Hypothetical Real‐Time GNSS Modeling of the 2016 Mw 7.8 Kaikōura Earthquake: Perspectives from Ground Motion and Tsunami Inundation Prediction

2018 ◽  
Vol 108 (3B) ◽  
pp. 1736-1745 ◽  
Author(s):  
Brendan W. Crowell ◽  
Diego Melgar ◽  
Jianghui Geng
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Tsunami Inundation ◽  
Kaikoura Earthquake

Modified Mercalli intensities for the M7.8 Kaikōura (New Zealand) 14 November 2016 earthquake derived from ‘felt detailed’ and ‘felt rapid’ online questionnaires

2017 ◽  
Vol 50 (2) ◽  
pp. 352-362 ◽  
Author(s):  
Tatiana Goded ◽  
Nick Horspool ◽  
Silvia Canessa ◽  
Matt Gerstenberger
Keyword(s):  
New Zealand ◽  
Real Time ◽  
Ground Motion ◽  
Public Awareness ◽  
Quality Data ◽  
Detailed Data ◽  
Minimum Requirement ◽  
Recent Earthquakes ◽  
Kaikoura Earthquake ◽  
Good Quality Data

This paper describes the shaking intensity levels caused by the M7.8 Kaikōura earthquake of 14/11/2016 according to the information from the two current GeoNet online questionnaires, ‘Felt Detailed’ and ‘Felt RAPID’. A recently developed method to extract intensity levels at a community scale using ‘Felt Detailed’ data is used. These are compared with individual intensities from ‘Felt RAPID’ survey, instrumental intensities from two recent ground motion to intensity conversion equations, and traditional intensity assignments. While maximum Modified Mercalli instrumental, traditional, ‘Felt RAPID’ and individual ‘Felt Detailed’ intensities go up to 8, community intensities using ‘Felt Detailed’ mostly only go up to 5, with only four communities with MM 6-7. Reasons for this discrepancy include a) lack of data around the epicentre; b) few reports from this event compared to other smaller recent earthquakes; and c) lack of public awareness of ‘Felt Detailed” surveys, released shortly after the earthquake. In addition, only 47% of reports were used to calculate community intensities, based on a minimum requirement for robust calculation of 5 reports. Although ‘Felt RAPID’ provided a much larger number of reports (more than 15,000) for this earthquake compared to ‘Felt Detailed’ (3500), the reliability of the former may be compromised by their lack of detail. Results from this paper suggest that, when enough reports are submitted, ‘Felt Detailed’ can provide good quality data that can be used in tools such as the near-real time shaking intensity maps provided in ShakeMapNZ.

A Seismic Alert System for Oil & Gas Offshore Fields

2021 ◽  
Author(s):  
Pamela Poggi ◽  
Emilia Fiorini ◽  
Daniela Tonoli ◽  
Francesca Ioele ◽  
Eric John Parker ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Oil And Gas ◽  
Focal Depth ◽  
Threshold Values ◽  
Alert System ◽  
Ground Acceleration ◽  
Gas Field ◽  
Expected Values ◽  
Earthquake Data

Abstract Objectives/Scope This paper presents an innovative web tool developed for the seismic monitoring of critical infrastructure. As an example, we describe an application for the ENI offshore facilities, Jangkrik and Merakes Fields Development, offshore Indonesia. Methods, Procedures, Process The system monitors reported seismic activity in a project area, and issues warnings when earthquakes detected may have directly or indirectly impacted facilities. Notifications allow the owner to optimize decisions regarding post-earthquake asset surveys and maintenance, avoiding the need for inspections in areas not significantly affected. A system of email alerts and a web based GIS platform provide the end-user with a tool to control its own assets. Results, Observations, Conclusions The purpose of the tool is to indirectly monitor earthquakes in an area and identify those which may have damaged the Oil and Gas facilities of interest. This identification requires accurate near real-time earthquake data such as date, time, location, magnitude, and focal depth. To this end, the system retrieves earthquake data from a qualified set of public seismic agencies. The system computes the expected values of shaking at the specific offshore facilities (platforms, subsea structures, pipelines, etc.). Calculations are based on sets of Ground Motion Prediction Equations (GMPEs) selected to match the seismotectonic environment. The expected values of seismic acceleration generated by an earthquake are compared with threshold values and a warning message is issued to the facilities supervisors when the ground acceleration exceeds design values. Threshold values related to secondary seismic effects (e.g., seismically induced landslides, debris flow) which could affect facilities integrity are also considered in the alert system. Threshold values are defined considering project seismic and geohazard documents, to summarize strong ground motion parameters that could potentially trigger damaging seismic geohazards, and project design documents to collect all data about seismic design of the assets. Monitoring intervals are defined based on the documentation screening. Several alarm levels are selected, based on the potential severity of earthquake effects. The more severe levels of ground motion, with high damage potential, can trigger recommendation for inspection. Novel/Additive Information Asset integrity and safety are key drivers in the offshore petroleum industry. Safety performance with respect to earthquakes is a fundamental issue in all seismic prone areas. The seismic alert system presented highlights, in near real time, earthquakes that are potentially critical for structures in an Oil and Gas field. This allows the owners to make quick decisions and plan necessary intervention regarding assets affected directly or indirectly by earthquakes. Exploiting the wide background of knowledge in engineering and geoscience and the modern availability of global earthquake data, the tool can provide useful assistance in managing asset integrity, regardless of the availability of local seismic networks or strong motion stations.

