پیش بینی حرکت زمین مبتنی بر فیزیک
ترجمه نشده

پیش بینی حرکت زمین مبتنی بر فیزیک

عنوان فارسی مقاله: چالش های مداوم در پیش بینی حرکت زمین مبتنی بر فیزیک و بینش های مربوط به سال های ۲۰۱۰ تا ۲۰۱۱ Canterbury و ۲۰۱۶ Kaikoura، زلزله های نیوزلند
عنوان انگلیسی مقاله: On-going challenges in physics-based ground motion prediction and insights from the 2010–۲۰۱۱ Canterbury and 2016 Kaikoura, New Zealand earthquakes
مجله/کنفرانس: دینامیک خاک و مهندسی زلزله – Soil Dynamics and Earthquake Engineering
رشته های تحصیلی مرتبط: مهندسی عمران
گرایش های تحصیلی مرتبط: زلزله
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.soildyn.2018.04.042
دانشگاه: University of Canterbury, Christchurch, New Zealand
صفحات مقاله انگلیسی: 11
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 2.989 در سال 2018
شاخص H_index: 78 در سال 2019
شاخص SJR: 1.359 در سال 2018
شناسه ISSN: 0267-7261
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13432
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1. Introduction

2. Ground motion prediction of the 2010 Darfield, 2011 Christchurch, and 2016 Kaikoura earthquakes

3. On-going challenges in ground motion simulation

4. Discussion and conclusions

Acknowledgements

References

بخشی از مقاله (انگلیسی)

Abstract

This paper presents on-going challenges in the present paradigm shift of earthquake-induced ground motion prediction from empirical to physics-based simulation methods. The 2010–۲۰۱۱ Canterbury and 2016 Kaikoura, New Zealand earthquakes are used to illustrate the predictive potential of the different methods. On-going efforts in simulation validation and theoretical developments are then presented, as well as the demands associated with the need for explicit consideration of modelling uncertainties. Finally, discussion is also given to the tools and databases needed for the efficient utilisation of simulated ground motions both in specific engineering projects as well as for near-real-time impact assessment.

Introduction

Earthquake-induced ground motion prediction is presently undergoing a paradigm shift from the empirical prediction of ground motion intensity measures (IMs, e.g. PGA, SA), based on regression analysis of observed IMs from past earthquakes, toward the use of physics-based simulation methods that directly predict the ground motion time series (i.e. multi-component acceleration as a function of time). This paradigm shift is presently occurring as a result of three key factors. Firstly, the diminishing returns offered from the continual efforts in empirical ground motion modelling, most evident in terms of the lack of any appreciable reduction in the standard deviation of IM prediction over four decades [13,44]. Secondly, recent well-recorded earthquakes (such as those discussed herein, among others) illustrate that, even now, physics-based simulation methods provide predictions that are comparable to, or even superior than, those from empiricallybased predictions [4,10,15,18,22]. Thirdly, the physics-based nature of such simulations provides a natural framework within which a substantially greater volume of data from seismological observations can be synthesised, enabling the incorporation of region and site-specific features, thus promising appreciable improvements in the ability to reduce prediction uncertainties in the coming years, and realising the flow-on benefits in the seismic design and assessment of built infrastructure [49]. It is important to appreciate that this empirical to physics-based modelling paradigm shift is akin to that which occurred in weather forecasting in the 1950’s – although the seismic problem is complicated relative to the weather problem because the salient phenomena occur beneath the earth’s surface, making direct observation challenging as compared to direct atmospheric observations.