Induced seismicity: a global phenomenon with special relevance to the Dutch subsurface

Pluymakers, Anne; Muntendam-Bos, A.G.; Niemeijer, A. R.

doi:10.1017/njg.2023.2

Cited by 3 publications

References 18 publications

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

Tehseen,

Uzma Omer,

Maham Mehr Awan

et al. 2024

VFAST trans. softw. eng.

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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.

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

Tehseen,

Uzma Omer,

Maham Mehr Awan

et al. 2024

VFAST trans. softw. eng.

View full text Add to dashboard Cite

show abstract

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

Tehseen,

Omer,

Mehr

et al. 2023

Preprint

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In this article, earthquake forecasting model using federated learning (FL) technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of earthquakes by prohibiting data to be transferred over the network for model training. The main objective of this study is to determine the impact of artificial stresses and climatic anomalies on regional seismicity. Experimental verification 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, towards a specific client where high correlation among all parameters have been reported.

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A Finite Volume Framework for the Fully Implicit Thermal-Hydro-Mechanical-Compositional Modeling in Subsurface Applications

Novikov,

Saifullin,

Hajibeygi

et al. 2024

Preprint

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Induced seismicity: a global phenomenon with special relevance to the Dutch subsurface

Cited by 3 publications

References 18 publications

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

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

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

A Finite Volume Framework for the Fully Implicit Thermal-Hydro-Mechanical-Compositional Modeling in Subsurface Applications

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