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Proc. 9th FIG Symp. on Deformation Monitoring, Olsztyn, pp 29-38. 29

DEVELOPMENT OF A MONITORING SYSTEM OF LANDSLIDE MOTIONS USING GPS H. Hartinger and F. K. Brunner Engineering Surveying and Metrology Technical University Graz Steyrergasse 30 8010 Graz, Austria Email: {hartinger, brunner}@aig.tu-graz.ac.at Internet: http://www.ivm.tu-graz.ac.at/ ABSTRACT In alpine regions natural catastrophes caused by landslides occur frequently. Often the devastating effects of landslides are increased by construction works. The aim of this project is the detailed investigation of landslides with the goal to discover possible precursors of mass movements. For this purpose a GPS based continuous monitoring system of landslide motions has been developed. A few stations are used to define a reference frame and have therefore to be placed in stable terrain such as bedrock. The remaining stations are the monitoring points situated in the deformation area. Each station consists of a GPS antenna, GPS receiver and data transmission unit. This paper reports about the development of the GPS hardware and the software of the monitoring system. The remaining difficulty in achieving subcentimetre position accuracy is the proper treatment of the systematic errors, mainly multipath. For near real-time systems this problem is crucial because of the low frequency characteristics of multipath. Several solution strategies have been investigated and the results shall be described. In a first practical application the system was used to monitor motions of a small landslide area in Styria, Austria. The results of these tests show that an accuracy of nearly ±2 mm in position can be achieved using the time-stacking method and a block-median filter length of 3 minutes.

1.

INTRODUCTION

Deformation experiments and GPS deformation monitoring applications have shown that GPS is capable to monitor subcentimetre deformations (e.g. Hartinger and Brunner, 1998). The main advantage of GPS sensors compared to conventional deformation monitoring sensors is that GPS requires no line-of-sight between the stations. This enables GPS to continuously monitor deformations even during unfavourable weather conditions such as rain, snow and fog. However, the attainable accuracy of a GPS based system is limited by the satellite geometry and systematic errors such as multipath. Weak satellite constellations usually amplify the systematic effects in the position results.

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Nevertheless, in many deformation monitoring applications the performance of a GPS based system is superior to standard systems. In this paper the development of the GPS hardware and the software of a continuously operating GPS deformation monitoring system, CODMS, is presented. The results of the first field test including the achievable accuracy are discussed. The CODMS is developed by us (IVM) as part of a research project for the International Decade of Natural Disaster Reduction (IDNDR) and funded by the Austrian Academy of Sciences.

2.

SYSTEM CONCEPT

2.1

System Requirements

The final goal of the IDNDR research project is the development of a CODMS capable to detect horizontal point motions with an accuracy of ±2 mm in near realtime, i.e. within a few minutes. Simultaneous GPS phase observations from a reference station and a rover station are (double) differenced in order to achieve this accuracy. This method cancels all clock errors. However, it assumes that deformations in the rover station can only be determined relative to a stable reference station. Therefore, it is an advantage to use a few stations for the definition of a local reference frame with the additional goal to control systematic effects. These stations have to be placed in stable terrain such as bedrock. The remaining stations are the monitoring points situated in the deformation area. Systematic errors such as multipath and antenna phase centre variations significantly deteriorate the attainable accuracy. GPS hardware with superior systematic error reduction is required to achieve subcentimetre accuracy in near real-time, e.g. GPS choke ring antennas reduce the effect of reflections from the ground (Solheim et al., 1996). In addition, certain quality parameters or observations which enable the modelling of systematic errors must be measured. We have shown that the Σ∆ model is able to reduce signal diffraction and multipath effects if the carrier-to-noise power ratio (C/N0) is used in combination with phase observations (Brunner et al., 1999). Even the impact of a weak satellite geometry can be improved by including low elevation satellites and using the C/N0 measurements to correctly weight these signals (Hartinger and Brunner, 1999). The CODMS developed by Bäumker and Fitzen (1998) mitigates systematic errors by time averaging the coordinate results. This method limits the real-time and even near real-time capability of deformation monitoring. Our approach is to model systematic errors with special observation and processing techniques. This strategy ultimately yields accurate GPS deformation estimates within a few minutes.

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Besides accuracy and real-time considerations, our CODMS should be generally applicable. It cannot be assumed that a perfect infrastructure exists to operate the GPS stations in landslide areas in alpine regions. Such applications require that the GPS stations operate autonomously. The hardware must function reliably and a low power consumption is essential. Moreover, all stations have to be controlled and configured from a central computer. 2.2

Hardware

All requirements discussed in section 2.1 influenced the design of our IVM CODMS. The development of the hardware system began in 1997 and the hardware components were selected at that time. Each GPS station consists of a choke ring antenna, a GPS receiver and a telemetry unit (data modem, controller and data transmitting antenna). All stations simultaneously transmit the GPS data on different frequencies to the central computer. This feature enables the determination of possible deformations in near real-time. The primary task of the central computer is the preparation of the GPS data for processing using the software GRAZIA and archiving the data on a hard disk. Beside that, all GPS stations can be configured from the central computer. Fig. 1 shows a schematic diagram of the IVM CODMS.

