Earthquake Ontology and LOD
Hiroki Uematsu1,2,* , Hideaki Takeda1,212

National Institute of Informatics, JapanThe Graduate University for Advanced Studies, SOKENDAI, Japan

Abstract

In this paper, we constructed an earthquake ontology and Linked Open Data for seismology. Earthquake
ontology defines data on seismic waveforms such as seismic intensity and occurred time, data onobservation stations where seismic waveforms were observed, and classes and properties such as thesize and depth of the hypocenter of the observed waveforms. By using the earthquake ontology, it ispossible to assign URIs to "earthquakes" that cannot be observed unlike seismic sources and observed
waveforms, making it possible to use the necessary earthquake data based on observation information.
We developed the seismic dataset not only the world’s seismic data for machine learning represented by
STEAD but also data publicly available in Japan on a limited basis, such as JMA and NIED prevention, asLinked Open Data using earthquake ontology.

Keywords

Ontology, Linked Open Data, Earthquake, Seismology

1. Introduction
Japan is one of the most earthquake-prone countries in the world. Around the Japanesearchipelago, four plates collide with each other, and more than 100,000 earthquakes occur peryear, averaging more than 300 earthquakes per day, including those that are not felt.
Seismic motion is observed as waveform data of acceleration and is used in various researchsuch as calculation of seismic intensity, determination of hypocenter, emergency earthquake
warning, and predicted seismic intensity. In recent years, it has been used as training data forresearch using machine learning, such as predicting the seismic intensity at a specific observation
station, whether the observed waveform is a seismic waveform, and identifying the P-wave/Swave of an earthquake. Since machine learning requires a large amount of high-quality training
data, seismic observation networks are useful. However, one of the networks K-NET[1] which
was established by the National Research Institute for Earth Science and Disaster Resilience(NIED) waveform data acquisition site does not have an API, users need to specify the date andtime, hypocenter, observation station, etc., and download the waveform data. In order to search
for waveform data independently observed by researchers and observation networks of theJapan Meteorological Agency (JMA) and local governments, it is possible to create a databasethat aggregates waveform data. Although, since the waveform data cannot be republished andISWC 2023 Posters and Demos: 22nd International Semantic Web Conference, November 6–10, 2023, Athens, Greece
Corresponding author.
$ hiroki_u@nii.ac.jp (H. Uematsu); takeda@nii.ac.jp (H. Takeda)
 0000-0003-4215-3112 (H. Uematsu); 0000-0002-2909-7163 (H. Takeda)*© 2023 Copyright c 2023 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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there is no URI that uniquely points to the waveform data, researchers will have their own
databases, making it difficult to create a reusable open waveform database.

2. Earthquake Observation
In general, the word "earthquake" refers to events such as tremors felt by people on their own,
but in reality, it refers to the rapid displacement of the bedrock due to the pushing and pulling of
the underground bedrock. Shaking occurs as a result of bedrock displacement and is recognized
by us at the ground surface. Because earthquakes occur underground, it is difficult to actuallyobserve them. Therefore, information on the waveforms observed at each observation station isimportant, such as the hypocenter estimation and calculating the seismic intensity.
Observations of seismic activity are conducted in many countries. The International Federation of Digital Seismograph Networks (FDSN)[2] has 2196 registered seismograph networks
with 24-bit resolution with data recorded in continuous time series at a sampling rate of at least
20 samples/second are registered. STEAD[3] registers approximately 1.2 million time series ofseismic waveforms observed by seismometers, covering more than 19,000 hours of datasets.
In Japan, seismic waveforms observed by observation networks such as K-NET and Kiknet, which are based on data from observation stations established by NIED, JMA, and local
governments, can be obtained. However, although the acquired data can be used for analysis
and other purposes, it cannot be redistributed, and only some of the JMA’s observation stationand data are registered in the FDSN. Other earthquake data is available in the Earthquake
Monthly Report (Catalog Edition)[4]. Although the observed waveforms themselves cannot beobtained, they can be considered to contain metadata on the observed seismic motions.
However, there is no list of which stations observe which earthquakes, although multiple
stations must observe the same earthquake to be selected when searching by the station. In
addition, when searching from the hypocenter, it is not known whether the observation stations
observe the earthquake that occurred at that hypocenter or not without searching the data andmaking a list. Furthermore, since the observation stations are different from each other, it isdifficult to retrieve the seismograms of earthquakes that occurred at the same hypocenter frommultiple observation stations because the IDs are not assigned to each earthquake.

