The Significant Earthquake Database is a global listing of over 5,700 earthquakes from 2150 BC to the present. A significant earthquake is classified as one that meets at least one of the following criteria: caused deaths, caused moderate damage (approximately $1 million or more), magnitude 7.5 or greater, Modified Mercalli Intensity (MMI) X or greater, or the earthquake generated a tsunami. The database provides information on the date and time of occurrence, latitude and longitude, focal depth, magnitude, maximum MMI intensity, and socio-economic data such as the total number of casualties, injuries, houses destroyed, and houses damaged, and $ dollage damage estimates. References, political geography, and additional comments are also provided for each earthquake. If the earthquake was associated with a tsunami or volcanic eruption, it is flagged and linked to the related tsunami event or significant volcanic eruption.

This dataset is composed of GPS stations (1 file) and seismometers (1 file) multivariate time series data associated with three types of events (normal activity / medium earthquakes / large earthquakes). Files Format: plain textFiles Creation Date: 02/09/2019Data Type: multivariate time seriesNumber of Dimensions: 3 (east-west, north-south and up-down)Time Series Length: 60 (one data point per second)Period: 2001-2018Geographic Location: -62 ≤ latitude ≤ 73, -179 ≤ longitude ≤ 25Data Collection - Large Earthquakes: GPS stations and seismometers data are obtained from the archive [1]. This archive includes 29 large eathquakes. In order to be able to adopt a homogeneous labeling method, dataset is limited to the data available from the American Incorporated Research Institutions for Seismology - IRIS (14 large earthquakes remaining over 29). > GPS stations (14 events): High Rate Global Navigation Satellite System (HR-GNSS) displacement data (1-5Hz). Raw observations have been processed with a precise point positioning algorithm [2] to obtain displacement time series in geodetic coordinates. Undifferenced GNSS ambiguities were fixed to integers to improve accuracy, especially over the low frequency band of tens of seconds [3]. Then, coordinates have been rotated to a local east-west, north-south and up-down system. > Seismometers (14 events): seismometers strong motion data (1-10Hz). Channel files are specifying the units, sample rates, and gains of each channel. - Normal Activity / Medium Earthquakes: > GPS stations (255 events: 255 normal activity): High Rate Global Navigation Satellite System (HR-GNSS) normal activity displacement data (1Hz). GPS data outside of large earthquake periods can be considered as normal activity (noise). Data is downloaded from [4], an archive maintained by the University of Oregon which stores a representative extract of GPS noise. It is an archive of real-time three component positions for 240 stations in the western U.S. from California to Alaska and spanning from October 2018 to the present day. The raw GPS data (observations of phase and range to visible satellites) are processed with an algorithm called FastLane [5] and converted to 1 Hz sampled positions. Normal activity MTS are randomly sampled from the archive to match the number of seismometers events and to keep a ratio above 30% between the number of large earthquakes MTS and normal activity in order not to encounter a class imbalance issue.