All posts by COMET2014

Permafrost dynamics in the remote Canadian Arctic revealed by high-resolution topographic measurements

Pablo J Gonzalez is a COMET researcher at the University of Liverpool.  He is currently (August 2017) carrying out a field campaign in the Canadian Arctic funded by the NERC British Arctic Office under the UK-Canada bursaries project Ice-landforms characterization due to permafrost dynamics around the Pingo National Landmark, Tuktoyaktuk, Northwest Territories, Canada.

Permafrost dynamics, related to ice aggradation and thawing, are effective climatic indicators.  Pingos (conical ice-cored hills, Fig. 1) concentrate large amounts of ice near the surface and, hence are highly sensitive systems to environmental changes.

Figure 1. Pingo National Landmark is a unique area with a high density of ice-landforms in Canada, hosting around 10% of pingos in the world. Credit: Wikipedia

Thus, the morphology and dynamics of pingos can be used to monitor regional effects of climate change over wide regions in the Arctic. However, we can identify two main difficulties in using pingos for environmental monitoring:

  1. The relationship of pingo morphology with its origin and permafrost conditions has not been established quantitatively.
  2. Monitoring pingo dynamics (growth, stability or collapse) has not been possible due to their small size (<300 m) and remote locations.

In this project, we aim to increase our understanding of pingo dynamics to fully exploit their potential as climate indicators. Here, we will apply novel methods to retrieve high spatial resolution (1-m) and very-high precision (<1m) topography based on satellite and drone technology.

The essential field work (late August 2017) is being carried out in collaboration with scientists from the Canada Centre for Remote Sensing, Natural Resources Canada (Dr. Yu Zhang and Dr. Sergey Samsonov), who are currently working in the area under the Polar Knowledge Canada project “Monitoring Land Surface and Permafrost Conditions along the Inuvik-Tuktoyaktuk Highway Corridor”.

This UK-Canada project will establish, for the first time, new and unique morphometric descriptions of a large number of pingos; conduct an exploratory analysis to establish links between current morphology with respect to genetics (origin), environmental conditions and stage of evolution; and unequivocally demonstrate the systematic decline, stability or growth of pingos in Tuktoyaktuk (Fig. 2), which could be linked to current climate change in the Western Canadian Arctic.

Figure 2. Location of Tuktoyaktuk, Northwestern Territories, Canada. Credit: Google Maps

 

Sentinel-1 satellites reveal pre-event movements and source areas of the Maoxian landslides, China

At about 5:38am local time on 24 June 2017 (21:38 on 23 June 2017 UTC), a massive landslide struck Xinmo Village, Maoxian County, Sichuan Province in China. Sichuan province is prone to earthquakes, including the 2008 Mw 7.9 Wenchuan earthquake that killed over 70,000 people, as well as the 1933 Mw 7.3 Diexi earthquake with a death toll of up to 9300.

Authorities have confirmed that the Maoxian landslide was triggered by heavy rain. The Maoxian landslide swept 64 homes in Xinmo village, blocking a 2km section of river and burying 1,600 meters of road. The collapsed rubble was estimated to be about eight million cubic meters (Figure 1a). Three days later (on 27 June 2017), a second landslide hit Xinmo Village; almost in the same time, another landslide occurred in Shidaguan Town, 20km away from Xinmo Village (Figure 1b).

Figure 1. The Maoxian landslides: (a) Xinmo Village (24 June 2017 local time); (b) Shidaguan  Town (27 June 2017 local time)

As part of the ESA’s Copernicus Program, the Sentinel-1 mission comprises a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging in all-weather conditions. Sentinel-1 images acquired before and after events such as landslides, earthquakes or volcanoes offer information on the extent and surface displacements of affected areas, which can be used for damage and future hazard assessment.

A joint team from Newcastle University (UK), Chengdu University of Technology, Tongji University, China Academy of Space Technology and Wuhan University (China) have been racing against time to respond these two events by combining ESA’s Sentinel-1, Chinese Gaofen-2/3 with field observations.

