monte Carlo. Open research.

I previously wrote a version of the ETAS simulation; the two are very similar but have certain differences, and both can be used for seismicity modeling.

Predict NYC taxi trip duration using a neural network and LightGBM ensemble that learns spatial relationships directly from trip data embeddings.

Predict NYC taxi trip duration using a neural network and LightGBM ensemble based on zone data, timestamps, and passenger counts.

Simulating events using the ETAS model (M-T) and corresponding backward fitting

COM automation for people with deadlines and a hatred of GUIs