Evaluation of Seismic Data acquired with a Streamer on the Jakobshavn Glacier, Greenland

Transcription

1 Evaluation of Seismic Data acquired with a Streamer on the Jakobshavn Glacier, Greenland Edil A. Sepulveda Carlo, Jose Velez, Anthony Hoch, and George Tsoflias University of Kansas 2335 Irving Hill Road Lawrence, KS Technical Report CReSIS TR 130 July 30, 2007 This work was supported by a grant from the National Science Foundation (#ANT ).

2 Abstract Seismic data was acquired on Jakobshavn Glacier in Greenland using a snowstreamer and individually-planted geophones, as the control data. The streamer and control data were reformatted and processed, and the quality of the streamer data was evaluated and compared with the control using trace to trace comparisons produced in MATLAB. Also the seismic equipment used in Greenland was tested in the University of Kansas campus to evaluate hardware performance. The results demonstrated that the snowstreamer is a reliable tool to conduct seismic studies through ice at the polar regions. The results are a step forward for the deployment of a longer streamer to gather precise seismic data for the mapping of the subsurface of Jakobshavn Glacier in Greenland. Introduction The warming of the Earth by the greenhouse gases is affecting glaciers all over the world, including those in the polar regions. The glaciers are melting faster than predicted and predictions for the near future anticipate an increase in the rate of global average sea-level rise (IPCC, 2007). It is important to study how rapidly glaciers are melting and to understand mass balance for a better prediction of how much sea-level will rise in the future decades, and alert the population at risk. Mass balance is the difference between the input, or accumulation, and the output, or ablation. When the mass balance is positive, more input than output, the glacier will grow. When the mass balance is negative, more outgo than income, the glacier will shrink (Glacier, 2007). According to scientists this is what is happening to the vast majority of the glaciers throughout the world, they are shrinking. Western Antarctica and Greenland have been studied extensively because of the potential impact that they present to sea level rise. If Greenland were to melt completely, sea-level would rise 7 meters (IPCC, 2007). The Jakobshavn Glacier, located in western Greenland, was the site chosen to conduct an ongoing seismic study. Jakobshavn Glacier, also known as Jakobshavn Isbrae, is the fastest flowing glacier in the world. Scientists were surprised when they found out that it doubled its speed between 1997 and 2003

3 ( The motion of this glacier is estimated at 40 meters per day or almost 15 kilometers per year (Maas, 2006). Seismic imaging is a suitable method to identify ice thickness and the layers above and below the base of the ice. With this information, glaciologists can then understand the movement and melting rate of this glacier, and many others in Greenland and Antarctica. For this reason, geophysicists at CReSIS, the Center for Remote Sensing on Ice Sheets, are researching different methods to efficiently acquire seismic data in the polar regions. Conventional seismic methods, as individually-planted geophones, are time-consuming and labor intensive. A snowstreamer, an array of geophones towed over ice, has been developed at CReSIS for the acquisition of seismic data at Jakobshavn Glacier, in Greenland. Background Geophysics is the measurement of contrasts in the physical properties of materials beneath the surface of the Earth and the attempt to deduce the nature and distribution of the materials responsible for these observations. Variations in elastic moduli and density cause seismic waves to travel at different speeds through different materials. By timing the arrivals of these waves at surface observation points, we can deduce a great deal about the nature and distribution of subsurface bodies (Burger, 2006) like, in this case, the recognition of the internal layers of the ice, the bedrock, and the internal layers of the bed. Seismic streamers were originally designed for marine studies, but they have been proven to work reasonably well in other environments on land and snow. What worries geophysicists about the use of streamers for seismic studies in general, are the quality of the geophone coupling to the ground and the level of ambient noise that they can display (Eiken, 1989). These problems are the ones that haven t allowed streamers to become common tools in recording seismic data in polar environments. 2