“Sentinel-1 acquired a post-event image thirteen and half hours after the Xinmo event, and provided us the first interferogram for the Xinmo landslide.” said Professor Zhenhong Li, Professor of Imaging Geodesy at Newcastle University, “This first Sentinel-1 interferogram, together with its corresponding coherence and amplitude maps, not only helped us identify the source area of this massive landslide, but also assisted with mapping the landslide boundary (Figure 2).”

Figure 2. The Xinmo landslide: (a) the first Sentinel-1 interferogram (20170612-20170624);  (b) UAV imagery. Note: (1) Red lines represent the landslide boundary derived from InSAR observations; (2) Yellow lines indicate relatively stable areas; and (3) Green and while circles imply the source area of this massive landslide.

“More importantly, through the analysis of the archived Sentinel-1 data, we found that pre-event movements exhibited in the source area during the period from 14 May to 19 June 2017 for the Xinmo event.”

GIF

Pre-event signals are even clearer for the Shidaguan landslide, suggesting it had been sliding for a while.

GIF

“It is well known that landslides are hard to predict” said Professor Qiang Xu, Geologist with Chengdu University of Technology, “This study convincingly demonstrates that InSAR can be used to detect and map active landslides, which is a great achievement.”

After presenting the joint research findings in the Dragon-4 symposium at Copenhagen on 27 June 2017, Professor Deren Li, Academician of Chinese Academy of Sciences (CAS) and Chinese Academy of Engineering (CAE) at Wuhan University concluded, “This joint effort suggests that landslide Early Warning System (EWS) might be encouraging. There are a range of factors we should consider for EWS, such as real time, automatic, spatial and temporal resolutions, and data uncertainty.”

 

Intrusive activity at Cerro Azul Volcano, Galápagos Islands (Ecuador)

Cerro Azul is the southernmost active volcano on Isabela Island, Galápagos (Ecuador). On 18-19 March 2017, seismic activity increased on the SE flank of the volcano.

On the same day, the Instituto Geofisico Escuela Politécnica National (IGEPN), the organisation responsible for the monitoring of Ecuadorian volcanoes, issued a warning for a possible imminent eruption.

The recorded seismicity was composed of volcano tectonic (VT) earthquakes, consistent with processes of rock fracturing, with the majority of the events having magnitude ranging between 2.4 and 3.  There were also sporadic events with magnitude up to 3.6 (see the second activity update released by IGEPN on 24 March).

The Sentinel-1 satellite acquired synthetic aperture radar data on 7  and 8 of March, prior to the onset of the seismic activity, and on 19 and 20 March, once seismicity started to exceed background levels both in terms of number of earthquakes and of energy release.

Applying SAR interferometric techniques (e.g. InSAR) showed significant deformation (up to 14 cm) in the region affected by the seismic swarm. More specifically, the InSAR data shows uplift at the southeastern flank of the volcano and contemporary subsidence centered at the summit of the volcano.

Sentinel-1 interferogram showing deformation caused by the magmatic intrusion as of 20 March 2017. Each color fringe corresponds to ~2.8 cm of displacement in the direction between the ground and the satellite.

COMET researcher Marco Bagnardi, working with the IGEPN, carried out a preliminary analysis of the InSAR data and observed that the deformation (at least as of 20 March 2017) can be explained by the intrusion of a 20-40 million cubic meters sill at a depth of ~5 km beneath the surface of the volcano.

Modelling results from the inversion of InSAR data. The proposed model is composed of a horizontal sill intrusion at ~5 km depth (black rectangle) fed by a deflating source at ~6 km depth (black star).

Such intrusion is likely to be fed by a 6 km deep reservoir, cantered beneath the summit of the volcano. The location of the intrusion well matches the location of the seismicity recorded by IGEPN.

Earthquake locations between 13 and 25 March 2017. Credit: IGEPN “Informe Especial Cerro Azul No. 2 – 2017”.

