Establishment of a seismic topographic effect prediction model in the Lushan Ms 7.0 earthquake area

2020 ◽  
Vol 221 (1) ◽  
pp. 273-288
Author(s):  
Hong Zhou ◽  
Jiting Li ◽  
Xiaofei Chen

SUMMARY The seismic topographic effect is one of the debated research topics in seismology and earthquake engineering. This debate is due to the discrepancy between the observed amplification and the amplification underestimation in numerical simulations. Although the numerical simulation of ground motion, which began in the 1970s, has been an important and effective way to study topographic effects, the quantitative mathematical model of topographic amplification is urgent. The actual influences on ground motion due to the topography depends on multiple topographic features, such as the topographic slope, topographic geometrical scale. To date, no definite conclusions regarding the main influencing factors and how to express the influencing factors have been made. In this paper, by introducing the back-propagation (BP) neural network technique, a set of mathematical parameters are determined to establish a quantitative topographic effect prediction model. These parameters are the elevation, the first gradient of the elevation and the higher order gradient in two orthogonal directions. Theoretically, the set of mathematical parameters is directly related to the simple topographic features, such as the elevation, topographic slope and height-to-width ratio. Furthermore, their combinations indirectly denote the complex topographic geometrical features, such as the different topographic geometrical scales, designated by the elevation (large-scale variable), the first gradient (middle-scale variable), the second-order gradient (small-scale variable) and so on (smaller scale variable), and the hill ridges that correspond to the sites with the first gradient of the elevation equal to zero and an elevation larger than its surrounding. In 2013, an earthquake of Ms 7.0 occurred in the Lushan area of Sichuan Province in Western China, where the topography sharply fluctuates. At station 51BXD, an acceleration was recorded close to 1.0 g, while at station 51BXM (14 km away from station 51BXD), the acceleration was recorded at only 0.2–0.3 g. In this paper, the spectral element method (SEM) is used to simulate the ground motion in the Lushan Ms 7.0 earthquake area. Then, the topographic amplification ratio of the simulated ground motion is calculated. Furthermore, a BP topographic amplification prediction model is established and compared based on different parameters. A rms of less than or close to 10 per cent between the BP model prediction results and topographic amplification ratio calculated using the simulated ground motion suggests that the parameters of the topographic elevation, the first gradient of the elevation and the second-order gradient in two orthogonal directions are enough to provide the acceptable topographic effect model in the Lushan area. Finally, using the prediction model, the topographic spectral ratio at stations 51BXD and 51BXM is predicted, and the topography amplification due to the scattering of seismic waves by the irregular topography at 51BXD is found to be 1.5–2 times that of 51BXM. The most important highlights of this paper identify the main factors of the topographic effect for the first time and provide an effective method for establishing a quantitative topographic effect prediction model.

2020 ◽  
Vol 36 (3) ◽  
pp. 1570-1584 ◽  
Author(s):  
David C Heath ◽  
David J Wald ◽  
C Bruce Worden ◽  
Eric M Thompson ◽  
Gregory M Smoczyk

Time-averaged shear wave velocity over the upper 30 m of the earth’s surface ( V S30) is a key parameter for estimating ground motion amplification as both a predictive and a diagnostic tool for earthquake hazards. The first-order approximation of V S30 is commonly obtained through a topographic slope–based or terrain proxy due to the widely available nature of digital elevation models. However, better-constrained V S30 maps have been developed in many regions. Such maps preferentially employ various combinations of V S30 measurements, higher-resolution elevation models, lithologic, geologic, geomorphic, and other proxies and often utilize refined interpolation schemes. We develop a new hybrid global V S30 map database that defaults to the global slope-based V S30 map, but smoothly inserts regional V S30 maps where available. In addition, we present comparisons of the default slope-based proxy maps against the new hybrid version in terms of V S30 and amplification ratio maps, and uncertainties in assigned V S30 values.


Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1328
Author(s):  
Jianguo Zhou ◽  
Shiguo Wang

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.


2021 ◽  
pp. 1-18
Author(s):  
Zhang Zixian ◽  
Liu Xuning ◽  
Li Zhixiang ◽  
Hu Hongqiang

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.


2018 ◽  
Vol 34 (3) ◽  
pp. 1177-1199 ◽  
Author(s):  
Pablo Heresi ◽  
Héctor Dávalos ◽  
Eduardo Miranda

This paper presents a ground motion prediction model (GMPM) for estimating medians and standard deviations of the random horizontal component of the peak inelastic displacement of 5% damped single-degree-of-freedom (SDOF) systems, with bilinear hysteretic behavior and 3% postelastic stiffness ratio, directly as a function of the earthquake magnitude and the distance to the source. The equations were developed using a mixed effects model, with 1,662 recorded ground motions from 63 seismic events. In the proposed model, the median is computed as a function of the vibration period and the normalized strength of the system, as well as the event magnitude and the Joyner-Boore distance to the source. The standard deviation of the model is computed as a function of the vibration period and the normalized strength of the system. The proposed model has the advantage of not requiring an auxiliary elastic GMPM to predict the median and dispersion of peak inelastic displacement.


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