A non-extensive approach to probabilistic seismic hazard analysis

Motaghed, Sasan; Khazaee, Mozhgan; Eftekhari, Seyed Nasrollah; Mohammadi, Masi

doi:10.5194/nhess-23-1117-2023

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…This could reveal that the order within the system of faults is decreasing, and the amount of accumulated stress is not yet enough to initiate a correlated behavior of the whole system [ 86 ]. High values of q M are found in the regions where large earthquakes have occurred, which is in agreement with previous results [ 30 , 31 , 51 , 62 , 82 , 84 , 86 , 87 ]. When a strong earthquake occurs and much more correlated behavior of the system constituents is assumed to take place, short- and long-range magnitude correlations emerge, inducing an increase in the non-extensivity parameter q M [ 87 ].…”

Section: Analysis and Resultssupporting

confidence: 93%

“…The FMD is usually expressed in terms of the Gutenberg–Richter scaling relation [ 28 ]. Herein, we further describe the FMD with the fragment–asperity model [ 29 ] derived in the framework of Non-Extensive Statistical Physics (NESP) [ 30 , 31 ]. NESP, as developed by Tsallis [ 32 ], provides a generalization of the Boltzmann–Gibbs (BG) statistical physics and constitutes a suitable framework for studying complex systems exhibiting scale invariance, multi-fractality and long-range interactions [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

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Complexity of Recent Earthquake Swarms in Greece in Terms of Non-Extensive Statistical Physics

Sardeli

Michas

Pavlou

et al. 2023

Entropy

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Greece exhibits the highest seismic activity in Europe, manifested in intense seismicity with large magnitude events and frequent earthquake swarms. In the present work, we analyzed the spatiotemporal properties of recent earthquake swarms that occurred in the broader area of Greece using the Non-Extensive Statistical Physics (NESP) framework, which appears suitable for studying complex systems. The behavior of complex systems, where multifractality and strong correlations among the elements of the system exist, as in tectonic and volcanic environments, can adequately be described by Tsallis entropy (Sq), introducing the Q-exponential function and the entropic parameter q that expresses the degree of non-additivity of the system. Herein, we focus the analysis on the 2007 Trichonis Lake, the 2016 Western Crete, the 2021–2022 Nisyros, the 2021–2022 Thiva and the 2022 Pagasetic Gulf earthquake swarms. Using the seismicity catalogs for each swarm, we investigate the inter-event time (T) and distance (D) distributions with the Q-exponential function, providing the qT and qD entropic parameters. The results show that qT varies from 1.44 to 1.58, whereas qD ranges from 0.46 to 0.75 for the inter-event time and distance distributions, respectively. Furthermore, we describe the frequency–magnitude distributions with the Gutenberg–Richter scaling relation and the fragment–asperity model of earthquake interactions derived within the NESP framework. The results of the analysis indicate that the statistical properties of earthquake swarms can be successfully reproduced by means of NESP and confirm the complexity and non-additivity of the spatiotemporal evolution of seismicity. Finally, the superstatistics approach, which is closely connected to NESP and is based on a superposition of ordinary local equilibrium statistical mechanics, is further used to discuss the temporal patterns of the earthquake evolution during the swarms.

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Section: Analysis and Resultssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Complexity of Recent Earthquake Swarms in Greece in Terms of Non-Extensive Statistical Physics

Sardeli

Michas

Pavlou

et al. 2023

Entropy

View full text Add to dashboard Cite

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“…The final step involves calculating the earthquake intensity at the desired location using the specific scenario and the chosen attenuation relationship. The probabilistic approach is considered the most appropriate for addressing the inherent uncertainties in natural hazard-related phenomena [47]. This method incorporates uncertainties such as earthquake magnitude, location, and time [48].…”

Section: Shamentioning

confidence: 99%

Optimizing Interpolation Methods and Point Distances for Accurate Earthquake Hazard Mapping

Alavi,

Bahrami,

Mashayekhi

et al. 2024

Buildings

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Earthquake hazard mapping assesses and visualizes seismic hazards in a region using data from specific points. Conducting a seismic hazard analysis for each point is essential, while continuous assessment for all points is impractical. The practical approach involves identifying hazards at specific points and utilizing interpolation for the rest. This method considers grid point spacing and chooses the right interpolation technique for estimating hazards at other points. This article examines different point distances and interpolation methods through a case study. To gauge accuracy, it tests 15 point distances and employs two interpolation methods, inverse distance weighted and ordinary kriging. Point distances are chosen as a percentage of longitude and latitude, ranging from 0.02 to 0.3. A baseline distance of 0.02 is set, and other distances and interpolation methods are compared with it. Five statistical indicators assess the methods. Ordinary kriging interpolation shows greater accuracy. With error rates and hazard map similarities in mind, a distance of 0.14 points seems optimal, balancing computational time and accuracy needs. Based on the research findings, this approach offers a cost-effective method for creating seismic hazard maps. It enables informed risk assessments for structures spanning various geographic areas, like linear infrastructures.

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