Marco Bagnardi said: “Within ten hours from receiving the warning from IGEPN, we were able to get hold of the most recent Sentinel-1 data for the area, process them to form differential interferograms, invert the data to infer the source of the observed deformation, and pass on the information to our Ecuadorian colleagues.”

The seismic activity seems to be continuing today.  IGEPN is currently proposing two possible scenarios for the evolution of this episode of volcanic unrest:

  • the intrusion could reach the surface and feed an effusive eruption in the coming days or weeks, as happened in 1998 and 2008; or
  • seismic activity and deformation could return to background level without the eruption of magma at the surface.

The next Sentinel-1 acquisitions will be on 1 and 2 April.  They will hopefully shed more light on the nature of the magmatic intrusion and on its evolution since 20 March.

 

 

 

 

Massive quake shifts perception of earthquake hazard models

Dramatic data from last year’s major earthquake in Kaikoura, New Zealand, will change the way scientists think about earthquake hazards in tectonic plate boundary zones.

The South Island earthquake was one of the most comprehensively recorded earthquakes in history.  Satellite images of the earthquake and its aftermath have enabled scientists to analyse the quake in an unprecedented level of detail.

COMET Director Tim Wright, study co-author, said: “We’ve never seen anything like the Kaikoura quake before, it was one of the most complex ever recorded.

“An earthquake commonly ruptures across a single fault line or faults that are closely grouped; Kaikoura ruptured at least 12 major faults. This challenges many assumptions about how individual faults control earthquake ruptures.”

Immediately following the start of Kaikoura’s earthquake in November, the Sentinel-1 and ALOS-2 satellites were tasked with gathering images and data.  COMET researchers at the University of Leeds used the data to provide a rapid analysis of what was happening during the quake.

They found that seismic readings of the earthquake were not giving accurate assessments of where the ruptures were occurring.

Professor Wright said: “Seismic readings are currently the fastest method of gathering earthquake data as they can quickly read shockwaves sent through the earth – but they paint a crude picture. The complexity of the Kaikoura earthquake caused seismologists to completely misinterpret the earthquake based on these seismic waves.

“Satellites such as the European Space Agency’s Sentinel-1 measure the way the ground deforms at very high resolution. The detail in the images showed us that ruptures took place across many separate faults.

“One of the aims of COMET is to expand satellite capability to provide rapid response earthquake data to ensure residents and rescue workers have access to fast and accurate information.”

The satellites provided pre- and post-earthquake images to measure the extent of land movement. Kaikaoura’s earthquake caused sections of earth to move up to 25 metres and created surface ruptures measuring 12 metres. This caused large scale landslides and triggered a tsunami.

Map of the South Island of New Zealand showing the ground deformation and location of the surface rupture from the November 2016 Kaikoura earthquake. The rainbow contours of ground motion are in 20 cm intervals whilst the red/blue colours show up to +/-6 m of motion near the fault. The sharp change in colour nearer the coast is where the fault has ruptured through to the surface.

In a study published today in Science, the team of researchers reports the full range of data analysed from Kaikaoura’s earthquake, including satellite imagery, field observation, GPS data and coastal uplift data.

The research will prompt reassessment of how many different faults can be involved in a single earthquake and could potentially feed into revaluations of seismic hazard models.

Lead researcher Dr Ian Hamling, a natural hazards geodesist from New Zealand research institute GNS Science, said: “There was growing evidence internationally that conventional seismic hazard models are too simple and restrictive.

“The message from Kaikoura is that earthquake science should be more open to a wider range of possibilities when rupture models are being developed. It underlines the importance of re-evaluating how rupture scenarios are defined for seismic hazard models.”

New Zealand’s complex network of faults are similar to those found in western United States, Japan and central Asia.

Leeds co-author and COMET Associate Dr John Elliott said: “While earthquakes like Kaikoura’s do not commonly occur, the data we’ve gathered from this event will expand our understanding of similar boundary zones around the world.

