Big Data in Seismology: Methods and Applications

Dear Colleagues,

We invite you to contribute to this Special Issue on Big Data in Seismology: Methods and Applications.

The term "Big Data" has grown in prominence over the last ten years. As a result, the field of big geospatial data has recently received considerable attention from various areas. This Special Issue outlines the development of big data theory and practice as a scientific area, as well as the key characteristics and processing strategies. For instance, big data sources in Earth sciences include meteorology, seismic exploration, and remote sensing. We also discuss seismic monitoring data, which can become big data combined with other geophysical data. In addition, researchers now have instant access to processing power thanks to cloud computing services.

Additionally, parallel and distributed computing enable scientists to run a variety of computations concurrently, frequently dispersed across multiple workstations. The utilization of data has played a significant role in geosciences. New potential for the extensive use of data in geosciences has emerged due to recent scientific and professional advancements in data collection in geosciences and data analysis using big data and machine learning.

Since the Mediterranean is a tectonic boundary zone between the African and European plates, it is a particularly seismically active region. Additionally, it is a highly populated area with significant seismic risk exposure. Applying approaches that seek to detect and analyze earthquakes would result in a rich and high-resolution dataset, which would, in turn, help shed more light on the underlying mechanisms. We aim to produce unique research publications regarding studies of earthquake swarms and complex seismic sequences in volcanic and tectonic environments to advance our understanding of the underlying physics of the processes at their genesis.

For this Special Issue, we encourage articles, reviews, and technical reports that combine theory and experiments to process seismic data using parallel computing and deep learning. Appropriate articles will discuss the most recent advancements and cutting-edge research in the geospatial big data field, with a focus on the following issues:

• Reporting and characterizing swarm-like and complex sequences in terms of spatial and temporal evolution, scaling properties, and triggering processes;
• Data selection and acquisition;
• Seismotectonic and seismicity of the region;
• Artificial intelligence and geoscience-based design methodologies (machine learning and deep learning);
• Data science's role in the geosciences' conventional and new issues;
• Needs and perspectives for the use of data in geosciences;
• Simulating the essential mechanical conditions resulting in rupture dynamics using laboratory and numerical models.

Dr. Alexandra Moshou
Dr. Antonios Konstantaras
Dr. Michail Gianniou
Guest Editors

Manuscript Submission Information

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