Generic Source Parameter Determination and Ground-Motion Prediction for Earthquake Early Warning

2020 ◽  
Vol 110 (1) ◽  
pp. 345-356 ◽  
Author(s):  
Itzhak Lior ◽  
Alon Ziv
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Stress Drop ◽  
Seismic Moment ◽  
Source Parameter ◽  
Parameter Determination ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Empirical Relations

ABSTRACT Currently available earthquake early warning systems employ region-specific empirical relations for magnitude determination and ground-motion prediction. Consequently, the setting up of such systems requires lengthy calibration and parameter tuning. This situation is most problematic in low seismicity and/or poorly instrumented regions, where the data available for inferring those empirical relations are scarce. To address this issue, a generic approach for real-time magnitude, stress drop, and ground-motion prediction is introduced that is based on the omega-squared model. This approach leads to the following approximate expressions for seismic moment: M0∝RT0.5Drms1.5/Vrms0.5, and stress drop: Δτ∝RT0.5Arms3/Vrms2, in which R is the hypocentral distance; T is the data interval; and Drms, Vrms, and Arms are the displacement, velocity, and acceleration root mean squares, respectively, which may be calculated in the time domain. The potential of these relations for early warning applications is demonstrated using a large composite data set that includes the two 2019 Ridgecrest earthquakes. A quality parameter is introduced that identifies inconsistent earthquake magnitude and stress-drop estimates. Once initial estimates of the seismic moment and stress drop become available, the peak ground velocity and acceleration may be estimated in real time using the generic ground-motion prediction equation of Lior and Ziv (2018). The use of stress drop for ground-motion prediction is shown to be critical for strong ground accelerations. The main advantages of the generic approach with respect to the empirical approach are that it is readily implementable in any seismic region, allows for the easy update of magnitude, stress drop, and shaking intensity with time, and uses source parameter determination and peak ground motion predictions that are subject to the same model assumptions, thus constituting a self-consistent early warning method.

Too-Late Warnings by Estimating Mw: Earthquake Early Warning in the Near-Fault Region

2020 ◽  
Vol 110 (3) ◽  
pp. 1276-1288
Author(s):  
Mitsuyuki Hoshiba
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Strong Motion ◽  
Earthquake Early Warning ◽  
Motion Generation ◽  
Near Fault ◽  
Time Gap ◽  
Fault Region ◽  
Strong Ground

ABSTRACT Earthquake early warning (EEW) systems aim to provide advance warnings of impending strong ground shaking. Many EEW systems are based on a strategy in which precise and rapid estimates of source parameters, such as hypocentral location and moment magnitude (Mw), are used in a ground-motion prediction equation (GMPE) to predict the strength of ground motion. For large earthquakes with long rupture duration, the process is repeated, and the prediction is updated in accordance with the growth of Mw during the ongoing rupture. However, in some regions near the causative fault this approach leads to late warnings, because strong ground motions often occur during earthquake ruptures before Mw can be confirmed. Mw increases monotonically with elapsed time and reaches its maximum at the end of rupture, and ground motion predicted by a GMPE similarly reaches its maximum at the end of rupture, but actual generation of strong motion is earlier than the end of rupture. A time gap between maximum Mw and strong-motion generation is the first factor contributing to late warnings. Because this time gap exists at any point of time during the rupture, a late warning is inherently caused even when the growth of Mw can be monitored in real time. In the near-fault region, a weak subevent can be the main contributor to strong ground motion at a site if the distance from the subevent to the site is small. A contribution from a weaker but nearby subevent early in the rupture is the second factor contributing to late warnings. Thus, an EEW strategy based on rapid estimation of Mw is not suitable for near-fault regions where strong shaking is usually recorded. Real-time monitoring of ground motion provides direct information for real-time prediction for these near-fault locations.