> Seismometers (255 events: 170 normal activity, 85 medium earthquakes): seismometers strong motion data (1-10Hz). Time series data collected from the international Federation of Digital Seismograph Networks (FDSN) client available in Python package ObsPy [6]. Channel information is specifying the units, sample rates, and gains of each channel. The number of medium earthquakes is calculated by the ratio of medium over large earthquakes during the past 10 years in the region. A ratio above 30% is kept between the number of 60 seconds MTS corresponding to earthquakes (medium + large) and total (earthquakes + normal activity) number of MTS to prevent a class imbalance issue. The number of GPS stations and seismometers for each event varies (tens to thousands). Preprocessing:- Conversion (seismometers): data are available as digital signal, which is specific for each sensor. Therefore, each instrument digital signal is converted to its physical signal (acceleration) to obtain comparable seismometers data- Aggregation (GPS stations and seismometers): data aggregation by second (mean)Variables:- event_id: unique ID of an event. Dataset is composed of 269 events.- event_time: timestamp of the event occurence - event_magnitude: magnitude of the earthquake (Richter scale)- event_latitude: latitude of the event recorded (degrees)- event_longitude: longitude of the event recorded (degrees)- event_depth: distance below Earth's surface where earthquake happened (km)- mts_id: unique multivariate time series ID. Dataset is composed of 2,072 MTS from GPS stations and 13,265 MTS from seismometers.- station: sensor name (GPS station or seismometer)- station_latitude: sensor (GPS station or seismometer) latitude (degrees)- station_longitude: sensor (GPS station or seismometer) longitude (degrees)- timestamp: timestamp of the multivariate time series- dimension_E: East-West component of the sensor (GPS station or seismometer) signal (cm/s/s)- dimension_N: North-South component of the sensor (GPS station or seismometer) signal (cm/s/s)- dimension_Z: Up-Down component of the sensor (GPS station or seismometer) signal (cm/s/s)- label: label associated with the event. There are 3 labels: normal activity (GPS stations: 255 events, seismometers: 170 events) / medium earthquake (GPS stations: 0 event, seismometers: 85 events) / large earthquake (GPS stations: 14 events, seismometers: 14 events). EEW relies on the detection of the primary wave (P-wave) before the secondary wave (damaging wave) arrive. P-waves follow a propagation model (IASP91 [7]). Therefore, each MTS is labeled based on the P-wave arrival time on each sensor (seismometers, GPS stations) calculated with the propagation model.[1] Ruhl, C. J., Melgar, D., Chung, A. I., Grapenthin, R. and Allen, R. M. 2019. Quantifying the value of real‐time geodetic constraints for earthquake early warning using a global seismic and geodetic data set. Journal of Geophysical Research: Solid Earth 124:3819-3837.[2] Geng, J., Bock, Y., Melgar, D, Crowell, B. W., and Haase, J. S. 2013. A new seismogeodetic approach applied to GPS and accelerometer observations of the 2012 Brawley seismic swarm: Implications for earthquake early warning. Geochemistry, Geophysics, Geosystems 14:2124-2142.[3] Geng, J., Jiang, P., and Liu, J. 2017. Integrating GPS with GLONASS for high‐rate seismogeodesy. Geophysical Research Letters 44:3139-3146.[4] http://tunguska.uoregon.edu/rtgnss/data/cwu/mseed/[5] Melgar, D., Melbourne, T., Crowell, B., Geng, J, Szeliga, W., Scrivner, C., Santillan, M. and Goldberg, D. 2019. Real-Time High-Rate GNSS Displacements: Performance Demonstration During the 2019 Ridgecrest, CA Earthquakes (Version 1.0) [Data set]. Zenodo.[6] https://docs.obspy.org/packages/obspy.clients.fdsn.html[7] Kennet, B. L. N. 1991. Iaspei 1991 Seismological Tables. Terra Nova 3:122–122.

