Regional terrain-based VS30 prediction models for China

نویسندگان

چکیده

Abstract Time-averaged shear-wave velocity to 30 m ( V S ) is commonly used in ground motion models as a parameter for evaluating site effects. This study collection of boreholes Beijing, Tianjin, Guangxi, Guangdong, and three other municipalities provinces, which were divided into regions with reference the seismic zonation map China, establish prediction based on terrain categories. Regional effects verified by comparing morphometric (topographic slope, surface texture, local convexity) thresholds classification maps obtained from global digital elevation model (DEM) data regional DEM regions. Additionally, using both types established analyzed comparatively provide credible China. Through analysis correlations between measured values predicted values, calculation mean squared error absolute percentage each region, consideration geological characteristics boreholes, finally applied developing Intercomparison indicated that subregional necessary classification. Finally, spatial method adopting inverse distance weighting residuals was update initial models. The developed could be construction earthquake disaster scenarios. Graphical

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Reference wind farm selection for regional wind power prediction models

Short-term wind power forecasting is recognized today as a major requirement for a secure and economic integration of wind generation in power systems. This paper deals with the case of regional forecasting of wind power with a large number of wind farms involved. Due to the large amount of potentially available information and also because part of the wind farms may not be "observable", foreca...

متن کامل

Adaptive Network-based Fuzzy Inference System-Genetic Algorithm Models for Prediction Groundwater Quality Indices: a GIS-based Analysis

The prediction of groundwater quality is very important for the management of water resources and environmental activities. The present study has integrated a number of methods such as Geographic Information Systems (GIS) and Artificial Intelligence (AI) methodologies to predict groundwater quality in Kerman plain (including HCO3-, concentrations and Electrical Conductivity (EC) of groundwater)...

متن کامل

Terrain prediction for an eight-legged robot

Most legged robotsmustnegotiateunknownenvironmentswith little orno terrainknowledge, as autonomous terrain mapping for robots is limited. A predictive terrain contour mapping strategy is proposed, which employs the use of a feed-forward neural network to predict the contours in environments, based on the positions of the neighboring legs. The predicted performance is better than previous implem...

متن کامل

a new approach to credibility premium for zero-inflated poisson models for panel data

هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...

15 صفحه اول

Presenting a Morphological Based Approach for Filtering The Point Cloud to Extract the Digital Terrain Model

The Digital terrain model is an important geospatial product used as the basis of many practical projects related to geospatial information. Nowadays, a dense point cloud can be generated using the LiDAR data. Actually, the acquired point cloud of the LiDAR, presents a digital surface model that contains ground and non-ground objects. The purpose of this paper is to present a new approach of ex...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Earth, Planets and Space

سال: 2023

ISSN: ['1880-5981', '1343-8832']

DOI: https://doi.org/10.1186/s40623-023-01826-3