Fig. 1 Schematic diagram of the IVM CODMS.

L1 frequency receivers are used at the rover stations in the deformation area. L1/L2 frequency receivers are installed as reference stations in order to gain information from both frequencies about the GPS signal propagation errors and to allow appropriate modelling. Three different data transmission scenarios were considered: data transmission by cable, mobile telephone and radio. However, for data transmission in alpine areas only radio satisfies the general availability. The characteristic features of the IVM CODMS hardware are:

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a) GPS receiver:

Phase accuracy below 1 mm, C/N0 data, low power consumption, several serial data input and output ports

b) GPS antenna:

Choke ring antennas of the same type at all stations reduce phase centre variations, spherical radomes to protect the antenna against environmental influences.

Fig. 2 Choke ring antenna with spherical radome.

15 cm

c) Telemetry unit:

Fig. 3 Radio modem

45cm

Fig. 4 Four element yagi antenna.

Radio modem: UHF frequency band (430 MHz), controlled data transmission, simplex operation mode, channel selection capability.

Radio antenna: 4 element yagi antenna with direction gain of 10 dB.

The noise blocking of the radio modems due to a small channel separation is reduced by the use of 4 element yagi antennas. With these antennas the polarisation of the radio wave can be changed between horizontal and vertical polarisation for the different channels. As a consequence, the actual channel separation doubles.

The maximum data rate of the data modems is 9600 bps. This limits the data sampling rate to a maximum data frequency of 0.33 Hz for all GPS stations operating simultaneously. 2.4

Data Flow

The GPS data flow and the tasks of the hardware components of the IVM CODMS are schematically described in Fig. 5. The GPS system measures phase, code, Doppler and C/N0 information of the satellite signals. This data is sent to the GPS station modem. The modem prepares the data for transmission. Before the information is sent, the transmission channel is checked for readiness of the remote modem and for possible data collision errors. The radio antenna polarizes the signal either horizontally or vertically. After reception at the central computer the remote modem checks the transmitted data for consistency. The remote modem asks for the next data if no errors occurred. If an error is detected, then

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the incorrect data block is repeated until it is received correctly. The data management software WINREF receives the data via a serial port, stores it and prepares it for the GPS processing software GRAZIA. It is possible to check the receiver set-up, and if necessary to configure the GPS receivers from the central computer.

Fig. 5 Data flow of the IVM CODMS.

2.5

Software GRAZIA

GRAZIA is a Kalman filter based kinematic GPS software with several data modelling features for high accuracy performance. The original version was developed from GPSoft written by Dr. Jospeh Czompo at the University of Calgary. We have used GRAZIA since 1997 and a number of data cleaning and systematic error reduction models have been implemented in the meantime.

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Fig. 6 Data pre-processing, position and velocity estimation steps in GRAZIA.

Fig. 6 shows a diagram of the processing steps in GRAZIA. Data pre-processing is performed before the measurements are used in the Kalman filter to estimate the positions and velocities at every epoch. First, the transmitted GPS data is validated. Then the weights of the phase observations are calculated using the Σ∆ model. Cycle slips are detected and if possible repaired by a method based on triple differenced phase observations. Finally, the position and velocity states are estimated by the Kalman filter.

3.

FIRST PRACTICAL TEST

3.1

Description of the landslide area

In a first practical application, the CODMS was used to monitor motions of a small landslide in Styria, Austria. The landslide motions in this area are caused by a clay quarry. For this first test we used two L1/L2 and one L1 GPS receiver. The rover station (Rov1) was positioned one metre from the clearly visible main surface scarp. One reference station (Ref1) was situated in the stable area at the top of the hill and the second reference station (Ref2) in the valley. These three stations were aligned approximately along the principal deformation axis. The baseline length from Ref1 to Rov1 was 137.2 m with a height difference of 10.9 m and from Ref2 to Rov1 459.6 m with a height difference of 42.8 m, see Fig. 7. Data were sampled with a rate of 3sec for 65 hours. The cut-off angle was set to 5° in the GPS receivers.

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Fig. 7 Map of the landslide area in Styria. Reproduced with the permission of the Bundesamtes für Eich und Vermessungswesen, Zl. 70330/97.

3.2

Data pre-processing

As mentioned in Chapter 2 our strategy is to deal with systematic errors affecting the GPS signals before the computation of coordinates. The Σ models yields variances of GPS phase observations at the zero-difference level. These variances are used to calculate the weights of double-differenced phase observations according to the law of variance propagation. The Σ models are described in more detail in Brunner et al. (1999) and Hartinger and Brunner (1999). Finally, the GPS height and position results are estimated in the Kalman filter. Fig. 8 shows the improvements of the results of the baseline Ref1 – Rov1 using the Σ models. The epoch-to-epoch height values show many outliers if the results are calculated without the Σ∆ model. Considering a height increase of 8 cm in only 6 minutes a velocity of 80 cm/h would falsely be detected. We believe that such sudden changes in the GPS results are caused by GPS signal diffraction effects. For example, low elevation satellites are tracked by the receiver although the line of sight of these satellites is obstructed. After the signal strength falls below the threshold of the receiver, the GPS coordinates return to the appropriate value within one epoch. The advantage of the Σ∆ model is that this corrupted data is downweighted and the final result is free of the diffraction effect.