1) Data Introduction • The Global Earthquake Data is an earthquake observation dataset that provides detailed information about 1,137 earthquakes around the world, including 43 attributes including magnitude, location, time of occurrence, epicenter depth, and intensity.

2) Data Utilization (1) Global Earthquake Data has characteristics that: • This dataset consists of various geological, geographic, and temporal characteristics and metadata such as earthquake magnitude, depth, latitude and longitude, location, time and date of occurrence, felt, cdi and mmi, tsunami occurrence, and alert. (2) Global Earthquake Data can be used to: • Analysis of earthquake occurrence patterns and risk areas: Using data such as earthquake scale, location, and time of occurrence, it can be used to analyze the spatio-temporal distribution and risk areas of earthquakes around the world, and to prepare for disasters and to evaluate earthquake risk. • Development of earthquake prediction and classification models: Based on various seismic characteristics data, it can be used for geological research and practical application models such as machine learning-based earthquake prediction, classification, and impact analysis.

Click the link to connect to DOGAMI Digital Data Series for data downloads. The Oregon Seismic Hazard Database, release 1 (OSHD-1.0), is the first comprehensive collection of seismic hazard data for Oregon. This publication consists of a geodatabase containing coseismic geohazard maps and quantitative ground shaking and ground deformation maps; a report describing the methods used to prepare the geodatabase, and map plates showing 1) the highest level of shaking (peak ground velocity) expected to occur with a 2% chance in the next 50 years, equivalent to the most severe shaking likely to occur once in 2,475 years; 2) median shaking levels expected from a suite of 30 magnitude 9 Cascadia subduction zone earthquake simulations; and 3) the probability of experiencing shaking of Modified Mercalli Intensity VII, which is the nominal threshold for structural damage to buildings. The perceived shaking and damage potential maps and the probability of damaging shaking maps are intended to provide non-specialists with a qualitative way to assess earthquake hazards, and to see the variation of hazard across the state.

This dataset contains ground motion velocity and acceleration seismic waveforms recorded by the Southern California Seismic Network (SCSN) and archived at the Southern California Earthquake Data Center (SCEDC). A Distributed Acousting Sensing (DAS) dataset is included.

Earthquake

## Overview

Earthquake is a dataset for object detection tasks - it contains Enkaz Or Yol annotations for 229 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Historical earthquakes recorded by Earthquakes Canada. This serie is composed of 4 earthquake datasets. Each dataset contains the earthquakes grouped by decade; 1980-1989, 1990-1999, 2000-2009, 2010-2019. However, the National Earthquake Database makes available seismic bulletin data from 1985 and onward. For a complete listing of current and historical earthquakes, visit https://www.earthquakescanada.nrcan.gc.ca/.

The Global Earthquake Proportional Economic Loss Risk Deciles is a 2.5 minute grid of earthquake hazard economic loss as proportions of Gross Domestic Product (GDP) per analytical Unit. Estimates of GDP at risk are based on regional economic loss rates derived from historical records of the Emergency Events Database (EM-DAT). Loss rates are weighted by the hazard's frequency and distribution. The methodology of Sachs et al. (2003) is followed to determine baseline estimates of GDP per grid cell. To better reflect the confidence surrounding the data and procedures, the range of proportionalities is classified into deciles, 10 class of an approximately equal number of grid cells of increasing risk. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

This Dataset contains year, month, origin time, state, country and location wise Latitude, Longitude, Depth and Magnitude of Earthquake events occured in India and its surrounding countries namely Afghanistan, Bangladesh, Bhutan, Kyrgyzstan, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Seychelles, Tajikistan, Uzbekistan, China, Sri Lanka, Turkmenistan and Thailand

Notes: origin_time is India Standard Time (IST) Time Zone

Datasets contain records of 782 earthquakes from 1/1/2001 to 1/1/2023. The meaning of all columns is as follows:

• title: title name given to the earthquake
• magnitude: The magnitude of the earthquake
• date_time: date and time
• cdi: The maximum reported intensity for the event range
• mmi: The maximum estimated instrumental intensity for the event
• alert: The alert level - “green”, “yellow”, “orange”, and “red”
• tsunami: "1" for events in oceanic regions and "0" otherwise
• sig: A number describing how significant the event is. Larger numbers indicate a more significant event. This value is determined on a number of factors, including: magnitude, maximum MMI, felt reports, and estimated impact
• net: The ID of a data contributor. Identifies the network considered to be the preferred source of information for this event.
• nst: The total number of seismic stations used to determine earthquake location.
• dmin: Horizontal distance from the epicenter to the nearest station
• gap: The largest azimuthal gap between azimuthally adjacent stations (in degrees). In general, the smaller this number, the more reliable is the calculated horizontal position of the earthquake. Earthquake locations in which the azimuthal gap exceeds 180 degrees typically have large location and depth uncertainties
• magType: The method or algorithm used to calculate the preferred magnitude for the event
• depth: The depth where the earthquake begins to rupture
• latitude / longitude: coordinate system by means of which the position or location of any place on Earth's surface can be determined and described
• location: location within the country
• continent: continent of the earthquake hit country
• country: affected country

This dataset provides comprehensive information on significant earthquakes that have occurred around the world since 1900 with a magnitude of 5 or above. The data includes essential details such as location, date and time, magnitude, depth, and other relevant information about each earthquake.

