Impact of climatic anomalies and reservoir induced seismicity on earthquake generation using Federated Learning

Abstract

In this article, impact of climatic anomalies and artificial hydraulic loading on earthquake generation has been studied using federated learning (FL) technique and a model for the prediction of earthquake has been proposed. Federated Learning being one of the most recent techniques of machine learning (ML) guarantees that the proposed model possesses the intrinsic ability to handle all concerns related to data involving data privacy, data availability, data security, and network latency glitches involved in earthquake prediction by restricting data transmission to the network during different stages of model training. The main objective of this study is to determine the impact of artificial stresses and climatic anomalies on increase and decrease in regional seismicity. Experimental verification of proposed model has been carried out within 100 km radial area from 34.708o N, 72.5478o E in Western Himalayan region. Regional data of atmospheric temperature, air pressure, rainfall, water level of reservoir and seismicity has been collected on hourly bases from 1985 till 2022. In this research, four client stations at different points within the selected area have been established to train local models by calculating time lag correlation between multiple data parameters. These local models are transmitted to central server where global model is trained for generating earthquake alert with ten days lead time alarming a specific client that reported high correlation among all selected parameters about expected earthquake.