A synoptic view of the natural time distribution and contemporary earthquake hazards in Sumatra, Indonesia

2021 ◽  
Author(s):  
Sumanta Pasari ◽  
Andrean V. H. Simanjuntak ◽  
Anand Mehta ◽  
Neha ◽  
Yogendra Sharma
Keyword(s):  
Time Distribution ◽  
Earthquake Hazards ◽  
Natural Time ◽  
Synoptic View

scholarly journals Nowcasting earthquakes in Sulawesi Island, Indonesia

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sumanta Pasari ◽  
Andrean V. H. Simanjuntak ◽  
Neha ◽  
Yogendra Sharma
Keyword(s):  
Seismic Risk ◽  
Large Earthquake ◽  
Earthquake Hazards ◽  
Power Law Distribution ◽  
Probability Models ◽  
Current State ◽  
City Regions ◽  
Natural Time ◽  
Earthquake Potential ◽  
The City

AbstractLarge devastating events such as earthquakes often display frequency–magnitude statistics that exhibit power-law distribution. In this study, we implement a recently developed method called earthquake nowcasting (Rundle et al. in Earth Space Sci 3: 480–486, 2016) to evaluate the current state of earthquake hazards in the seismic prone Sulawesi province, Indonesia. The nowcasting technique considers statistical behavior of small event counts between successive large earthquakes, known as natural times, to infer the seismic progression of large earthquake cycles in a defined region. To develop natural-time statistics in the Sulawesi Island, we employ four probability models, namely exponential, exponentiated exponential, gamma, and Weibull distribution. Statistical inference of natural times reveals that (i) exponential distribution has the best representation to the observed data; (ii) estimated nowcast scores (%) corresponding to M ≥ 6.5 events for 21 cities are Bau-bau (41), Bitung (70), Bone (44), Buton (39), Donggala (63), Gorontalo (49), Kendari (27), Kolaka (30), Luwuk (56), Makassar (52), Mamuju (58), Manado (70), Morowali (37), Palopo (34), Palu (62), Pare-pare (82), Polewali (61), Poso (42), Taliabu (55), Toli-toli (58), and Watampone (55); and (iii) the results are broadly stable against the changes of magnitude threshold and area of local regions. The presently revealed stationary Poissonian nature of the underlying natural-time statistics in Sulawesi brings out a key conclusion that the seismic risk is the same for all city regions despite their different levels of cycle progression realized through nowcast scores. In addition, though the earthquake potential scores of the city regions will be updated with the occurrence of each small earthquake in the respective region, the seismic risk remains the same throughout the Sulawesi Island.

Contemporary Earthquake Hazards in the West-Northwest Himalaya: A Statistical Perspective through Natural Times

2020 ◽  
Vol 91 (6) ◽  
pp. 3358-3369
Author(s):  
Sumanta Pasari ◽  
Yogendra Sharma
Keyword(s):  
Land Use Planning ◽  
Probability Distributions ◽  
Cumulative Distribution ◽  
Earthquake Hazards ◽  
City Region ◽  
Sensitivity Testing ◽  
The West ◽  
Small Magnitude ◽  
Himalayan Orogeny ◽  
Natural Time

Abstract Himalayan earthquakes have deep societal and economic impact. In this article, we implement a surrogate method of nowcasting (Rundle et al., 2016) to determine the current state of seismic hazard from large earthquakes in a dozen populous cities from India and Pakistan that belong to the west-northwest part of Himalayan orogeny. For this, we (1) perform statistical inference of natural times, intersperse counts of small-magnitude events between pairs of succeeding large events, based on a set of eight probability distributions; (2) compute earthquake potential score (EPS) of 14 cities from the best-fit cumulative distribution of natural times; and (3) carry out a sensitivity testing of parameters—threshold magnitude and area of city region. Formulation of natural time (Varostos et al., 2005) based on frequency–magnitude power-law statistics essentially avoids the daunting need of seismicity declustering in hazard estimation. A retrospective analysis of natural time counts corresponding to M≥6 events for the Indian cities provides an EPS (%) as New Delhi (56), Chandigarh (86), Dehradun (83), Jammu (99), Ludhiana (89), Moradabad (84), and Shimla (87), whereas the cities in Pakistan observe an EPS (%) as Islamabad (99), Faisalabad (88), Gujranwala (99), Lahore (89), Multan (98), Peshawar (38), and Rawalpindi (99). The estimated nowcast values that range from 38% to as high as 99% lead to a rapid yet useful ranking of cities in terms of their present progression to the regional earthquake cycle of magnitude ≥6.0 events. The analysis inevitably encourages scientists and engineers from governments and industry to join hands for better policymaking toward land-use planning, insurance, and disaster preparation in the west-northwest part of active Himalayan belt.

