Tag Archives: GPS

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.

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?


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.


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?


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.


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.


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


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.


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.