For the earthquake case study, USGS Did You Feel It? (DYFI) (Atkinson and Wald 2007, Wald and Dewey 2005, Wald et al. 1999, Wald et al. 2012), USGS Tweet Earthquake Dispatch (TED) (Earle et al. 2010, Earle et al. 2012), Quake-Catcher Network (Cochran et al. 2009a, Cochran et al. 2009b), NetQuakes, Earthquake Detective, and Quest for Quakes Challenge projects will be surveyed for how OI methods can help detect hazards, collect hazard impacts, deploy a network of low-cost sensors, and improve hazard models. This case study will inform the Powell Center Working Group for Earthquake Monitoring and potential risk projects like Watson’s risk project with Hawaiian Volcano Observatory (HVO) on Bridging Local Outreach and Seismic Signal Monitoring (BLOSSM), which proposes to deploy low-cost seismometers called Raspberry Shake (Anthony et al. 2018, Calais et al. 2009) in schools across Hawai‘i to engage youth in science and to increase public awareness of earthquake risk. In-person interviews and participant observations will be conducted at NEIC and HVO to understand their projects.
Did You Feel It? (DYFI) collects information from people who felt an earthquake and creates maps that show what people experienced and the extent of damage.
Atkinson, G.M., and Wald, D.J. (2007). “Did You Feel It?” Intensity Data: A Surprisingly Good Measure of Earthquake Ground Motion. Seismological Research Letters, 78: 362-368. https://doi.org/10.1785/gssrl.78.3.362.
Wald, D. J., and Dewey, J.W. (2005). Did You Feel It? Citizens Contribute to Earthquake Science. USGS Fact Sheet 2005-3016. https://doi.org/10.3133/fs20053016.
Wald, D.J., Quitoriano, V., Dengler, L.A., and Dewey J.W. (1999). Utilization of the Internet for Rapid Community Intensity Maps. Seismological Research Letters, 70(6): 680-697. https://doi.org/10.1785/gssrl.70.6.680.
Wald, D.J., Quitoriano, V., Worden, C.B., Hopper, M., and Dewey, J.W. (2012). USGS “Did You Feel It?” Internet-based Macroseismic Intensity Maps. Annals of Geophysics, 54(6). https://doi.org/10.4401/ag-5354.
TED harvests real-time tweets through a continuous connection to Twitter. The system applies a query parameter to reduce the stream to tweets that contain the keyword earthquake in several languages. The system also applies other data-cleaning techniques. It merges tweets, ordering them, accounting for duplicates and filling any data gaps. Data from aggregators — users who redistribute secondhand earthquake information — are removed from the dataset. For each tweet filtered by keyword, TED archives the creation time and text, the Twitter user location, the Twitter tweet ID, and the time the tweet appeared in the TED database. The system also uses the Yahoo Maps API Geocoding Service to estimate the latitude and longitude of the sender’s location, if provided. Around the clock, TED also ingests seismically derived earthquake information from the USGS’s near-real-time internal global earthquake stream. TED archives the earthquake time, region, magnitude and hypocenter (latitude, longitude and depth). It also records the source of the scientifically derived earthquake information. For earthquakes that have been verified using seismic instruments, TED subsequently sends out a tweet to followers with basic information about the earthquake.
The main advantage of mining citizen reports through Twitter is speed. Rapid tweet-based earthquake detection can potentially fill the gap between the time when an earthquake occurs and the time when seismically derived information becomes available.
TED detects two to three earthquakes a day, on average. Especially in regions with few seismometers, TED reports often come in before traditional seismic networks detect an earthquake, giving seismologists early warning. TED sometimes detects earthquakes entirely missed by USGS’s automatic processing system, thereby increasing the number of felt events known to the agency. In addition, the tweet text and attached images sometimes offer a rapid qualitative assessment of an earthquake’s impact.
Earle, P.S., Bowden, D.C., and Guy, M. (2012). Twitter Earthquake Detection: Earthquake Monitoring in a Social World. Annals of Geophysics, [S.l.], 54(6). http://dx.doi.org/10.4401/ag-5364.
Earle, P.S., Guy, M., Buckmaster, R., Ostrum, C., Horvath, S., and Vaughan, A. (2010). OMG Earthquake! Can Twitter Improve Earthquake Response? Seismological Research Letters, 81(2): 246-251. https://doi.org/10.1785/gssrl.81.2.246.
The USGS is working to achieve a denser and more uniform spacing of seismographs in select urban areas to provide better measurements of ground motion during earthquakes. These measurements improve our ability to make rapid post-earthquake assessments of expected damage and contribute to the continuing development of engineering standards for construction.
To accomplish this, we developed a new type of digital seismograph that connects to a local network via WiFi and uses existing broadband connections to transmit data to USGS after an earthquake. The instruments are designed to be installed in private homes, businesses, public buildings and schools with an existing broadband connection to the internet. Data from these instruments is transmitted to USGS after an earthquake, and can be viewed here.