“Not only could the data help inform us for the future but it may change how we’ve interpreted ancient earthquakes. If an earthquake like Kaikoura’s took place thousands of years ago, current methods of paleo-seismology would possibly see it as a series of earthquakes over a long period of time, rather than as one large single quake.”

The full paper, ‘Complex multi-fault rupture during the 2016 M7.8 Kaikoura earthquake, New Zealand’ was published on 23 March in Science First Release.

Earthquake resilience in the East African Rift: the challenge of making baseline measurements

By COMET Scientist Juliet Biggs

Malawi is a small country at the tip of the East African Rift. Small, that is, until you spend several weeks driving around in loops over challenging roads.

Ake Fagereng (Cardiff), Hassan Mdala (Geological Survey) and I covered nearly 5000 km in less than three weeks. Why would we do this to ourselves? To establish a baseline geodetic network at the southern tip of the East African Rift.

We want to know how strain is distributed, and which are the active structures. This latest field season is just another piece in the tectonic jigsaw that we hope will ultimately lead to improving earthquake resilience in developing countries.

Our partner for the last few years has been the Geological Survey Department of Malawi – in particular, Hassan, who operates a small mobile office in the back seat of the car. From here, he fixes all our problems, often before we even realize they exist; making use of a boundless network of friends and family, all of whom seem to be just waiting to help with our latest peculiar request.

Each GPS station is composed of an antenna, a receiver, a car battery, a regulator, a solar panel and a metal pin that we drill into the rock. Most of the kit only stays for 4 days, but we’ll need to re-measure the position of the pin for many years to come. In our search for suitable sites, we met many enthusiastic head teachers, police chiefs and district commissioners and one rather terrifying headmistress, all of whom helped with security arrangements in schools or telecommunications masts.

At Mua Primary school, with the Head Teacher, Deputy Head and our GPS antenna
At Mua Primary school, with the Head Teacher, Deputy Head and our GPS antenna

Everywhere we went, there were spectacular landscapes and interesting geology, most of which we were forced to drive straight past. But so much of my research is done from satellites orbiting at 600 km that I can get a bit over-excited about getting my nose to the rock again, and we took advantage of what opportunity we had.

Malawi is largely composed of various types of gneiss, a high-grade metamorphic rock, with lots of migmatites, blobs that were once partially molten rock. Perhaps the most spectacular landscapes are the ‘inselbergs’, large granite intrusions, which now stick several thousand metres above the surrounding landscape. On our only day off, squeezed somehow into a hectic schedule, we hiked up the largest of these, Mulanje, for some truly spectacular views. We also found some fascinating faults, but you’ll have to wait for the paper to hear about those!

Hiking on Mulanje Massif. According to the Official Malawi Tourism site, the third largest mountain in Africa – a rather dubious statistic, but still steep enough to trouble my knees
Hiking on Mulanje Massif. According to the Official Malawi Tourism site, the third largest mountain in Africa – a rather dubious statistic, but still steep enough to trouble my knees

Many people ask about the personal challenges to fieldwork in Africa, and there are indeed plenty of those: insect bites, the side-effects of malaria medication, power cuts, cold showers, endless meetings getting permission, long days driving over roads covered with pot-holes, goats, dogs, chickens and bicycles, and on this occasion a car break-in (I’m wondering if there’s such a thing as a frequent flyer card for local police stations…?).

But none of that matters much; I love every minute (well, almost, I could skip the police stations). A great trip requires great company: Hassan, for whom every minor disaster is ‘a learning opportunity’, and Ake whose response to the next challenge is usually ‘well, that’ll be fun’.

Ake and Hassan contemplating a job well done. ‘Til next time.
Ake and Hassan contemplating a job well done. ‘Til next time

All I can say, is I learned a lot and had fun doing it, so I guess they are both right.  I simply feel privileged to spend time in a country with such interesting scientific questions, beautiful landscapes and warm, friendly people. I suspect we’ll find a way to come back before too long!

Our fieldwork was funded by Global Challenges Research Fund through an EPSRC Institutional Sponsorship Award to the University of Bristol; a BGS-COMET fieldwork award and a Researcher Mobility Grant from the World Universities Network.

