"Symbols show the few regions of the world where public citizens and organizations currently receive earthquake warnings and the types of data used to generate those warnings (7). Background color is peak ground acceleration with 10% probability of exceedance in 50 years from the Global Seismic Hazard Assessment Program." DOI: 10.1126/sciadv.1500036
“Symbols show the few regions of the world where public citizens and organizations currently receive earthquake warnings and the types of data used to generate those warnings (7). Background color is peak ground acceleration with 10% probability of exceedance in 50 years from the Global Seismic Hazard Assessment Program.” DOI: 10.1126/sciadv.1500036

This map stakes well the need for a decentralized earthquake warning system. The dark and light blue rings show where early earthquake warnings are available, while the reddish and yellow patches describe areas prone to earthquakes. There’s a readily visible disparity, which a team of scientists from the University of California, Berkeley, leverages to outline how early earthquake warnings can be crowdsourced. In a paper in Scientific Advances on April 10, the team proposes using the accelerometer in our smartphones to log and transmit tiny movements in the ground beneath us to a server that analyzes them for signs of a quake and returns the results (insert cute quote about crowdsourced information being used by the crowd).

This idea isn’t entirely new. In 2013, two seismologists from the Instituto Nazionale di Geosifica e Vulcanologia in Italy used cheap MEMS (micro-electromechanical system) accelerometers to determine that they’re good for anticipating quakes that are rated higher than five on the Richter scale if located close to the epicenter. Otherwise, the accelerometers weren’t reliable when logging seismic signals that weren’t sharp or unique enough – such as is the case with weaker earthquakes or the strong ground-motion associated with moving faults – because the instruments produced sufficient noise to drown their own readings out.

In fact, this issue might’ve been evident in 2010 itself. Then, a team out of Stanford University proposed using “all the computers” on the Internet to “catch” quakes. To be part of this so-called Quake Catcher Network, users would have to install a piece of QCN software along with a ‘low-maintenance’ motion sensor on their desktops/laptops to empower them with the same capabilities as a smartphone-borne accelerometer, but more sensitive. The software would log motion data due to mild tremors or stronger and strong ground-motion and relay it over the web in near-real-time. The QCN has been live for over a year now, although most of its users are situated in Europe and North America.

Perhaps the earliest instance of crowdsourcing in the Age of the Smartphone was with Twitter. In 2008, a 7.9-magnitude earthquake in China killed over 10,000 in a rain-hit region of the country. The CNN wrote, “Rainy weather and poor logistics thwarted efforts by relief troops who walked for hours over rock, debris and mud on Tuesday in hopes of reaching the worst-hit area”. Twitter, however, was swarming with updates from the region, often revealing gaps in the global media’s coverage of the disaster. The Online Journalism Blog summed it up:

Robert Scoble was following proceedings on his much-followed Twitter, and feeding back information from his followers, including, for instance (after he tweeted the fact that Tweetscan was struggling) that people were saying Summize was the best tool to use.

If you followed the conversation through Scoble using Quotably, you could then find Gregg Scott, who in turn was talking to RedChina, Karoli, mmsullivan, and inwalkedbud who was in Chengdu, China (also there was Casperodj and Lyrrael).

If you wanted to check out inwalkedbud you could do so using Tweetstats and find he has been twittering since December. Sadly the Internet Archive doesn’t bring any results, though.

The mainstream media had differing reports: RTE (Ireland) said “No major damage after China earthquake” – but UK’s Sky News reported four children killed and over 100 injured; Chinaview (China) said no buildings had collapsed – but an Australian newspaper said they had.

Filtering the noise

In all these cases – the Italian MEMS experiment, the QCN desktop/laptop-based tracker and with updates on Twitter – the problem has not been to leverage the crowd effectively. In 2015, we’re already there. The real problem has been reliability. Quakes stronger than five on the Richter scale signal danger everywhere, and there are enough smartphone-bearing users around the world to be on alert for them. But quakes less strong are bad news particularly in developing economies, where bad infrastructure and crowding are often to blame for collapsing buildings that claim hundreds of lives.