The dataset is updated weekly and sourced from the United States Geological Survey (USGS), which maintains a global catalog of earthquake information. The dataset includes earthquakes from all regions of the world, from the most seismically active regions like the Pacific Ring of Fire to less active regions like Europe and Africa.

Earthquakes are natural disasters that can cause severe damage to property, loss of life, and environmental damage. The dataset can be used for various research purposes, including studying earthquake patterns and trends over time, examining the impact of earthquakes on human populations and infrastructure, and developing models to predict future earthquake activity.

Researchers can use the dataset to explore the characteristics of earthquakes such as their frequency, magnitude, and location. By analyzing this data, researchers can identify earthquake patterns and trends and use the information to develop better models to predict future earthquakes. This dataset is a valuable resource for researchers and scientists who study earthquakes and their effects on the environment and human life.

Here's an explanation of each column in the USGS earthquake data:

time: The time of the earthquake, reported as the number of milliseconds since the Unix epoch (January 1, 1970, 00:00:00 UTC). latitude: The latitude of the earthquake's epicenter, reported in decimal degrees. longitude: The longitude of the earthquake's epicenter, reported in decimal degrees. depth: The depth of the earthquake, reported in kilometers. mag: The magnitude of the earthquake, reported on various magnitude scales (see magType column below). magType: The magnitude type used to report the earthquake magnitude (e.g. "mb", "ml", "mw"). nst: The total number of seismic stations used to calculate the earthquake location and magnitude. gap: The largest azimuthal gap between azimuthally adjacent stations (in degrees). dmin: The distance to the nearest station in degrees. rms: The root-mean-square of the residuals of the earthquake's hypocenter location. net: The ID of the seismic network used to locate the earthquake. id: A unique identifier for the earthquake event. updated: The time when the earthquake event was most recently updated in the catalog, reported as the number of milliseconds since the Unix epoch. place: A human-readable description of the earthquake's location. type: The type of seismic event (e.g. "earthquake", "quarry blast", "explosion"). horizontalError: The horizontal error, in kilometers, of the location reported in the latitude and longitude columns. depthError: The depth error, in kilometers, of the depth column. magError: The estimated standard error of the reported earthquake magnitude. magNst: The number of seismic stations used to calculate the earthquake magnitude. status: The status of the earthquake event in the USGS earthquake catalog (e.g. "reviewed", "automatic"). locationSource: The ID of the agency or network that provided the earthquake location. magSource: The ID of the agency or network that provided the earthquake magnitude.

Global Earthquake Dataset

This dataset provides detailed information on 1,000 recorded earthquakes from around the world, offering 20 key attributes that capture crucial details of each seismic event. Whether you're exploring global earthquake patterns, building predictive models, or conducting geospatial analysis, this dataset provides a wealth of data for a wide range of analyses.

Dataset Features:

• ID: Unique identifier for each earthquake event.
• Magnitude: Richter scale value representing the earthquake's magnitude.
• Type: Type of seismic event (e.g., earthquake, explosion, etc.).
• Title: Brief title describing the earthquake.
• Date: The date when the earthquake occurred.
• Time: The exact time of the earthquake.
• Felt: Number of people who reported feeling the earthquake.
• Alert: Alert level issued (if any), such as green, yellow, orange, or red.
• Tsunami: Indicator of whether the earthquake triggered a tsunami (Yes/No).
• Depth (km): Depth of the earthquake, measured in kilometers.
• Latitude & Longitude: Geographical coordinates of the earthquake's epicenter.
• Place: The region or location closest to the earthquake’s epicenter.
• Location: Specific name of the area where the earthquake occurred.
• Continent: The continent where the earthquake was recorded.
• Country: Country of occurrence.
• Subnational: State or regional division within the country.
• City: Nearest major city affected by the earthquake.
• Locality: Smaller local area or community impacted by the earthquake.
• Postcode: Postal code of the locality.