Andy Hooper receives 2016 AGU Macelwane Medal

Congratulations to COMET scientist Professor Andy Hooper, who has been awarded the American Geophysical Union (AGU) James B. Macelwane Medal in recognition of his contributions to the geophysical sciences.

a.hooper

Established in 1961, the medal is given to outstanding early career scientists who have shown depth, breadth, impact, creativity and novelty in their research.

Professor Hooper, who is also Co-Director of the Institute of Geophysics and Tectonics at University of Leeds, pioneered the development of new software (StaMPS) to extract ground displacements from time series of synthetic aperture radar (SAR) acquisitions.  StaMPS is now used widely across the Earth Observation community.

He also discovered a new link between ice cap retreat and volcanism via geodetic monitoring from space and subsequent modelling of the 2010 Icelandic volcanic eruptions, and played a significant role in the €6m FUTUREVOLC project, leading the long-term deformation effort to integrate space and ground based observations for improved monitoring and evaluation of volcanic hazards.

Alongside other COMET researchers, he was part of a team contributing to the international scientific response to the earthquake which devastated Nepal in April 2015.

Most recently, working with colleagues from Iceland, he has shed new light on how volcanoes collapse during major eruptions, focusing on the 2014-15 eruption at Bárdarbunga.

Eruption column and lava flow from the air on 22 September 2014. Credit:Thórdís Högnadóttir
Eruption column and lava flow at Bárdarbunga, 22 September 2014. Credit: Thórdís Högnadóttir

Professor Hooper will be presented with the award at the 2016 AGU Fall Meeting, where he will also be giving a talk at the Union Session focusing on the new generation of scientists, where he will also be conferred an AGU fellow.

Congratulations Andy from all your colleagues at COMET.

Read more about the latest AGU awards

Iceland volcano collapse explained

COMET scientists have helped to shed new light on how volcanoes collapse during major eruptions, in new research published in Science.

The study, led by the University of Iceland, investigated a recent collapse at Bárdarbunga Volcano, Iceland, during the biggest volcanic eruption in Europe since the huge event at Laki in 1784.

Eruption column and lava flow from the air on 22 September 2014. Credit:Thórdís Högnadóttir
Eruption column and lava flow from the air on 22 September 2014. Credit: Thórdís Högnadóttir

The largest eruptions on Earth are commonly associated with collapse of the roof of a volcano into a magma chamber below. As they are infrequent, however – only five caldera collapses were recorded during the twentieth century – the processes involved are poorly understood.

The Bárdarbunga eruption, which lasted from August 2014 until February 2015, produced 1.5 km3 of basaltic lava.  In the course of the eruption, the top of the volcano caldera gradually sagged downwards, leaving an elongated bowl shaped depression over 13 km long and up to 65 m deep.

Bárdarbunga from the north, showing the main subsidence bowl within the caldera. Credit: Magnús T. Gudmundsson
Bárdarbunga from the north, showing the main subsidence bowl within the caldera. Credit: Magnús T. Gudmundsson

The total volume of the subsidence was 1.8 km3 – similar to the total volume of lava erupted and injected into the crust , implying a strong link between the two.

COMET scientist Marco Bagnardi said: “We saw the events at Bárdarbunga as an opportunity to better understand caldera collapse, and used multiple techniques to investigate.”

COMET researchers used radar data from satellites to measure ground deformation at Bárdarbunga’s caldera over a number of 24 hour periods.  As the topography continually changed, these data revealed movement of faults that reached to within a kilometre or so of the surface.

Combined with other techniques, including airborne altimetry, high precision GPS, seismology,  radio-echo soundings, and ice flow modelling, the results were used to create a detailed picture and timeline of how the caldera was collapsing and why.

From 16 August 2014 – before the eruption began – magma had been migrating out of a chamber 12 km below the ground, forming a fracture in the Earth’s crust.  Continued monitoring during the event showed that the magma was moving sideways from the volcano, beneath the surface, before finally erupting at Holuhraun, 47 km to the northeast, two weeks later.