Let’s take another look at the disparity map:

"Symbols show the few regions of the world where public citizens and organizations currently receive earthquake warnings and the types of data used to generate those warnings (7). Background color is peak ground acceleration with 10% probability of exceedance in 50 years from the Global Seismic Hazard Assessment Program." DOI: 10.1126/sciadv.1500036
“Symbols show the few regions of the world where public citizens and organizations currently receive earthquake warnings and the types of data used to generate those warnings (7). Background color is peak ground acceleration with 10% probability of exceedance in 50 years from the Global Seismic Hazard Assessment Program.” DOI: 10.1126/sciadv.1500036

The redder belts are more prevalent in South America, Central and East Asia and in a patch running between Central Europe and the Middle East. Not being able to detect weaker quakes if not for centralized detection agencies in these regions keeps hundreds of millions of people under threat. So, the real achievement when scientists confidently crowdsource early earthquake warnings is the use of specialized filtering techniques and algorithms to increase the sensitivity of smartphones to subtle movements in the ground and so the reliability of their measurements. Where concepts like phase smoothing, Kalman filters and GNSS receivers thrumming in a smartphone’s chassis spell the difference between news and help.

Tech 1, Coarseness 0.

These are only some of the techniques in use – and whose use the Berkeley group thinks particularly significant in their early warning system’s designs. Phase smoothing is a technique where errors associated with data transmission between smartphones and satellites – such as measurement noise or reflection by metallic objects in the transmission’s path – are mitigated by keeping track of the rate of change of the distance between the phone and the satellite. A Kalman filter is an algorithm that specializes in picking out data patterns from a chaos of signals and using that pattern to fish for even more signals like it, thus steadily filtering out the noise. Together, they help scientists adjust for drift – which is when an object moves by a greater distance than an earthquake would have it move.

Finally, the scientists further refine the data by comparing it to legacy GNSS (Global Navigation Satellite System) data, which is the most accurate but also the most costly system with which to anticipate and track earthquakes. In their Science Advances Paper, the Berkeley group writes that the data obtained through thousands of smartphones “can be substantially improved by using differential corrections via satellite-based augmentation systems, tracking the more precise GNSS carrier phase and using it to filter the [crowdsourced] data (“phase smoothing”), or by combination with independent INS data in a Kalman filter.”

A warning system all India’s

But the best part: “Today’s smartphones have some or all of these capabilities”, negating the otherwise typical coarseness and unreliability associated with crowdsourced data. Here’s more evidence of this:

(B) Drift of position obtained from various devices (GNSS, double-integrated accelerometers, and Kalman filtering thereof) compared to observed earthquake displacements. DOI: 10.1126/sciadv.1500036
(B) Drift of position obtained from various devices (GNSS, double-integrated accelerometers, and Kalman filtering thereof) compared to observed earthquake displacements. DOI: 10.1126/sciadv.1500036

Chart (B), which is the one of interest to us, shows the amount of drift present in data acquired by various methods over time. The black lines show the observed displacements due to earthquakes of different magnitudes. So, a colored line represents reliable data as long as it is below the corresponding black line. For example, the red line for “C/A code + p-s + SBAS” shows a largely reliable reading of an M6 earthquake until about 50 seconds, after which it starts to drift. Similarly, most colored lines are below the black lines for M8-9 earthquakes, so all those methods can be used to reliably track the stronger earthquakes. The line described by the Berkeley group is the red line – the crowdsourced line.

The ideal thing would be to develop more sophisticated filtering mechanisms that’d bring the red line close to the blue GNSS line at the bottom, which of course exhibits zero drift. Fortunately, self-reliance on this front might be possible soon in the Indian Subcontinent region. Since 2013, the Indian Space Research Organization has launched four of its planned seven Regional Navigation Satellite System (IRNSS) that could augment regional efforts to crowdsource earthquake-warnings. The autonomous system is expected to live in 2016.