4 Objectives The objective of this project is to evaluate the quality of streamer data acquired in May 2007 on Jakobshavn Glacier in Greenland. To accomplish that we: Process the seismic data using commercially available software and by writing MATLAB code. Determine whether streamer data is comparable to the control data gathered using individually-planted geophones. Determine how much wind noise affects the quality of the data acquired by the snowstreamer. Determine the best streamer setup for optimum data quality. Interpret the data and identify the bedrock and other internal layers of the ice and bedrock. Evaluate if the snowstreamer can be used to identify the bed, internal ice layers and other geologic features beneath the bed. Test the seismic equipment used in Greenland to evaluate hardware performance. What is a streamer? A streamer is a set of geophones joint together by cables used to record seismic data. It is a useful tool because it can facilitate the gathering of seismic data. The streamer constructed for the seismic research on Jakobshavn Glacier in Greenland consisted of a set of geophones mounted on steel and aluminum base plates joint together with a fire hose. It was assembled by CReSIS Graduate Research Assistants, Anthony Hoch, Geophysics Graduate Student, and Chris Gifford, robotics specialist, at the University of Kansas. The weight of the streamer was approximately 200 pounds. Streamer cost was around $2,000. The setup of the streamer used in Greenland consisted of 24 conventional geophones mounted on eight plates, four aluminum and four steel. The plates were spaced 1.5 meters from each other, for a total streamer length of 12 meters. The streamer s geometry consisted of 8 vertical and 16 horizontal (SV and SH) geophones. Each plate had 1 vertical and 2 horizontal geophones. They were positioned in 3

5 conventional orientation and Galperin orientation, another type of configuration where geophones are positioned at different angles. Figure 1 shows the configuration of the geophones on the snowstreamer deployed on Jakobshavn Glacier. Conventional Galperin Figure 1 Snowstreamer deployed on Jakobshavn Glacier, Greenland (left) and geophone s configuration on the streamer (right) Methodology I. Data Acquisition George Tsoflias and a group of scientists from Pennsylvania State University acquired seismic data on the Jakobshavn Glacier in May The snowstreamer consisted of 24 geophones mounted on eight plates, meanwhile the control line, was composed of 24 individually-planted geophones. The snowstreamer and the individual geophones, or control line, were placed next to each other, as figure 2 shows. 4

6 Figure 2 Snowstreamer next to the control line The source used to produce the sound waves was 0.5 kilograms of explosives placed 10 meters below the surface. The source was advanced at 160 meters long increments from the snowstreamer. Two lines were acquired for the study, named line 34 and line 51-52, the main line. Line 34 will not be used for the analysis because it didn t include the control line. Line 34 was used to test deployment of the snowstreamer in the field. Line consisted of 22 shots, or sources, but only 14 shots were used for the analysis, because the first 8 shots didn t include control line data due to malfunctioning instrumentation. II. Data Processing Processing of the seismic data employed MATLAB and SPW. MATLAB is a highperformance high level software language for technical computing. SPW, or Seismic Processing Workshop, is a commercially available interactive seismic data processing software ( The first step is to reformat the raw data that came from Greenland, changing it from SEG2 to SEG-Y format, using the seismic program SPW I/O Utility. Using MATLAB, a script was written to process the data and plot it in three different graph series. The script used to produce the trace to trace comparison of the streamer and control data is included at the appendix section. 5

7 III. Data Interpretation The graphs and figures created after processing the raw data and inputting the geometry and topography of the study area will be used for the interpretation of the seismic data. The figures will include magnitude and frequency plots, a db graph, and a shot gather image. The magnitude, frequency series and the db graph were generated in MATLAB and were used for the trace to trace comparison between the streamer and control data. Meanwhile receiver gathers were generated in the seismic program SPW SeisViewer, for a broader perspective of the data gathered by each streamer and control channel. The figures will help us identify the bedrock reflection, the wave velocity, and features or layers in the ice and beneath the bedrock. IV. Evaluating the seismic instrumentation On July 10, 2007 we tested in Lawrence, Kansas the seismic instrumentation used in Greenland, to test if the differences in magnitude of the sound waves recorded by the snowstreamer in Greenland were due to hardware problems. The site of the study was the west campus of the University of Kansas (KU). The streamer geophones were individually planted in a line next to the geophones of the control line, as seen in figure 3. Figure 3 Seismic study on west campus of KU on July 10,