Bardarbunga dike
The eruption in Holuhraun on its fourth day (3 September 2014). Credit: Thórdís Högnadóttir

Further analysis showed that a few days after the initial migration, the outflow of magma activated faults around the edge of the caldera leading to a series of earthquakes, which marked the beginning of the caldera collapse.  The collapsing roof then acted like a piston forcing even more magma out of the chamber below, which in turn led to further collapse.

COMET scientist Professor Andy Hooper explained: “Through modelling many different data sets, we were able to show that the caldera collapse was caused by magma leaving the reservoir, and this in turn squeezed more magma out of the reservoir, forming a positive feedback. This mechanism led to much more magma being erupted than would otherwise have been the case, which explains how eruptions on an even larger scale can occur.”

Overall, between 12 and 20% of the magma had left the magma chamber when the caldera collapse began.

Summarising the research, Prof Hooper added: “This work has given us real insight into caldera collapse, not only at Bárdarbunga but also at even larger eruptions.  What’s particularly interesting is how the collapse of the magma reservoir and the flow out of it clearly amplify each other.”

The paper, available now in Science, is Gudmundsson et al. (2016) Gradual caldera collapse at  Bárdarbunga volcano, Iceland, regulated by lateral magma outflow, doi:10.1126/science.aaf8988.

Coupling volcanic gas emission measurements to computational models of conduit gas flow

A new paper published in Geophysical Research Letters by Tom Pering and Andrew McGonigle has combined fluid dynamical modelling of gas flow in conduits with high time resolution measurements of volcanic gas discharge for the first time, revealing new insights into the dynamics of Stromboli volcano.

Their work is based on a recently developed approach using ultraviolet cameras which enable measurements of volcanic gas emission rates with unprecedented time resolution – around 1 Hz – such that gas release patterns associated with rapid explosive and non-explosive basaltic processes, can be resolved for the first time.

Data were captured on Stromboli, where an intriguing coda of lifetime on the order of 10s of seconds was identified following each explosion. Computational models were also developed to simulate the upward flow of conduit filling, so called “Taylor bubbles”, which are believed to be responsible for explosions on Stromboli when they burst at the surface.

The numerical models reveal the fissioning of smaller bubbles from the Taylor bubble bases to generate a train of “daughter bubbles”, thought to be responsible for generating the post-explosive coda upon arrival at the surface.

This process could play a primary yet hitherto unconsidered role in driving the dynamics of strombolian volcanism, both on Stromboli and other targets worldwide, with significant implications for the magnitude of resulting eruptions.

Combining models with field observations in this way shows considerable promise for improving our understanding of how gases drive volcanic activity.

The full reference is:  T.D Pering, A. J. S McGonigle, M. R James, G. Tamburello, A. Aiuppa, D. Delle Donne, M. Ripepe Conduit dynamics and post-explosion degassing on Stromboli: a combined UV camera and numerical modelling treatment, Geophysical Research Letters 2016 DOI: 10.1002/2016GL069001

Airborne volcanic ash detection using infrared spectral imaging

A new paper in Scientific Reports, co-authored by COMET’s Tamsin Mather, has demonstrated for the first time that airborne remote detection of volcanic ash is possible.

Airborne volcanic ash is a known hazard to aviation, but there are no current means to detect ash in-flight as the particles are too fine for on-board radar detection and, even in good visibility, ash clouds are difficult or impossible to detect by eye.

The economic cost and societal impact of the Icelandic eruption of Eyjafjallajökull generated renewed interest in finding ways to identify airborne volcanic ash in order to keep airspace open and avoid aircraft groundings.

The research, led by COMET Board Member Fred Prata, involved designing and building a bi-spectral, fast-sampling, uncooled infrared camera device (AVOID) to examine its ability to detect volcanic ash more than 50 km ahead of aircraft.

Experiments conducted over the Atlantic Ocean, off the coast of France involved an artificial ash cloud being created from a second aircraft, using ash from the Eyjafjallajökull eruption itself.

