Earthquake Hazard Mitigation and Real-Time Warnings of Tsunamis and Earthquakes

2015 ◽  
Vol 172 (9) ◽  
pp. 2335-2341 ◽  
Author(s):  
Hiroo Kanamori
Keyword(s):  
Real Time ◽  
Hazard Mitigation ◽  
Earthquake Hazard

Real-time seismology and earthquake hazard mitigation

Nature ◽  
10.1038/37280 ◽  
1997 ◽  
Vol 390 (6659) ◽  
pp. 461-464 ◽  
Author(s):  
Hiroo Kanamori ◽  
Egill Hauksson ◽  
Thomas Heaton
Keyword(s):  
Real Time ◽  
Hazard Mitigation ◽  
Earthquake Hazard

Near-Real-Time Seismic Monitoring for Pipelines

2018 ◽  
Author(s):  
Martin Zaleski ◽  
Gerald Ferris ◽  
Alex Baumgard
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Earthquake Hazard ◽  
Lateral Spreading ◽  
Soil Liquefaction ◽  
Gas Pipelines ◽  
Hazard Management ◽  
Earthquake Probability ◽  
Oil And Gas Pipelines ◽  
Map Data

Earthquake hazard management for oil and gas pipelines should include both preparedness and response. The typical approach for management of seismic hazards for pipelines is to determine where large ground motions are frequently expected, and apply mitigation to those pipeline segments. The approach presented in this paper supplements the typical approach but focuses on what to do, and where to do it, just after an earthquake happens. In other words, we ask and answer: “Is the earthquake we just had important?”, “What pipeline is and what sites might it be important for?”, and “What should we do?” In general, modern, high-pressure oil and gas pipelines resist the direct effects of strong shaking, but are vulnerable to large co-seismic differential permanent ground displacement (PGD) produced by surface fault rupture, landslides, soil liquefaction, or lateral spreading. The approach used in this paper employs empirical relationships between earthquake magnitude, distance, and the occurrence of PGD, derived from co-seismic PGD case-history data, to prioritize affected pipeline segments for detailed site-specific hazard assessments, pre-event resiliency upgrades, and post-event response. To help pipeline operators prepare for earthquakes, pipeline networks are mapped with respect to earthquake probability and co-seismic PGD susceptibility. Geological and terrain analyses identify pipeline segments that cross PGD-susceptible ground. Probabilistic seismic models and deterministic scenarios are considered in estimating the frequency of sufficiently large and close causative earthquakes. Pipeline segments are prioritized where strong earthquakes are frequent and ground is susceptible to co-seismic PGD. These may be short-listed for mitigation that either reduces the pipeline’s vulnerability to damage or limits failure consequences. When an earthquake occurs, pipeline segments with credible PGD potential are highlighted within minutes of an earthquake’s occurrence. These assessments occur in near-real-time as part of an online geohazard management database. The system collects magnitude and location data from online earthquake data feeds and intersects them against pipeline network and terrain hazard map data. Pipeline operators can quickly mobilize inspection and response resources to a focused area of concern.

An integrated decision support system using satellite and in-situ data for coastal area hazard mitigation and resilience to natural disasters

2021 ◽  
Author(s):  
Christos Kontopoulos ◽  
Nikos Grammalidis ◽  
Dimitra Kitsiou ◽  
Vasiliki Charalampopoulou ◽  
Anastasios Tzepkenlis ◽  
...  
Keyword(s):  
Decision Support ◽  
Surface Water ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Satellite Data ◽  
Hazard Mitigation ◽  
Google Earth ◽  
Landsat 8

<p>Nowadays, the importance of coastal areas is greater than ever, with approximately 10% of the global population living in these areas. These zones are an intermediate space between sea and land and are exposed to a variety of natural (e.g. ground deformation, coastal erosion, flooding, tornados, sea level rise, etc.) and anthropogenic (e.g. excessive urbanisation) hazards. Therefore, their conservation and proper sustainable management is deemed crucial both for economic and environmental purposes. The main goal of the Greece-China bilateral research project “EPIPELAGIC: ExPert Integrated suPport systEm for coastaL mixed urbAn – industrial – critical infrastructure monitorinG usIng Combined technologies” is the design and deployment of an integrated Decision Support System (DSS) for hazard mitigation and resilience. The system exploits near-real time data from both satellite and in-situ sources to efficiently identify and produce alerts for important risks (e.g. coastal flooding, soil erosion, degradation, subsidence), as well as to monitor other important changes (e.g. urbanization, coastline). To this end, a robust methodology has been defined by fusing satellite data (Optical/multispectral, SAR, High Resolution imagery, DEMs etc.) and in situ real-time measurements (tide gauges, GPS/GNSS etc.). For the satellite data pre-processing chain, image composite/mosaic generation techniques will be implemented via Google Earth Engine (GEE) platform in order to access Sentinel 1, Sentinel 2, Landsat 5 and Landsat 8 imagery for the studied time period (1991-2021). These optical and SAR composites will be stored into the main database of the EPIPELAGIC server, after all necessary harmonization and correction techniques, along with other products that are not yet available in GEE (e.g. ERS or Sentinel-1 SLC products) and will have to be locally processed. A Machine Learning (ML) module, using data from this main database will be trained to extract additional high-level information (e.g. coastlines, surface water, urban areas, etc.). Both conventional (e.g. Otsu thresholding, Random Forest, Simple Non-Iterative Clustering (SNIC) algorithm, etc.) and deep learning approaches (e.g. U-NET convolutional networks) will be deployed to address problems such as surface water detection and land cover/use classification. Additionally, in-situ or auxiliary/cadastral datasets will be used as ground truth data. Finally, a Decision Support System (DSS), will be developed to periodically monitor the evolution of these measurements, detect significant changes that may indicate impending risks and hazards, and issue alarms along with suggestions for appropriate actions to mitigate the detected risks. Through the project, the extensive use of Explainable Artificial Intelligence (xAI) techniques will also be investigated in order to provide “explainable recommendations” that will significantly facilitate the users to choose the optimal mitigation approach. The proposed integrated monitoring solutions is currently under development and will be applied in two Areas of Interest, namely Thermaic Gulf in Thessaloniki, Greece, and the Yellow River Delta in China. They are expected to provide valuable knowledge, methodologies and modern techniques for exploring the relevant physical mechanisms and offer an innovative decision support tool. Additionally, all project related research activities will provide ongoing support to the local culture, society, economy and environment in both involved countries, Greece and China.</p>