8 Instead of explosives, used for the seismic study on Greenland, the seismic source in this study was a rifle, as seen in figure 4. There were 36 shots recorded with a distance of 1 meter between each shot. The data was later reformatted and processed in MATLAB similar to the raw data that came from Greenland. Figure 4 Rifle used as the seismic source in the seismic study at KU Analysis: Comparison between Streamer and Control Seismic Data Channels 3 and 15 of the streamer seem to be the ones that follow more closely the control data. Channel 3 of the snowstreamer seems to have the best performance. This is a vertical geophone on an aluminum plate on the streamer. Although channel 15 is also a vertical geophone on an aluminum plate (normal orientation), it doesn t perform as well as channel 3 in windy conditions because unlike 3, it is exposed to the wind; channel 3 is protected from the wind by the fire-hose. In general the vertical channels of the streamer are the ones that image the subsurface more clearly and follow more closely the data gathered with the control hand planted geophones. 7

9 Figure 5 Comparison between aluminum (Ch.3) and steel (Ch.6) plates with vertical geophones on the streamer and control lines The aluminum plates appear to perform slightly better than steel plates, as you can see in the trace to trace comparison on figure 5. The causes for it are right now unknown, but possible causes may have to do with the stiffness and weight of the aluminum and steel. Aluminum is lighter than steel, therefore, because of weight concerns for the equipment transported to Greenland; aluminum plates would make the snowstreamer lighter and easier to transport. For the best geophone configuration for the streamer, conventional or Galperin configurations were evaluated. Figures 6 illustrates how the Galperin configuration compared with the normal or conventional configuration, with both geophones been of aluminum. Meanwhile, figure 7 compares aluminum and steel plates with Galperin orientation. 8

10 Figure 6 Comparison between normal orientation (Ch.3) and Galperin vertical orientation (Ch. 21) Figure 7 Comparison between channels 21 (left) and 24 (right) of the streamer 9

11 The vertical geophones on normal or conventional orientation appear less sensitive to wind noise than the Galperin vertical channels. Galperin orientated geophones were most sensitive to the wind noise than conventional vertical geophones. For the horizontal channels, in particular, the SV channel, the Galperin geophone performs as well as the conventional geophone, as seen in figure 8. Figure 8 Comparison between vertical (Ch.3) and SV (Ch.8) components of the streamer and control data 10

12 Figure 9 Comparison between vertical (Ch.3) and horizontal (Ch.1) components of the streamer and control data The vertical component recordings were superior to horizontal component data, as seen in figures 8 and 9. This appears very clearly in the data interpreted. The streamer channels with horizontal orientations always show a lot of wind noise in the data. SH component seems to not be an option. It performs only well to some extent under no wind conditions, and with just a little wind the data becomes noisy. SV component can be an option when the wind conditions are calm and the sensor is preferably Galperin. The wind has a negative effect on the data gathered by the streamer. The individuallyplanted geophones, or control, also are negatively affected by the wind. It brings higher than normal noise to the data being gathered. The wind increases the magnitude or amplitude of the seismic wave data recorded by the streamer and also the control. This data that is later view and interpret through MATLAB, shows irregular magnitudes caused by the wind, which impede an accurate identification of the bed or well as interpretation of the different internal layers of the ice and bed. This is illustrated on the trace to trace comparisons of streamer and control data in figure

13 Figure 10 Aluminum plates under different wind conditions Results The results of the comparison between streamer and control data illustrate that when there is no wind the streamer data shows more clearly the bedrock reflection than the control data. The magnitude of the seismic waves in the streamer data is higher than the one from the control data, facilitating the identification of the bedrock at approximately 1 second. Even in conditions when the wind is strong, 10+ knots, the streamer data shows 12

14 the bedrock reflection at approximately 1 second, clearer than the control data, in some cases. This depends a great deal on the direction of the geophone, if it is vertical or horizontal. Direct wave Ice base reflection Figure 11 Receiver gather (top) and Topography (bottom) of the studied area. Channel 3(left) is the streamer and channel 27 (right) is the control for the data of line In this receiver gather, the streamer performs very well when compared to the control data. The first energy burst represents the energy from the direct wave, while the second energy burst represents the bedrock reflection. The image from the streamer identifies very well the bedrock at approximately 1 second. The reflection of the bedrock has a parabola form, very similar to the topography of the area. But the parabola occurs because as you move farther from the streamer or control lines, it takes longer for the wave arrival to be recorded. The control line was having problems the first day of data collection, but for the second day, it was functioning well. This is the reason for the data missing in the first shots of the control image. The noise is very evident in the last traces (right), because of winds in excess of 10 knots. Both, streamer and control appear to be 13