Hawai‘i Island is home to Mauna Loa and Kīlauea, respectively the largest and most active volcanoes on the planet. The USGS Hawaiian Volcano Observatory (HVO) monitors, investigates, and assesses the hazards posed by these volcanoes since 1912. HVO uses real-time information from instruments to monitor thermal and visual changes, volcanic gas emissions, ground deformation, and seismic activity. However, most of these monitoring instruments are located along volcanic edifices and rift zones, far from heavily populated areas, such as Hilo, Kona, and Pāhoa. Increasing the coverage of monitoring stations on the island is financially impractical, due to the high cost of instrumentation. In recent years, publicly available data and technological advancements have given new momentum to citizen science initiatives. Data gathered by specialized instrumentation are reproducible by enthusiastic hobbyists, using readily available off-the-shelf components. We use emerging technology, such as the Raspberry Shake seismograph, to empower youth in a problem-based learning approach during a summer-long course. With guidance from HVO scientists, students essentially adopt the hazards mission of the USGS. Students not only aid in the volcano monitoring efforts on Hawai‘i Island, but also (1) take ownership of their own learning, (2) increase their capacity in STEM, and (3) engage the local community and address its needs.
When MyShake detects an earthquake, its network of phones records the shaking to collect valuable data.
MyShake delivers ShakeAlert™ across California: Earthquake alerts provided in partnership with USGS ShakeAlert™ and CalOES.
Quickly understand the impact of an earthquake: See damage and shaking reports submitted by other community members along with information from the USGS and other global earthquake authorities.
Share your experience: Felt an earthquake? Share your experience with fellow users and MyShake scientists.
Become a citizen scientist: Your device becomes an earthquake sensor and joins a smartphone network collecting valuable data.
Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can build community among schools, museums, and other learning environments to improve earthquake science awareness. The QCN is a seismic education network that utilizes strong-motion accelerometers to record nearby earthquake activity. As of June 2016, QCN is operated by the Southern California Earthquake Center (SCEC) and the Incorporated Research Institutions for Seismology (IRIS).
The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC). The volunteer computers monitor motion with vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever new strong motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes and which ones represent cultural noise (like doors slamming or trucks driving by).
Cochran, E.S., Lawrence, J.F., Christensen, C., and Chung, A. (2009a). A Novel Strong-Motion Seismic Network for Community Participation in Earthquake Monitoring. IEEE Instrumentation & Measurement Magazine, 12(6): 8-15. https://doi.org/10.1109/MIM.2009.5338255.
Cochran, E.S., Lawrence, J.F., Christensen, C., and Jakka, R.S. (2009b). The Quake-Catcher Network: Citizen Science Expanding Seismic Horizons. Seismological Research Letters, 80 (1): 26-30. https://doi.org/10.1785/gssrl.80.1.26.
Classifying seismic signals can be challenging because each signal is unique. Help us classify the seismograms into whether they contain a seismic signal or not. If they contain a seismic signal, help us classify whether the signal is from an earthquake or from tremor. This project aims to classify types of seismic events such as earthquakes or tremor, which are measured as motion of the surface of the earth. Raw seismic waves cannot be heard, so for this project we altered their frequencies to audible pitches. Volunteers can classify types of events just by listening to them. In classifying the seismic events, we would come to better understand the population of these seismic events and the conditions under which they occur.
In this citizen science project, we ask citizens to listen to relevant sections of seismograms that are converted to audible frequencies. Citizen scientists helped identify local seismic events whose recorded signals are much smaller than those associated with the surface waves that have triggered these local events. The local events include small earthquakes as well as tectonic tremor. While progress has been made in understanding how these events might be triggered by surface waves from large teleseismic earthquakes around the world, there is no consensus on its physical mechanism. The aim of our project is to engage the help of citizen scientists to increase general knowledge of triggered seismic events that may or may not occur during transient strain changes, such as from propagating surface waves. A better understanding of triggered seismic events is expected to provide important clues toward a fundamental understanding of how earthquakes nucleate and the physical mechanisms that connect different earthquakes and other slip events. From the volunteers’ classifications we determined that citizen scientists achieve a higher reliability in detecting earthquakes and noise than in detecting tremor or other signals and that citizen scientists more accurately identify earthquake signals than a trained machine-learning algorithm. For tremor classifications we currently depend entirely on humans as no machine has yet learned to detect triggered tremor.
The ”Quest for Quakes” challenge is managed by the NASA Tournament Lab established by NASA and the Crowd Innovation Laboratory at Harvard University in 2010 to create the most innovative, efficient and optimal solutions for specific, real-world challenges being faced by NASA researchers. The lab is using Appirio’s topcoder.com crowdsourcing service to host the challenge, which is open to the public and the more than 815,000 members of the topcoder community.
Data for the competition comes from the more than 65 terabytes of data collected from 125 sensors in California and 40 international sensors monitored by the QuakeFinder group, a humanitarian research and development project by Stellar Solutions, Inc., Palo Alto, California.
The data for this competition was provided by the QuakeFinder group, a humanitarian research and development project by Stellar Solutions, Inc., Palo Alto, California. QuakeFinder has 125 sensors in California and 40 sensor suites around the world. These ultra-low frequency magnetometers collect and transmit high-rate data to Stellar Solutions’ data center for management and evaluation. Over 65 terabytes of data have been collected from sensors along the San Andreas fault and other faults in California, Chile, Peru, Greece, Indonesia and Taiwan.
A new NASA challenge is looking for evidence to support a theory that electromagnetic pulses (EMP) may precede an earthquake, potentially offering a warning to those in the quake’s path. The “Quest for Quakes” two-week algorithm challenge seeks to develop new software codes or algorithms to search through data and identify electromagnetic pulses that may precede an earthquake. Some researchers have speculated such pulses originating from the ground near earthquake epicenters could signal the onset of some quakes.