Potential Use Cases:

• Geospatial Analysis: Visualize and analyze earthquake distributions globally.
• Seismic Activity Insights: Examine patterns and trends in earthquake occurrences by magnitude, depth, and geographical location.
• Natural Disaster Risk Assessment: Assess regions at higher risk for seismic activity and tsunamis.
• Predictive Modeling: Develop models to forecast future earthquakes or understand correlations between various features.
• Exploratory Data Analysis: Dive deep into specific locations or periods to gain insights into seismic events.

With comprehensive data from multiple regions and detailed attributes, this dataset is suitable for both academic research and data science projects related to natural disasters, geophysics, and hazard assessment.

U.S. Earthquake Dataset

This dataset contains earthquake events in the U.S. from January 1, 2020, to December 31, 2023. It inclucdes 3,009 sequences with 29,521 events across 3 magnitude types. The original data can be accessed via USGS Earthquake Search. The detailed data preprocessing steps used to create this dataset can be found in the TPP-LLM paper and TPP-LLM-Embedding paper. If you find this dataset useful, we kindly invite you to cite the following papers:… See the full description on the dataset page: https://huggingface.co/datasets/tppllm/us-earthquake.

The USGS Earthquake Hazards Program of the U.S. Geological Survey (USGS) is part of the National Earthquake Hazards Reduction Program (NEHRP) led by the National Institute of Standards and Technology (NIST).

The USGS role in NEHRP is to provide Earth sciences information and products for earthquake loss reduction. The goals of the USGS' Earthquake Hazards Program are: * Improve earthquake hazard identification and risk assessment methods and their use; * Maintain and improve comprehensive earthquake monitoring in the United States with focus on "real-time" systems in urban areas; * Improve the understanding of earthquakes occurrence and their effects and consequences.

This dataset can be used to provide and apply relevant earthquake science information and knowledge for reducing deaths, injuries, and property damage from earthquakes through understanding of their characteristics and effects and by providing the information and knowledge needed to mitigate these losses.

A seismic hazard model for South America, based on a smoothed (gridded) seismicity model, a subduction model, a crustal fault model, and a ground motion model, has been produced by the U.S. Geological Survey. These models are combined to account for ground shaking from earthquakes on known faults as well as earthquakes on un-modeled faults. Seismicity rates are estimated by counting historical earthquakes in a grid with a cell dimension of 0.1 degrees in latitude and longitude. These gridded earthquake rates are smoothed using a 50 kilometer fixed length smoothing kernel. Separate rate models were developed for the craton and active tectonic regions for earthquake depths between 0 and 50 km. Gridded rates for earthquakes with depths greater than 50 km were done separately.

In addition to displaying earthquakes by magnitude, this service also provide earthquake impact details. Impact is measured by population as well as models for economic and fatality loss. For more details, see: PAGER Alerts. Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cache-able relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cache-able.Update Frequency: Events are updated as frequently as every 5 minutes and are available up to 30 days with the following exceptions:

Events with a Magnitude LESS than 4.5 are retained for 7 daysEvents with a Significance value, 'sig' field, of 600 or higher are retained for 90 days In addition to event points, ShakeMaps are also provided. These have been dissolved by Shake Intensity to reduce the Layer Complexity.The specific layers provided in this service have been Time Enabled and include: Events by Magnitude: The event’s seismic magnitude value.Contains PAGER Alert Level: USGS PAGER (Prompt Assessment of Global Earthquakes for Response) system provides an automated impact level assignment that estimates fatality and economic loss.Contains Significance Level: An event’s significance is determined by factors like magnitude, max MMI, ‘felt’ reports, and estimated impact.Shake Intensity: The Instrumental Intensity or Modified Mercalli Intensity (MMI) for available events.For field terms and technical details, see: ComCat DocumentationAlternate SymbologiesVisit the Classic USGS Feature Layer item for a Rainbow view of Shakemap features.RevisionsAug 14, 2024: Added a default Minimum scale suppression of 1:6,000,000 on Shake Intensity layer.Jul 11, 2024: Updated event popup, setting 'Tsunami Warning' text to 'Alert Possible' when flag is present. Also included hyperlink to tsunami warning center.Feb 13, 2024: Updated feed logic to remove Superseded eventsThis map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to USGS source for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

