Time Series Analysis in Earthquake Complex Networks
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: 20 August 2024 | Viewed by 151
Special Issue Editors
Interests: physics of disordered media; nonlinear dynamics and artificial intelligence in analysis of fracture processes from laboratory to earthquake scale; earthquake precursors; environmental and prospecting geophysics
Interests: geophysical time series analysis; statistical methods for the investigation of geophysical processes; point processes; fractals and multifractals; graphs and networks; complexity; information theory
Special Issues, Collections and Topics in MDPI journals
Interests: geophysics; earth physics; seismology; applied geophysics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Earthquake time series are complex due to the variety of forces affecting EQ generation, complexity of the Earth's crust and mantle structure, where the earthquake source is developing, and complexity of the EQ process development in space (generation of elementary cracks, their multiplication and coalescence, leading to main fault formation and final rupture) and time (kinetics of the different stages of the fracturing process). The early stages of the process developing in the deep layers of the Earth's crust cannot be studied in detail experimentally; at present, the only way to model these initial stages of EQ source development is experimental and theoretical modeling. New physical/mathematical methods of data analysis like nonlinear dynamics, artificial intelligence/machine learning/deep learning, non-extensive statistical analysis, natural time analysis and complex network approach allow us to obtain mathematical regularities underlying physical/geophysical processes using just the time series of experimental observations.
The aim of this Special Issue is to study the progress in the analysis of complex systems’ time series during the fracture process using new approaches. Researchers are encouraged to present original contributions and their latest advancements in theoretical and experimental studies focused on understanding the complexity of earthquake time series.
Dr. Tamaz Chelidze
Dr. Luciano Telesca
Prof. Dr. Filippos Vallianatos
Guest Editors
Manuscript Submission Information
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Keywords
- complex systems
- laboratory fracture and earthquake process models
- nonlinear dynamics and machine learning
- non-extensive statistical analysis