15 affected by the wind noise. Multi-fold stacking, adding multiple channels to form a simple trace, can improve the signal to noise ratio. Figure 12 Test of the seismic equipment used in Greenland (Seismic study at KU) The seismic hardware test study conducted at KU showed excellent agreement between the magnitude of the streamer and control seismic instrumentation, as seen on figure 12. This means that the streamer data from Greenland compare rather well with the individually-planted geophones, or control, and that no major hardware problems occurred with the seismic equipment. Conclusions and Recommendations The interpretation and analysis from the data gathered and processed concludes that this particular streamer constructed by scientists at CReSIS is, in fact, a reliable tool to conduct seismic studies through ice at the polar regions. When there is no wind the streamer performs very well, even better than the control handplants. When the wind starts to pick up, less than 5 knots, the streamer continues to be a reliable tool to identify the bed and the other internal layers of the ice and the bed. When the conditions are 14

16 windy, 10 or more knots, the streamer is certainly affected, but the bed can be identified in some cases, depending on the streamer set up. At these windy conditions even the control data is affect to a great extent. It also has to be considered that the streamer is more exposed to the wind that the control line, whose geophones are buried in the snow. From the analysis we also can conclude that the best streamer setup is conventional vertical geophones on aluminum plates. The steel plates didn t perform as well as the aluminum plates. The conventional or normal orientation was far better than the Galperin geophone configuration. The vertical component is the ideal set up for the streamer; horizontal components do not detect P-waves as well as vertical ones. The recommendations are to not to use the horizontal component (SH) in the streamer. The SV component needs further revision to determine if it can be a reliable alternative for the seismic methods on glacier research. The Galperin orientation on the SV channels performs rather well, but only when there is no wind. At the moment, the SV and SH components of the streamer aren t expected to be used for further research. From the interpretation of the figures we can conclude that the streamer can be used to identify the bedrock, and even also other internal ice layers and geologic features beneath the bed. We concluded that there was no hardware malfunction in the seismic equipment used in Greenland; therefore, the streamer performs similarly or even better than the individually-planted geophones, or control line. The results are a step forward for the deployment of a longer streamer to gather precise seismic data for the mapping of the subsurface of Jakobshavn Glacier in Greenland. Future Work There is a great deal of work scheduled for the use of the snowstreamer in seismic studies through ice. They include: Construct a longer snowstreamer, 500 meters or 1 kilometer long. The setup of the new streamer would consist of aluminum plates with conventional vertical geophones. 15

17 For May 2008 another expedition to Greenland is planed to collect seismic data from Jakobshavn Glacier using the newly constructed snowstreamer. 2-D and 3-D mapping of the subsurface of Jakobshavn Glacier will be possible using the seismic data gathered by the snowstreamer. Acknowledgements We would like to thank CReSIS and the NSF for the opportunity given to conduct this research work and the help provided to achieve our work and presenting it in this technical report. References Burger, H.R., Sheehan A.F., & Jones C.H., 2006: Introduction to Applied Geophysics: Exploring the Shallow Subsurface. Rev. ed of: Englewood Cliffs, N.J.: Prentice Hall, Eiken, O., Degutsch, M., Ritse, P., & Rod, K., 1989: Snowstreamer: an efficient tool in seismic acquisition. First Break, Vol.7, Issue 9, Glacier. (2007). In Encyclopedia Britannica. Retrieved July 2, 2007, from Encyclopedia Britannica Online: IPCC, 2007: Summary for Policymakers. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernamental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Maas, H.R., Dietrich, R., Schwalbe, E., Babler, M, & Westfeld, P., 2006: Analysis of the Motion Behaviour of Jakobshavn Isbrae Glacier in Greenland by Monocular Image Sequence Analysis. IAPRS Volume XXXVI