For the San Diego region, using data from the Southern California Earthquake Data Center (SCEDC), we filtered events by latitude 32.715, longitude -117.1611 within a 150 km radius, focusing on earthquake events from August 1, 2004, 00:00:00 to August 1, 2024, 00:00:00. All magnitude types and depths were included, and 21 variables were feature-engineered to enhance predictive modeling. This dataset provides a robust foundation for earthquake prediction in the San Diego area, incorporating both raw seismic data and advanced engineered features.

Dl Earthquake

## Overview

Dl Earthquake is a dataset for classification tasks - it contains Earthquake Cities annotations for 295 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. Continuous data was acquired from February 2023 to the end of June 2023. The continuous data was then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy, during the acquisition period. A total of 67 events were selected from the Italian Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The waveform data was then further analyzed and annotated by analysts from INGV. Annotations include a pick time for the S and P wave, and an uncertainty level for the annotations.

The data consists of two datasets, one containing earthquake traces, the other containing noise-only traces. There are two folders: the dataset_earthquakes folder contains seismic traces; the dataset_noise folder contains noise-only traces.

The earthquake dataset consists of 328 3x25001 arrays, each related to a seismic event and with its own metadata. The dataset follows the Seisbench format, in which each trace follows the convention 'bucket0$trace_number;:n_dimensions;:n_samples', where 'bucket0' indicates the block to which the trace belongs; 'trace_number' indicates the trace' index within the block; 'n_dimensions' denotes the number of measurement axes; and 'n_samples' represents the number of samples in the trace. The waveforms are included in the the waveforms.hdf5 file of the earthquake_dataset folder, while the metadata is in the metadata.csv file in the folder. For each trace in the waveforms.hdf5 file there is an associated row in the metadata.csv file at the same index (indicated by 'trace_number' in the trace name).

The original miniSEED files that were analyzed by the INGV analysts are made available. They are contained in the miniseed_files folder. Each file name follows the format '_eventID_originTime_WS.POZA.Sx.DNy.MSEED' where eventID is the ID of the event that is recorded in the trace, originTime is the origin of the event in UTC time (expressed with the YYYY-MM-DDThh:mm:ss.ssssss format), x is a number that is used to identify the sensor that recorded the trace, y indicates the measurement direction of that trace, named '1', '2', 'Z'. For each trace in the waveforms.hdf5 file, the name of the miniSEED files that comprise the trace are in the metadata row for that trace, under the ‘trace_name_original_1’, ‘trace_name_original_2’, and ‘trace_name_original_Z’ fields in the metadata.csv file.

The dataset_noise folder follows the same convention. It contains a waveforms.hdf5 file with waveforms without seismic activity. The metadata_csv file has the metadata associated to each noise trace. The miniSEED_files_noise folder contains the original miniSEED files of the noise traces.

In 2024, a total of 1,374 earthquakes with magnitude of five or more were recorded worldwide as of December that year. The Ring of Fire Large earthquakes generally result in higher death tolls in developing countries or countries where building codes are less stringent. China has suffered from a number of strong earthquakes that have resulted in extremely high death tolls. While earthquakes occur around the globe along the various tectonic plate boundaries, a significant proportion occur around the basin of the Pacific Ocean, in what is referred to as the Ring of Fire due to the high degree of tectonic activity. Many of the countries in the Ring of Fire, including Japan, Chile, the United States and New Zealand, led the way in earthquake policy and science as a result. The impacts of earthquakes The tragic loss of life is not the only major negative effect of earthquakes, a number of earthquakes have caused billions of dollars worth of damage to infrastructure and private property. The high cost of damage in the 2011 Fukushima and Christchurch earthquakes in Japan and New Zealand respectively demonstrates that even wealthy, developed countries who are experienced in dealing with earthquakes are ill-equipped when the large earthquakes hit.