scholarly journals Nowcasting Earthquakes in Sulawesi Island, Indonesia

2020 ◽  
Author(s):  
Sumanta Pasari ◽  
Andrean VH Simanjuntak ◽  
Yogendra Sharma
Keyword(s):  
Large Earthquake ◽  
Earthquake Hazard ◽  
Earthquake Hazards ◽  
Power Law Distribution ◽  
Probability Models ◽  
Current State ◽  
Natural Time ◽  
Earthquake Hazard Assessment ◽  
Assessment Techniques ◽  
Earthquake Cycles

Abstract Large devastating events such as earthquakes often display frequency-magnitude statistics that exhibit power-law distribution. In this study, we implement a new method of nowcasting (Rundle et al. 2016) to evaluate the current state of earthquake hazards in the seismic prone Sulawesi province, Indonesia. The nowcasting technique considers statistical behavior of small event counts, known as natural times, to infer the seismic progression of large earthquake cycles in a defined region. To develop natural time statistics in the Sulawesi Island, we employ four probability models, namely exponential, exponentiated exponential, gamma, and Weibull distribution. Statistical inference of natural times reveals that (i) exponential distribution has the best representation to the observed data; (ii) estimated nowcast scores (%) corresponding to M≥6.5 events for 21 cities are Bau-bau (41), Bitung (70), Bone (44), Buton (39), Donggala (63), Gorontalo (49), Kendari (27), Kolaka (30), Luwuk (56), Makassar (52), Mamuju (58), Manado (70), Morowali (37), Palopo (34), Palu (62), Pare-pare (82), Polewali (61), Poso (42), Taliabu (55), Toli-toli (58), and Watampone (55); and (iii) the results are broadly consistent to the changes of magnitude threshold and area of local regions. Therefore, the present nowcasting analysis, similar to the traditional earthquake hazard assessment techniques, offers a simple yet versatile metric to the scientists, engineers and policymakers to examine the current state of earthquake hazards in the thickly populated Sulawesi Island.

Space and Time Distribution of Hard X-Ray Emission in a Loop at the Beginning of a Flare

1994 ◽  
Vol 144 ◽  
pp. 275-277
Author(s):  
M. Karlický ◽  
J. C. Hénoux
Keyword(s):  
Time Distribution ◽  
Electron Bombardment ◽  
Spatial Evolution ◽  
Superthermal Electrons ◽  
Flare Loop ◽  
X Ray ◽  
Superthermal Electron ◽  
Flare Loops ◽  
Chromospheric Evaporation ◽  
Temporal And Spatial

AbstractUsing a new ID hybrid model of the electron bombardment in flare loops, we study not only the evolution of densities, plasma velocities and temperatures in the loop, but also the temporal and spatial evolution of hard X-ray emission. In the present paper a continuous bombardment by electrons isotropically accelerated at the top of flare loop with a power-law injection distribution function is considered. The computations include the effects of the return-current that reduces significantly the depth of the chromospheric layer which is evaporated. The present modelling is made with superthermal electron parameters corresponding to the classical resistivity regime for an input energy flux of superthermal electrons of 109erg cm−2s−1. It was found that due to the electron bombardment the two chromospheric evaporation waves are generated at both feet of the loop and they propagate up to the top, where they collide and cause temporary density and hard X-ray enhancements.

Intraseasonal Pattern in the Time Distribution of Suicides

Crisis ◽  
2000 ◽  
Vol 21 (2) ◽  
pp. 95-97 ◽  
Author(s):  
Marco BL Rocchi ◽  
Chiara Perlini
Keyword(s):  
Time Distribution

The effect of study time distribution on the learning and retention of paired associates

2010 ◽  
Author(s):  
Mario O. de Jonge ◽  
Diane Pecher ◽  
Jan W. Van Strien ◽  
Huib Tabbers ◽  
Rene Zeelenberg
Keyword(s):  
Time Distribution ◽  
Study Time ◽  
Paired Associates
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