3. Method
To solve the problem in section 2, we aim to make the observed waveforms publicly available
and searchable in the form of Linked Data, which links data together. First, we organized thevocabulary related to earthquakes. The JMA’s Earthquake Monthly Report (Catalog Edition) does
not provide data on observed waveforms, but it does provide metadata on observed earthquake
ground motions. The data in the Earthquake Monthly Report (Catalog Edition) include source
data, measured data, first motion mechanism solution data, CMT solution data, seismic intensity
data, tsunami data, etc. In this paper, the seismic intensity data file was first selected as thetarget. The seismic intensity data contains a record called the hypocenter record, which contains
information on the hypocenter, and information on the observation points where the earthquake
motion that occurred at the hypocenter was observed. Therefore, we created an ontology as an

Figure 1: Earthquake Ontology (observedWave and hypocenter)

earthquake vocabulary based on the data contained in the Earthquake Monthly Report (CatalogEdition) of JMA. The seismic intensity data contains a record called the hypocenter record,
which contains information on the hypocenter, and information on the observation stations
where the earthquake motion that occurred at the hypocenter was observed.
Since the earthquake itself cannot be observed, it is important to show the relationship between the waveform information actually observed at the observation station and the hypocenterand magnitude estimated from the observed waveform as the semantics of the earthquake.
Figure 1 shows the hypocenter and observed waveforms graphically.
The earthquake ontology was constructed based on the hypocenter, seismic motion, observed
waveforms, and observation station that constitute an earthquake. The seismic motion andthe observation station that observe the waveforms at the ground surface were describedusing the SOSA (Sensor, Observation, Sample, and Actuator) ontology of the SSN (SemanticSensor Network)[5]. The earthquake ontology observation station class inherits from theSOSA:Sensor class, and seismicMotion and observedWave are set to the properties observed
from the observation station. In the earthquake ontology, the hypocenter is identified fromthe seismic waveforms observed by the stations, and the data set summarizing these three
relationships is intended to be captured as an earthquake.

4. Earthquake LOD
We converted the available data from the JMA’s Earthquake Monthly Report (Catalog Edition)
and FDSN earthquake events into Linked Data based on Earthquake Ontology. Since FDSN
includes observation networks registered with ISC and STEAD, data outside Japan are using
FDSN. Networks in FDSN are United States National Seismic Network, Hawaiian Volcano Observatory Network, Montana Regional Seismic Network, Southern California Seismic Network,
Nevada Seismic Network, Pacific Northwest Seismic Network - University of Washington, USGSNorthern California Seismic Network, Alaska Geophysical Network, Oklahoma Seismic Network,University of Utah Regional Seismic Network, Raspberry Shake, Alaska Volcano Observatory, Texas
Seismological Network, Puerto Rico Seismic Network & Puerto Rico Strong Motion Program, US
Geological Survey Networks, Lamont-Doherty Cooperative Seismographic Network, GeologicalSurvey Networks, National Tsunami Warning Center Alaska Seismic Network.
Table 1
Number of Hypocenters and Stations
Organization
FDSN
JMA

Hypocenters1602972100740Stations
60646
6795

Our earthquake LOD is available at seismic.balog.jp and can be searched through the SPARQL
endpoint (https://seismic.balog.jp/sparql). FDSN data since 1970 and JMA data from 1919 to2019 were collected and converted to LOD. Table 1 shows statistics of the Earthquake LOD.
Table 2
Number of hypocenters over magnitude 7YearNumber201910201833201731201642201523201419201323201224201144201027

Table 2 shows the estimated number of hypocenters observed since 2010 with a magnitudeof 7 or more. Although there are variations from year to year, we can see that 2011, when theGreat East Japan Earthquake occurred in Japan, had the largest number of hypocenters with amagnitude of 7 or higher.
Figure 2 shows the location of hypocenters of magnitude 7 or greater that have occurred since2010. It can be seen the hypocenters are located along the plate and that they are concentrated
in Japan. These data can be retrieved with the following SPARQL Query (Listing 1).
Listing 1: SPARQL Query1
2
3
4
5
6
7
8
9
10

PREFIX jpe: <https://seismic.balog.jp/ontology/jp-earthquake.ttl#>
SELECT year(xsd:dateTime(?origin)) COUNT(*) AS ?cnt WHERE {?s a jpe:hypocenter ;jpe:originTime ?origin ;jpe:magnitude ?mag .
FILTER(xsd:dateTime(?origin) > "2010-01-01T00:00:00"^^xsd:dateTime)FILTER(?mag >= 7)} GROUP BY year(xsd:dateTime(?origin))
ORDER BY DESC(year(xsd:dateTime(?origin)))5. Conclusion
In this paper, we organized earthquake data registered in the Earthquake Monthly Report from
JMA and data from FDSN published. We also created an ontology by organizing vocabulary
related to earthquakes and published it in the form of Linked Open Data by assigning URIs toearthquakes based on information on observed waveforms and hypocenters.
In the future, we aim to create an infrastructure for multiple observation networks byconverting the latest data published in the JMA’s seismic intensity database, data in NIED’sseismic observation network, and seismic data in FDSN into LOD. Furthermore, we will promote

Figure 2: Hypocenter map from 2010

the availability of an earthquake catalog format that can be used as earthquake data and learning
data, and LOD conversion of data observed by our own observation network. Benchmarking
based on the same dataset is important for source determination, calculation of predicted seismic
intensity, and training data for machine learning, but it is believed that datasets for reproduction
are not distributed due to the fact that Japanese data cannot be redistributed and IDs are notassigned. By using the earthquake ontology created in this paper to describe the datasets usedin earthquake research in LOD, it is expected that the availability of datasets for reconstruction
will be improved.

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