4 new publications in "The Power of Citizen Seismology: Science and Social Impacts"

Several RISE papers (some of them co-funded by the Turnkey project) dealing with crowdsourced data and public earthquake communication have recently been published. Four papers are part of the special issue of the open-access online Frontiers journal “The Power of Citizen Seismology: Science and Social Impacts” edited by Rémy Bossu, Kate Huihsuan Chen and Wen-Tzong Liang.

These four RISE papers published in the Frontiers journal cover interactions with the public, how seismological community can benefit from such interaction for rapid situation awareness and a method to speed-up seismic locations of felt earthquakes. You can find all papers here: 

Are you interested in more RISE publications? Here you will find the list with all our publications.


*The image for this news item is adapted from: Bossu et al. (2020), "Rapid Public Information and Situational Awareness After the November 26, 2019, Albania Earthquake: Lessons Learned From the LastQuake System"



A closer look #2: What is Operational Earthquake Forecasting?

One important focus of RISE is to advance earthquake predictability research such as Operational Earthquake Forecasting. This research can benefit from the constantly evolving observational capabilities of seismic monitoring efforts, which, for instance, result in an ever-increasing amount of recorded earthquakes, especially toward smaller magnitudes. Such capabilities need to be exploited to gain more insight into the earthquake occurrence processes and, therefore, to improve earthquake forecasting.

In our first step, we explore existing high-resolution earthquake catalogues that contain events with magnitudes down to ML0 or below. We started to develop an interactive tool that will facilitate and aid us in a more intuitive analysis of seismicity in five dimensions (see Figure).

In particular, we will focus on these aspects:

  1. Spatio-temporal variability in the frequency-magnitude distribution: e.g., statistical analyses of event sizes could tell us more about the state of a fault system.
  2. Earthquake clustering properties: e.g., well-located hypocenters could reveal how earthquake sequences progress and how earthquakes are triggered.
  3. Foreshock analysis: e.g., earthquakes prior to a larger earthquake might share a common spatial-temporal pattern. In addition, high-resolution catalogues could potentially reveal many more sequences that have foreshocks than is currently believed.
  4. Limits of the current quality of earthquakes catalogs, e.g., what information are we missing?

We will adopt state-of-the-art methods (e.g., from the machine learning domain) to augment these analyses, for instance, to employ a parameter selection and search for signals and patterns that are indicative of the earthquake occurrence process.

Our findings will have an impact on improving our understanding of the earthquake occurrence process. Our gained knowledge could allow us to develop innovative earthquake forecasting models, which can be stochastic, physics-based and/or of a hybrid type. Ultimately, our advances will contribute to mitigating better the seismic risk, which will be analysed within another work package of RISE.


A closer look #1: Towards optical sensing of ground motion for improved seismic hazard assessment

Optical fibres are the backbone of our modern communication network. Short pulses of laser light transmit enormous amounts of data, but on their journey from sender to the receiver, they also gather information about the optical fibre itself. In fact, microscopic displacements of the fibre slightly distort the laser pulses – an effect that has recently become detectable with highly sensitive interferometers.

This emerging technology, known as Distributed Acoustic Sensing (DAS), allows us to measure ground motion excited by a large variety of sources, such as earthquakes or landslides. Harnessing existing networks of telecommunication fibres, DAS, therefore, offers the opportunity to assess and potentially mitigate natural hazards in densely populated urban areas.

To explore this opportunity, RISE researchers at ETH Zurich are conducting a pilot experiment in the Swiss capital Bern, closely collaborating with the telecommunication company SWITCH. Several connected telecommunication fibres are traversing the city in different directions along with a 6 km long path measure ground motion every two metres, in real-time, nearly 1000 times per second. Most of the observed ground motion is caused by traffic, industrial installations, and construction sites.

Though the amplitude of these signals is, fortunately, much lower than the ground motion caused by destructive earthquakes, this wealth of data can be used to infer rock properties of the upper tens to hundreds of metres of the subsurface. Knowing these properties is essential to predict the ground motion caused by potential future earthquakes.

Research on DAS in urban environments is in its infancy, within the RISE project and worldwide. Initial results are very promising, especially in terms of the quality and unprecedented spatial resolution of the data. Yet, substantial research and development are still needed to process the enormous amounts of DAS data efficiently.


Detect earthquake-triggered landslides via Twitter

Detect earthquake-triggered landslides via Twitter

Landslides may significantly hamper earthquake response, because they can block roads. However, there is very little information about how, where and when earthquakes triggered landslides within the first few hours after a global earthquake occurred. Therefore, the European-Mediterranean Seismological Centre (EMSC) started a project in collaboration with the Qatar Computing Research Institute (QCRI) to evaluate whether it is possible to detect such landslides by monitoring tweets on Twitter in real time. 

Although such detection already exists for earthquakes at EMSC, the detection of landslides presents a different challenge, as the number of tweets published on this topic is very small. In order to tackle this issue, EMSC is working together with QCRI, which has developed AIDR (Artificial Intelligence Disaster Response). This is a platform, which harvests tweets from Twitter and uses artificial intelligence (AI) to analyse the pictures. A database of landslide pictures was the first training dataset generously provided by the British Geological Survey. Further AI training is currently ongoing. Besides that, we are manually checking all detected tweets on a daily basis and hope to have an operational prototype in the coming months.


Promising Kick-Off Meeting

Promising Kick-Off Meeting

The RISE kick-off meeting took place from 2 to 4 September 2019 in Zurich, Switzerland. Over 60 participants joined to discuss the project's first steps. Besides many interesting presentations held project members and external experts, a poster fair and breakout sessions provided enough room to be creative and to take everyone's expertise and ideas into account. During the conference dinner on a boat on Lake Zurich, the participants had the possibility to network and discuss in a different setting. Now the RISE community is ready to start with their work to make Europe more resilient.


RISE kick-off meeting in Zurich

RISE kick-off meeting in Zurich

From 2 to 4 September 2019, the RISE kick-off meeting will be held in Zurich. Over 60 representatives of the scientific community are expected to attend. The event will feature several poster sessions, breakout sessions, and a networking event to support the projects collaboration.

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