Anushka Joshi

Indian Institute of Technology Roorkee | University of Roorkee · Department of Electronics and Computer Engineering

In this paper, a scalable end-to-end tree boosting system called XGBoost has been applied for predicting the magnitude of an earthquake from the early part of earthquake waveform data. This model uses the features extracted from the early P wave phase of the records as an input. The model's effectiveness has been verified by using data on earthquak...

A cross-region prediction model named SeisEML (an acronym for Seismological Ensemble Machine Learning) has been developed in this paper to predict the peak ground acceleration (PGA) at a given site during an earthquake. The SeisEML model consists of hybridized models, kernel-based algorithms, tree regression algorithms, and regression algorithms. T...

A new machine learning model, named, EEWPEnsembleStack has been developed for predicting the magnitude of the earthquake from a few seconds of recorded ground motion after the arrival of the P phase. The testing and training dataset consists of 2360 and 591 strong-motion records from central Japan recorded by the Kyoshin Network. Eight parameters t...

Subsurface shear wave velocity plays an important role in designing earthquake-resistant structures. Average shear wave velocity up to 30 m depth, known as Vs30, is used as a common design parameter. Ambient noise data that is generated by ground vibrations due to the passing of vehicles or other passive sources carry an important information about...