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Fig. 8 Epoch-to-epoch height variations (Ref1-Rov1) without systematic error modelling and with the Σ∆ model. The right hand side shows a detailed situation of one

However, multipath effects remain as dominant error source and limit the attainable near real-time accuracy of GPS. We have tried several hardware modifications and modelling techniques to reduce multipath. So far, the time stacking method appears to be the most effective technique to reduce the multipath effect, Brunner (1995), Genrich and Bock (1992). Multipath is mainly a function of the satellite position relative to a reflecting surface. As the satellite positions change during a day, the multipath effect shows up as a time varying effect in the results. However, after one sidereal day the position of the satellite repeats itself and thus a very similar multipath effect is observed. Taking the difference between the results from two consecutive sidereal days (time-stacking) most of the low frequency coordinate oscillations are eliminated, see Fig. 9. The application of time-stacking eliminates low frequency multipath oscillations, and thus the velocity (between two consecutive days) of the monitoring points can be estimated more accurately. The time-stacking procedure as used in our data analysis is described in Hartinger and Brunner (1998).

Fig. 9 Time series of epoch-to-epoch north component variations and power-spectra of day1 and day2 and after time-stacking (day2 –day1).

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Considering the landslide movements, velocity is actually of main interest. This makes time-stacking a valuable analysis tool for the detection of possible precursor of landslide motions. 3.2

Deformation analysis

The data of the first test of 72 hours duration was processed using the above described procedures. Fig. 10 shows the calculated time series of deformations and velocities along the principal deformation axis and Fig. 11 shows the variations of the GPS height differences. The epoch-to-epoch position results are contaminated by noise from various sources. The maximum position error is 15 mm. The multipath effect is significantly reduced by averaging position values over one hour for which the maximum position error is now 1.5 mm. This is an improvement by a factor of 10 as compared to the epoch-to-epoch results. The RMS of the hourly mean values improves by a factor of about 4 which indicates strong correlations between the original 3sec data. Time-stacking removes most of the multipath effect. As a consequence the change of positions during a sidereal day is obtained. The maximum point velocity error is about 2.0 mm/24h. Currently, this value seems to be the limit for the detection of horizontal deformations with a GPS CODMS in near real-time. Post-processing the timestacked results with a low-pass filter improves the detectable velocity even below the mm level.

Fig. 10 Calculated deformations along the principal deformation axis. 1st row: epoch-to-epoch results, 2nd row: one hour mean values and 3rd row: velocity / 24 hours.

Compared to the position results the accuracy of the GPS height is decreased by a factor 3-4. The maximum epoch-to-epoch height error is 40 mm, and the one hour mean height values have a maximum error of 4 mm. Similar to the position results, this is an improvement by a factor of 10 compared to the epoch-to-epoch

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results. Time-stacking the epoch-to-epoch height results indicates a maximum velocity error of 7.0 mm/24h.

Fig. 11 Calculated height deformation. 1st row: epoch-to-epoch results, 2nd row: one hour mean values and 3rd row: velocity / 24 hours.

Acknowledgements This work has been supported by the Austrian Academy of Science by the IDNDR research grant 10/99. References Bäumker M and HP Fitzen (1998) High precision slow motion monitoring with low cost GPS receivers in real time. In: Kahmen H, Brückl E, Wunderlich T (eds) Geodesy for Geotechnical and Structural Engineering. Proc. IAG Special Commission 4 Symposium Eisenstadt, pp 337-343 Brunner FK (1995) Kontinuierliche Deformationsvermessung mit GPS, In Altan und Lucius (Eds) Proc. Neue Technologien in der Geodäsie, TU Istanbul, pp 2536 Brunner FK, Hartinger H, Troyer L (1999) GPS signal diffraction modelling: the stochastic SIGMA-∆ model, Journal of Geodesy 73: 259-267 Genrich J, Bock Y (1992) Rapid resolution of crustal motion at short ranges with the global positioning system, Journal of Geophysical Research 97: 3261-3269

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Hartinger H, Brunner FK (1998) Experimental detection of deformations using GPS. In: Kahmen H, Brückl E, Wunderlich T (eds) Geodesy for Geotechnical and Structural Engineering. Proc. IAG Special Commission 4 Symposium Eisenstadt, pp 145-152 Hartinger H, Brunner FK (1999) Variances GPS Phase Observations: the SIGMAε model. GPS Solutions 2/4: 35-43 Solheim F, Alber C, Exner M, Rocken C, Meertens C, J. Johnson (1996) An Improved GPS Geodetic Antenna. Internet: http://www.unavco.ucar.edu/science_tech/technology/publications/geo_antenna/a ntenna-1.html