scholarly journals Inferring ice thickness from a glacier dynamics model and multiple surface data sets

2017 ◽  
Vol 29 (5-6) ◽  
pp. e2460 ◽  
Author(s):  
Yawen Guan ◽  
Murali Haran ◽  
David Pollard
Keyword(s):  
Ice Thickness ◽  
Data Sets ◽  
Dynamics Model ◽  
Surface Data ◽  
Glacier Dynamics

Shear and compressive wave data acquisition using multicomponent vibrating source and landstreamer

2021 ◽  
Author(s):  
Andre Pugin ◽  
Barbara Dietiker ◽  
Kevin Brewer ◽  
Timothy Cartwright
Keyword(s):  
Leading Edge ◽  
Data Sets ◽  
Compressive Wave ◽  
Near Surface ◽  
Source Type ◽  
Receiver Array ◽  
Surface Data ◽  
Source Orientation ◽  
Refracted Waves ◽  
Wave Modes

<p>In the vicinity of Ottawa, Ontario, Canada, we have recorded many multicomponent seismic data sets using an in-house multicom­ponent vibrator source named Microvibe and a landstreamer receiver array with 48 3-C 28-Hz geophones at 0.75-m intervals. The receiver spread length was 35.25 m, and the near-offset was 1.50 m. We used one, two or three source and three receiver orientations — vertical (V), inline-horizontal (H1), and transverse-horizontal (H2). We identified several reflection wave modes in the field records — PP, PS, SP, and SS, in addition to refracted waves, and Rayleigh-mode and Love-mode surface waves. We computed the semblance spectra of the selected shot records and ascertained the wave modes based on the semblance peaks. We then performed CMP stacking of each of the 9-C data sets using the PP and SS stacking velocities to compute PP and SS reflection profiles.</p><p>Despite the fact that any source type can generate any combination of wave modes — PP, PS, SP, and SS, partitioning of the source energy depends on the source orientation and VP/VS ratio. Our examples demonstrate that the most prominent PP reflection energy is recorded by the VV source-receiver orientation, whereas the most prominent SS reflection energy is recorded by the H2H2 source-receiver orientation with possibility to obtain decent shear wave near surface data in all other vibrating and receiving directions.</p><p>Pugin, Andre and Yilmaz, Öz, 2019. Optimum source-receiver orientations to capture PP, PS, SP, and SS reflected wave modes. The Leading Edge, vol. 38/1, p. 45-52. https://doi.org/10.1190/tle38010045.1</p>

scholarly journals On the limit to resolution and information on basal properties obtainable from surface data on ice streams

2008 ◽  
Vol 2 (2) ◽  
pp. 167-178 ◽  
Author(s):  
G. H. Gudmundsson ◽  
M. Raymond
Keyword(s):  
Flow Line ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Surface Flow ◽  
Ice Thickness ◽  
Ice Streams ◽  
Statistical Estimates ◽  
Surface Data ◽  
Highly Sensitive ◽  
Surface Measurements

Abstract. An optimal estimation method for simultaneously determining both basal slipperiness and basal topography from variations in surface flow velocity and topography along a flow line on ice streams and ice sheets is presented. We use Bayesian inference to update prior statistical estimates for basal topography and slipperiness using surface measurements along a flow line. Our main focus here is on how errors and spacing of surface data affect estimates of basal quantities and on possibly aliasing/mixing between basal slipperiness and basal topography. We find that the effects of spatial variations in basal topography and basal slipperiness on surface data can be accurately separated from each other, and mixing in retrieval does not pose a serious problem. For realistic surface data errors and density, small-amplitude perturbations in basal slipperiness can only be resolved for wavelengths larger than about 50 times the mean ice thickness. Bedrock topography is well resolved down to horizontal scale equal to about one ice thickness. Estimates of basal slipperiness are not significantly improved by accurate prior estimates of basal topography. However, retrieval of basal slipperiness is found to be highly sensitive to unmodelled errors in basal topography.

scholarly journals Interannual sea ice thickness variability in the Bay of Bothnia

2018 ◽  
Vol 12 (11) ◽  
pp. 3459-3476 ◽  
Author(s):  
Iina Ronkainen ◽  
Jonni Lehtiranta ◽  
Mikko Lensu ◽  
Eero Rinne ◽  
Jari Haapala ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Thickness Variability ◽  
Fast Ice ◽  
Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

scholarly journals Significance of Surface Temperature in the Thermal Infrared Sensing of Sea and Lake Ice

1975 ◽  
Vol 15 (73) ◽  
pp. 277-283 ◽  
Author(s):  
Ambrose O. Poulin
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Snow Depth ◽  
Thermal Infrared ◽  
Variable Thermal Conductivity ◽  
Ice Thickness ◽  
Infrared Sensing ◽  
Physical Conditions ◽  
Surface Data ◽  
Steady State Heat

Abstract Thermal infrared sensing can provide much information about sea ice, and some of the physical conditions associated with sea ice suggest that surface temperature may be a good indicator of ice thickness. However, steady-state heat-flow calculations suggest that the variable thickness of the snow-cover and its low. variable thermal conductivity would preclude the use of surface temperature alone as a suitable indicator of ice thickness. Measurements of surface temperature, snow depth, and ice thickness suggest that, in an area of relatively uniform ice thickness, surface temperature might be useful as an indicator of snow depth if some surface data can be obtained.

scholarly journals Significance of Surface Temperature in the Thermal Infrared Sensing of Sea and Lake Ice

1975 ◽  
Vol 15 (73) ◽  
pp. 277-283
Author(s):  
Ambrose O. Poulin
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Snow Depth ◽  
Thermal Infrared ◽  
Variable Thermal Conductivity ◽  
Ice Thickness ◽  
Infrared Sensing ◽  
Physical Conditions ