Seismogenic environment and mechanism of the Yangbi MS6.4 earthquake in Yunnan, China

Duan, Mengqiao; Zuo, Kezhen; Zhao, Chen; Zhou, Lianqing

doi:10.1016/j.eqs.2022.08.001

Cited by 6 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have processed earthquake datasets of different regions using ML methods . (Duan et al, 2023) automated the construction of earthquake catalog for multiple reservoirs in China using ML. proposed ML based method to control risk of induced seismicity in unconventional reservoirs.…”

Section: Computational Approachesmentioning

confidence: 99%

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

Tehseen,

Omer,

Mehr

et al. 2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Computational Approachesmentioning

confidence: 99%

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

Tehseen,

Omer,

Mehr

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It has been observed that before an earthquake the seismic region goes through a preparatory period, during which the tectonic stresses cause electromagnetic emissions and heat transferred from the solid-earth to the atmosphere producing climatic anomalies [4]. Many natural processes have been recognized as precursors or warning signals before devastating earthquakes [5] including atmospheric temperature anomalies [6], atmospheric pressure anomalies [7], air ionization [8] and anomalies in rate of rainfall [9] within the selected region. Although, many studies have been conducted to determine the relationship between earthquake precursors but most of the researchers have worked to find out relationship of any single precursor on earthquake generation [10].…”

Section: Introductionmentioning

confidence: 99%

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

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.

show abstract

Three-dimensional VP, VS, and VP/VS imaging based on AI microseismic detection reveals the mechanism of induced earthquakes in the Xiluodu Reservoir Area, China

Li,

Zhou,

Duan

et al. 2024

Journal of Asian Earth Sciences

View full text Add to dashboard Cite

Seismogenic environment and mechanism of the Yangbi MS6.4 earthquake in Yunnan, China

Cited by 6 publications

References 25 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

Three-dimensional VP, VS, and VP/VS imaging based on AI microseismic detection reveals the mechanism of induced earthquakes in the Xiluodu Reservoir Area, China

Contact Info

Product

Resources

About