3-D seismic structure of the Kachchh, Gujarat, and its implications for the earthquake hazard mitigation

2011 ◽  
Vol 57 (1) ◽  
pp. 83-105 ◽  
Author(s):  
A. P. Singh ◽  
O. P. Mishra ◽  
B. K. Rastogi ◽  
Dinesh Kumar
Keyword(s):  
Hazard Mitigation ◽  
Earthquake Hazard ◽  
Seismic Structure

A Method for Creating Real-Time Earthquake Hazard Map in Korean Peninsula

2017 ◽  
Vol 18 (1) ◽  
pp. 193-198
Author(s):  
Hyeju Oh ◽  
◽  
Jieun Im ◽  
Yelim Lee ◽  
Sanghoo Yoon ◽  
...  
Keyword(s):  
Real Time ◽  
Korean Peninsula ◽  
Earthquake Hazard ◽  
Hazard Map

Real-time earthquake hazard assessment based on high-rate GNSS PPPAR

2020 ◽  
Author(s):  
Yang Jiang ◽  
Yang Gao ◽  
Michael Sideris
Keyword(s):  
Real Time ◽  
Hazard Assessment ◽  
Seismic Waves ◽  
Seismic Hazard Assessment ◽  
Earthquake Hazard ◽  
High Rate ◽  
Peak Ground Velocity ◽  
International Gnss Service ◽  
Time Service ◽  
Earthquake Hazard Assessment

<p>To provide hazard assessment in rapid or real-time mode, accelerations due to seismic waves have traditionally been recorded by seismometers. Another approach, based on the Global Navigation Satellite System (GNSS), known as GNSS seismology, has become increasingly accurate and reliable. In the past decade, significant improvements have been made in high-rate GNSS using precise point positioning and its ambiguity resolution (PPPAR). To reach cm-level accuracy, however, PPPAR requires specific products, including satellite orbit/clock corrections and phase/code biases generated by large GNSS networks. Therefore, the use of PPPAR in real-time seismology applications has been inhibited by the limitations in product accessibility, latency, and accuracy. To minimize the implementation barrier for ordinary global users, the Centre National D’Etudes Spatiales (CNES) in France has launched a public PPPAR correction service via real-time internet streams. Broadcasting via the real-time service (RTS) of the international GNSS service (IGS), the correction stream is freely provided. Therefore, in our work, a new approach using PPPAR assisted with the CNES product to process high-rate in-field GNSS measurements is proposed for real-time earthquake hazard assessment. A case study is presented for the Ridgecrest, California earthquake sequence in 2019. The general performance of our approach is evaluated by assessing the quality of the resulting waveforms against publicly available post-processing GNSS results from a previous study by Melgar et al. (2019), Seismol. Res. Lett. XX, 1–9, doi: 10.1785/ 0220190223. Even though the derived real-time displacements are noisy due to the accuracy limitation of the CNES product, the results show a cm-level agreement with the provided post-processed control values in terms of root-mean-square (RMS) values in time and frequency domain, as well as seismic features of peak-ground-displacement (PGD) and peak-ground-velocity (PGV). Overall, we have shown that high-rate GNSS processing based on PPPAR via a freely accessible service like CNES is a reliable approach that can be utilized for real-time seismic hazard assessment.</p>

Computational Challenges in Real-Time Hybrid Simulation of Tall Buildings under Multiple Natural Hazards

2018 ◽  
Vol 763 ◽  
pp. 566-575 ◽  
Author(s):  
Chinmoy Kolay ◽  
James M. Ricles ◽  
Thomas M. Marullo ◽  
Safwan Al-Subaihawi ◽  
Spencer E. Quiel
Keyword(s):  
Real Time ◽  
Natural Hazards ◽  
Hybrid Simulation ◽  
Tall Buildings ◽  
Earthquake Hazard ◽  
Performance Based Design ◽  
The Real ◽  
Large Systems ◽  
Wind Events ◽  
Energy Dissipation Devices

The essence of real-time hybrid simulation (RTHS) is its ability to combine the benefits ofphysical testing with those of computational simulations. Therefore, an understanding of the real-timecomputational issues and challenges is important, especially for RTHS of large systems, in advancingthe state of the art. To this end, RTHS of a 40-story (plus 4 basement stories) tall building havingnonlinear energy dissipation devices for mitigation of multiple natural hazards, including earthquakeand wind events, were conducted at the NHERI Lehigh Experimental Facility. An efficient implementationprocedure of the recently proposed explicit modified KR-a (MKR-a) method was developedfor performing the RTHS. This paper discusses this implementation procedure and the real-time computationalissues and challenges with regard to this implementation procedure. Some results from theRTHS involving earthquake loading are presented to highlight the need for and application of RTHSin performance based design of tall buildings under earthquake hazard.

Sign in / Sign up

Export Citation Format

Share Document