The measurements made by AVOID,  along with additional in situ sampling, confirmed the ability of the device to detect and quantify ash in an artificial ash cloud.  This is the first example of airborne remote detection of volcanic ash from a long-range flight test aircraft.

The full reference is Prata, A. J. et al. Artificial cloud test confirms volcanic ash detection using infrared spectral imaging. Sci. Rep. 6; doi: 10.1038/srep25620 (2016).

Detecting geohazards with GPS

COMET’s Marek Ziebart and Chris Atkins, both at UCL, have been using sidereal filtering to detect geohazards in near-real time.  This article describes how they have applied this to earthquakes in the Aleutian Islands, Alaska.

Geodetic quality GPS (Global Positioning System) data – fundamentally high precision range measurements made between orbiting spacecraft and tracking stations, often rigidly attached to bedrock – is one of the key observables available to us to analyse plate tectonics and the earthquake cycle. Such tracking stations now often make measurements at 1 Hz or higher. This enables us to calculate high rate position time series for the tracking stations, revealing various kinds of motion related to seismic waves and other forms of deformation.

 Figure 1: Deep braced GNSS tracking antenna at the Aleutian Islands, Cape Sarichef, Alaska (AV24-WestdahlNWAK2008). Photo: UNAVCO.org

Figure 1: Deep braced GNSS tracking antenna at the Aleutian Islands, Cape Sarichef, Alaska (AV24-WestdahlNWAK2008). Credit: UNAVCO.org

One such time series is shown below, calculated by a standard processing method called PPP (precise point positioning).  The plot shows the change in position in the north-south direction. When do you think the earthquake seismic waves begin to arrive?

Marek2

Figure 2: 25 minutes of the 1Hz GPS time series for AV24 – a deep braced GNSS antenna on the Aleutian Islands, Cape Sarichef, Unimak Island, Alaska at the time of the 2011 Tohoku earthquake (WestdahlNWAK2008)

GPS Seismology

How can we use this type of data? The time series in figure 2 shows an estimate of the GPS antenna movement in the North-South direction during the 2011 magnitude 9.0 Tohoku earthquake over a period of 25 minutes. This is a measurement of the ground shaking – the data is used to understand the nature of the earthquake and to compare it to other earthquakes in different places around the world. Earthquakes often cause a change in the shape of the ground that is retained after the earthquake is over, and which can be determined from a time series. This kind of information can be used to estimate the size of the earthquake, how much energy was released and tells us about the earthquake’s epicentre location.

Another kind of instrument, a seismometer, can be used to determine similar information. The drawback of seismometers is that they can ‘clip’ when the seismic waves become too large to handle. A GPS antenna, on the other hand, has no such limitation – no matter how big the amplitude of the seismic waves, they can still be measured using GPS technology. Because of this, GPS antennas such as the ones shown in figures 1 and 6 below are sometimes called broadband GPS seismometers. In practice we combine information from both GPS antennas and seismometers – they are complimentary. There are now thousands of such GPS antennas installed around the world, connected to the internet and taking measurements 24 hours a day.

Marek3

Figure 3: an example of a GPS tracking station network – this one is in the Gulf of Alaska and forms part of the US Plate Boundary Observatory (PBO)

So what’s the problem? The problem is Multipath – this is akin to radio interference where the signals transmitted by the orbiting satellites have more than one way of getting to the antenna. The strongest, direct signal – which is the one we want to measure – is distorted by signals bouncing off adjacent reflecting surfaces in the antenna’s vicinity. This causes spurious patterns of motion in the receiver time series. These can vary rapidly over a few seconds in a noise-like way (with amplitudes at the few mm level), or they can appear as a random walk – a slowly varying position error (with changes at the level of a few centimetres). Both these effects limit our ability to use GPS data for seismometry. To get some feel for the effect consider Figure 4.