End-to-end PGA estimation for earthquake early warning using transformer networks

2020 ◽  
Author(s):  
Jannes Münchmeyer ◽  
Dino Bindi ◽  
Ulf Leser ◽  
Frederik Tilmann
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Lead Times ◽  
Ground Shaking ◽  
Target Sites ◽  
End To End

<p>The key task of earthquake early warning is to provide timely and accurate estimates of the ground shaking at target sites. Current approaches use either source or propagation based methods. Source based methods calculate fast estimates of the earthquake source parameters and apply ground motion prediction equations to estimate shaking. They suffer from saturation effects for large events, simplified assumptions and the need for a well known hypocentral location, which usually requires arrivals at multiple stations. Propagation based methods estimate levels of shaking from the shaking at neighboring stations and therefore have short warning times and possibly large blind zones. Both methods only use specific features from the waveform. In contrast, we present a multi-station neural network method to estimate horizontal peak ground acceleration (PGA) anywhere in the target region directly from raw accelerometer waveforms in real time.</p><p>The three main components of our model are a convolutional neural network (CNN) for extracting features from the single-station three-component accelerograms, a transformer network for combining features from multiple stations and for transferring them to the target site features and a mixture density network to generate probabilistic PGA estimates. By using a transformer network, our model is able to handle a varying set and number of stations as well as target sites. We train our model end-to-end using recorded waveforms and PGAs. We use data augmentation to enable the model to provide estimations at targets without waveform recordings. Starting with the arrival of a P wave at any station of the network, our model issues real-time predictions at each new sample. The predictions are Gaussian mixtures, giving estimates of both expected value and uncertainties. The model can be used to predict PGA at specific target sites, as well as to generate ground motion maps.</p><p>We analyze the model on two strong motion data sets from Japan and Italy in terms of standard deviation and lead times. Through the probabilistic predictions we are able to give lead times for different levels of uncertainty and ground shaking. This allows to control the ratio of missed detections to false alerts. Preliminary analysis suggest that for levels between 1%g and 10%g our model achieves multi-second lead times even for the closest stations at a false-positive rate below 25%. For an example event at 50 km depth, lead times at the closest stations with epicentral distances below 20 km are 6 s and 7.5 s. This suggests that our model is able to effectively use the difference between P and S travel time and accurately assess the future level of ground shaking from the first parts of the P wave. It additionally makes effective use of the information contained in the absence of signal at other stations.</p>

A Real-Time Tsunami Inundation Forecast System Using Vector Supercomputer SX-ACE

2018 ◽  
Vol 13 (2) ◽  
pp. 234-244 ◽  
Author(s):  
Akihiro Musa ◽  
Takashi Abe ◽  
Takuya Inoue ◽  
Hiroaki Hokari ◽  
Yoichi Murashima ◽  
...  
Keyword(s):  
Real Time ◽  
Local Governments ◽  
High Performance ◽  
Polygonal Domain ◽  
Computational Domain ◽  
Xeon Phi ◽  
Forecast System ◽  
Tsunami Inundation ◽  
City Regions ◽  
Efficient Vector

Tsunami disasters can cause serious casualties and damage to social infrastructures. An early understanding of disaster states is required in order to advise evacuations and plan rescues and recoveries. We have developed a real-time tsunami inundation forecast system using a vector supercomputer SX-ACE. The system can complete a tsunami inundation and damage estimation for coastal city regions at the resolution of a 10 m grid size in under 20 minutes, and distribute tsunami inundation and infrastructure damage information to local governments in Japan. We also develop a new configuration for the computational domain, which is changed from rectangles to polygons and called a polygonal domain, in order to effectively simulate in the entire coast of Japan. Meanwhile, new supercomputers have been developed, and their peak performances have increased year by year. In 2016, a new Xeon Phi processor calledKnights Landingwas released for high-performance computing. In this paper, we present an overview of our real-time tsunami inundation forecast system and the polygonal domain, which can decrease the amount of computation in a simulation, and then discuss its performance on a vector supercomputer SX-ACE and a supercomputer system based on Intel Xeon Phi. We also clarify that the real-time tsunami inundation forecast system requires the efficient vector processing of a supercomputer with high-performance cores.

scholarly journals The Predictive Relationship between Earthquake Intensity and Tweets Rate for Real‐Time Ground‐Motion Estimation

2017 ◽  
Vol 88 (3) ◽  
pp. 840-850 ◽  
Author(s):  
Yelena Kropivnitskaya ◽  
Kristy F. Tiampo ◽  
Jinhui Qin ◽  
Michael A. Bauer
Keyword(s):  
Motion Estimation ◽  
Real Time ◽  
Ground Motion ◽  
Earthquake Intensity ◽  
Predictive Relationship
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