scholarly journals Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning

2017 ◽  
Vol 7 (6) ◽  
pp. 625 ◽  
Author(s):  
Manuel Fernández-Gómez ◽  
Gualberto Asencio-Cortés ◽  
Alicia Troncoso ◽  
Francisco Martínez-Álvarez
Keyword(s):  
Ensemble Learning ◽  
Large Earthquake ◽  
Earthquake Magnitude

Equivalent number of uniform cycles versus earthquake magnitude relationships for fine-grained soils

2019 ◽  
Vol 56 (11) ◽  
pp. 1596-1608
Author(s):  
Priyesh Verma ◽  
Ainur Seidalinova ◽  
Dharma Wijewickreme
Keyword(s):  
Large Earthquake ◽  
Earthquake Magnitude ◽  
Undisturbed Soil ◽  
Experimental Database ◽  
Cyclic Shear ◽  
Fine Grained ◽  
Evaluation Procedures ◽  
Magnitude Scaling ◽  
Equivalent Number ◽  
Time Histories

In current geotechnical seismic design practice, the empirical correlation between equivalent number of uniform cycles (Neq) of shaking and earthquake magnitude (Mw) forms an integral part of liquefaction potential evaluation. This relationship, in turn, is used to derive the magnitude scaling factors that are commonly used in field-based liquefaction evaluation procedures. The Neq versus Mw relationship for liquefaction assessment was examined for fine-grained soils using time-histories in the range 5 < Mw ≤ 9, especially including strong ground motion time-histories from the latest subduction zone earthquakes with Mw > 8.0. The experimental database available from cyclic direct simple shear tests conducted on natural fine-grained soils retrieved from undisturbed soil sampling was used to obtain the cyclic shear resistance weighting curves for the study. The work presented herein has contributed to further improving the current models used to represent magnitude scaling factor (MSF) values for large earthquake magnitudes and the functional dependency of this parameter on soil type. The MSF–Mw curve derived for low-plastic Fraser River Delta silt lies in-between the MSF curves derived for clean sand and clay, resonating with the inferences that have been made that the silt behavior can neither be considered sand-like nor clay-like.

Earthquake Magnitude Scaling Using Peak Ground Velocity Derived from High-Rate GNSS Observations

2020 ◽  
Vol 92 (1) ◽  
pp. 227-237
Author(s):  
Rongxin Fang ◽  
Jiawei Zheng ◽  
Jianghui Geng ◽  
Yuanming Shu ◽  
Chuang Shi ◽  
...  
Keyword(s):  
Rapid Determination ◽  
Seismic Station ◽  
Scaling Law ◽  
Large Earthquake ◽  
Earthquake Magnitude ◽  
New Method ◽  
High Rate ◽  
Peak Ground Velocity ◽  
Absolute Deviation ◽  
Magnitude Scaling

Abstract Rapid response to destructive tsunami and seismic events requires rapid determination of the earthquake magnitude. We propose a new method that employs peak ground velocities (PGVs) derived from Global Navigation Satellite System (GNSS) data to estimate earthquake magnitudes. With a total of 1434 records from 22 events as the constraints, we perform the regression and obtain a PGV scaling law for magnitude determination. The advantage of the new method is that the PGVs are extracted from the GNSS velocity waveforms, which can be easily computed using broadcast GNSS ephemeris. In contrast, the peak ground displacement (PGD) depends on a sophisticated high-precision GNSS-processing subject to external correction data, realization of which cannot be kept robust constantly, especially in real time. The results show that the PGV magnitudes agree with reported moment magnitudes with mean absolute deviation of 0.26 magnitude units for the 22 events and also agree well with the PGD magnitude. We further demonstrate that GNSS-derived PGV and the modified Mercalli intensity values can be consistent with their counterparts from the U.S. Geological Survey ShakeMap products and therefore the GNSS-derived PGVs have the potential to be included in the ShakeMap as a complementary constraint, especially in areas with sparse seismic station coverage for large earthquake.

Reversing Cyclic Elastic Demands on Structures during Earthquakes and Applications to Ductility Requirements

1984 ◽  
Vol 1 (1) ◽  
pp. 7-32 ◽  
Author(s):  
Virgilio Perez ◽  
A. Gerald Brady
Keyword(s):  
Elastic Limit ◽  
Epicentral Distance ◽  
Large Earthquake ◽  
Earthquake Magnitude ◽  
Maximum Response ◽  
Plastic Behavior ◽  
Maximum Displacement ◽  
Long Period ◽  
Elastic Demands ◽  
Number Of Cycles

The study of all earthquake-induced oscillator response peaks from selected records shows how these peaks decrease in amplitude with the number of cycles attaining them. These studies concentrate on the ratio between the peak amplitudes of response experienced throughout the duration of a given number of cycles and the maximum response. The ratio shows a trend that is fairly independent of the structure's period, the epicentral distance, and the earthquake magnitude. If during a very large earthquake a structure is forced into displacements beyond the elastic limit it must withstand them successfully with well-designed ductility. For long-period structures, these inelastic displacements, if assumed to result from idealized elasto-plastic behavior, are approximately equal to the elastic displacements under study. The trends shown here consequently indicate that the amplitudes of the elasto-plastic displacements still attained after two, four, or eight cycles, remain at levels that are appreciably high percentages of the maximum displacement.

scholarly journals LARGE EARTHQUAKE MAGNITUDE PREDICTION IN TAIWAN BASED ON DEEP LEARNING NEURAL NETWORK

2018 ◽  
Vol 28 (2) ◽  
pp. 149-160 ◽  
Author(s):  
Jipan Huang ◽  
Xin'an Wang ◽  
Yong Zhao ◽  
Chen Xin ◽  
Han Xiang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Earthquake ◽  
Earthquake Magnitude ◽  
Deep Learning Neural Network

Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks

2015 ◽  
Vol 28 (5) ◽  
pp. 1043-1055 ◽  
Author(s):  
G. Asencio-Cortés ◽  
F. Martínez-Álvarez ◽  
A. Troncoso ◽  
A. Morales-Esteban
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Large Earthquake ◽  
Earthquake Magnitude ◽  
Artificial Neural
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