article وصول مفتوح

Artificial Intelligence and Big Data Analytics for Seismic Hazard Assessment: Methodological Advances and Computational Frameworks for the Marmara Region, Türkiye

  • Zenodo (CERN European Organization for Nuclear Research)
  • European Organization for Nuclear Research
Research footprint

At a glance

الاستشهادات
0
المراجع
0
Comments
0
Paper overview

Abstract

The Marmara region of Türkiye, situated along the North Anatolian Fault Zone (NAFZ), constitutes one of the most seismically active and densely monitored zones globally. Given the region’s high vulnerability and the catastrophic impacts of historical events—notably the 1999 İzmit and 2023 Kahramanmara¸s sequences—there is a critical need for advanced seismic hazard risk assessment (SHRA) methods that move beyond static models. This review examines the paradigm shift from traditional geophysics to big data seismology, characterized by the “Five Vs”: volume, velocity, variety, veracity, and value. Critically, we distinguish between two fundamentally different problems: Earthquake Early Warning (EEW), which operates on sub-second timescales after rupture initiation, and probabilistic earthquake forecasting, which operates on timescales of years to decades. The study discusses how cloud-native platforms such as Azure Databricks, combined with data pipelines using Apache Kafka (version 3.5.1) and Apache Spark (version 4.1.2), enable the real-time processing of petabyte-scale seismic sensor streams. Key technological tools, including Physics-Informed Neural Networks (PINNs) and deep learning models such as PhaseNet, are analyzed for their demonstrated ability to enhance EEW systems through sub-second phase picking and automated event detection. Seismic tomography is also undergoing AI-enabled transformation, yielding higher-resolution subsurface imaging. We present statistical validation metrics and uncertainty quantification methods essential for credible hazard assessment. By addressing computational bottlenecks through hybrid computing architectures and edge computing, this framework aims to improve the warning lead time for Istanbul’s critical infrastructure. This work provides a structured roadmap for bridging the gap between traditional seismic data analysis and operational predictive analytics in the Marmara region.

Record transparency

Publication details

DOI
10.5281/zenodo.20513886
OpenAlex
W7163169897
Document type
article
Language
EN
Source
Zenodo (CERN European Organization for Nuclear Research)
Last metadata update
المجتمع

Comments

تسجيل الدخول للانضمام إلى النقاش.

  1. لا توجد تعليقات بعد. ابدأ النقاش.