18 Appendix MATLAB script used to produce the trace to trace comparison of the streamer and control data: %Correlation_Coefficient addpath c:\segymat dt=0.0005; time=0:dt:8.0-dt; file=[18:1:25, 27:1:34, 36:1:39, 41:1:42]; for n=[9]; n_shot=(file(n)); data=zeros(length(time),48); filename=strcat('j_10',num2str(n_shot),'.sgy'); [Data,SegyTraceHeaders,SegyHeader]=ReadSegy(['C:\GEORGE_RAW\seismic_Gre enland\line_pole51_52\' filename]); %Data=Data(0.6/dt:1.6/dt,:); ns=length(data(:,1)); % frequency dimension fnyq=1/(2*dt); df=1/time(ns); freq=0:df:ns*df-df; if length(data(1,:))==24 Data(:,[9 8 7])=Grotate(Data(:,[9 8 7])); Data(:,[ ])=Grotate(Data(:,[ ])); Data(:,[ ])=Grotate(Data(:,[ ])); Data(:,[ ])=Grotate(Data(:,[ ])); elseif length(data(1,:))==48 Data(:,[9 8 7])=Grotate(Data(:,[9 8 7])); Data(:,[ ])=Grotate(Data(:,[ ])); Data(:,[ ])=Grotate(Data(:,[ ])); Data(:,[ ])=Grotate(Data(:,[ ])); Data(:,[ ])=Grotate(Data(:,[ ])); Data(:,[ ])=Grotate(Data(:,[ ])); Data(:,[ ])=Grotate(Data(:,[ ])); Data(:,[ ])=Grotate(Data(:,[ ])); end Channel = 3; data_f = fft(data); plot_title = []; switch Channel; 17

19 end case 3, plot_title = [plot_title 'Normal Vertical Aluminum']; case 6, plot_title = [plot_title 'Normal Vertical Steel']; case 9, plot_title = [plot_title 'Galperin Vertical Aluminum']; case 12, plot_title = [plot_title 'Galperin Vertical Steel']; case 15, plot_title = [plot_title 'Normal Vertical Aluminum']; case 18, plot_title = [plot_title 'Normal Vertical Steel']; case 21, plot_title = [plot_title 'Galperin Vertical Aluminum']; case 24, plot_title = [plot_title 'Galperin Vertical Steel']; switch n; case 9, plot_title = [plot_title ' (no wind)']; case 10, plot_title = [plot_title ' (no wind)']; case 11, plot_title = [plot_title ' (no wind)']; case 12, plot_title = [plot_title ' (no wind)']; case 13, plot_title = [plot_title ' (no wind)']; case 14, plot_title = [plot_title ' (wind 1-2 knots)']; case 15, plot_title = [plot_title ' (wind 2-3 knots)']; case 16, plot_title = [plot_title ' (wind 5+ knots)']; case 17, plot_title = [plot_title ' (wind 10 knots)']; case 18, plot_title = [plot_title ' (wind 10 knots)']; case 19, plot_title = [plot_title ' (wind 10 knots)']; case 20, plot_title = [plot_title ' (wind 10 knots)']; case 21, plot_title = [plot_title ' (wind 5-10 knots)']; case 22, plot_title = [plot_title ' (wind 5 knots)']; end time=time(1:(length(data(:,1)))); figure; subplot(1,2,1); plot(data(:,channel),-time,'b',data(:,channel+24),- time,'k'); title(plot_title); xlabel('magnitude'); ylabel('time (s)'); axis([ ]); subplot(2,2,4); plot(freq,abs(data_f(:,channel)),'b',freq,abs(data_f(:,channel+24)),'k' ); title('frequency Domain'); xlabel('frequency (Hz)'); ylabel('magnitude'); axis([0 fnyq 0 max(abs(data_f(:,channel)))]); legend('streamer','control'); mag = 20.*log10(abs(Hilbert(Data)).*(1.6985e-4).*1000); %dbm (db relative to milivolts) subplot(2,2,2); plot(time, mag(:,channel),'b',time, mag(:,channel+24),'k'); title('db relative to milivolts'); xlabel('time (s)'); ylabel('db'); end 18