On the day of the earthquake (shown as the black time series) the onset of the arrival of the largest seismic waves is shown clearly at around 6:03:00. A not dissimilar signature is also visible around 5:50:00. However, that same signature (an apparent ground movement of some 25mm over a few minutes, shaking and then reducing in magnitude) is visible at almost the same time on the previous day (in the grey time series). It doesn’t mean the earthquake was repeating – these are multipath distortions. They appear indistinguishable from seismic waves. Our problem, then, is how to remove the spurious, apparent motion from the time series, whilst retaining the actual signature of the earthquake. So what’s the solution?

Marek4

Figure 4: the GPS time series (North-South component) for station AV24 on the day of the earthquake and on the day before. The time series on the vertical axis are offset by 60mm for clarity

Sidereal Filtering

Sidereal filtering is a technique used to reduce errors caused by multipath in the positioning of GPS receivers. It relies upon the receiver remaining static from one day to the next relative to its surrounding environment and takes advantage of the ground track repeat time of the GPS satellites, which is just less than a sidereal day (usually around 23 hours, 55 minutes, 55 seconds). The repeating multipath error can thus be identified and largely removed from the following day by subtraction.

A conventional position-domain sidereal filter (PDSF) identifies and removes this repeating pattern from a position time series. However, the ground track repeat time of individual GPS satellites can differ from each other by a few seconds, whereas a PDSF has to assume that all satellites have the same repeat time. This can cause problems for the PDSF, especially when the oscillations caused by multipath interference are particularly high. We have developed an observation-domain sidereal filter (ODSF) that identifies and removes multipath errors from the GPS phase measurements themselves.

Unlike the PDSF, it can account for the fact that the ground track repeat times of the GPS satellites differ by a few seconds from one another. This means that it is more effective at removing the effects of high-frequency multipath error and is less sensitive to satellite outages. For each phase measurement, the ODSF algorithm searches for an appropriate correction based on the azimuth and elevation of the relevant satellite. That correction is derived from the measurement residuals on the previous day that most closely correspond to that azimuth and elevation. The precision of these measures of azimuth and elevation needs to be high – a hundredth of a degree or better. This is readily achievable in our algorithm.

Marek5

Figure 5: The same GPS time series for station AV24, but with the time series resulting from the two types of sidereal filter also shown. The time series on the vertical axis are offset by 50mm for clarity

Figure 5 shows the same 25-minute time series of 1 Hz displacements in northing as shown in Figure 4. Also plotted in red and green are the time series that result after applying the two types of sidereal filter: the PDSF and ODSF respectively. Notice that both the PDSF and the ODSF are largely successful at removing these oscillating errors. They reveal a surface wave arrival time of about 06:00 with the largest Love waves arriving at around 06:03. These events would not have been so easy to distinguish in the original unfiltered time series.

Marek6

Figure 6: Deep braced GNSS tracking antenna on the Aleutian Islands, Cape Sarichef, Unimak Island, Alaska (AV27-WestdahlSWAK2008). Credit: UNAVCO.org

Marek7

Figure 7: GPS time series for station AV27 including those resulting from the two types of sidereal filter. The time series on the vertical axis are offset by 50mm for clarity

Figure 7 similarly shows 1 Hz displacements at station AV27 which is only 11 km from AV24. One would therefore expect a very similar displacement signal. However, it is clear that the standard PPP time series, plotted in black and grey, are severely affected by strong short-period (~11 s) multipath error. In this case, the PDSF was unable to remove such a high-frequency error and instead increased the amplitude of these oscillations. The ODSF on the other hand was far more effective because it could take into account the differing repeat times of the satellites.

Conclusions

The precision of GPS technology has improved dramatically since 1995 when the system reached FOC (full operational capability). It is an effective tool for measuring geohazards and other geophysical phenomena in near real-time. However, data processing and interpretation must be handled carefully. Multipath effects on position time series can introduce spurious signatures that resemble seismic events and hamper our ability to exploit the technology. Sidereal filtering offers a very effective way to clean up GPS positioning time series to reveal more clearly geophysical events, effectively in real-time.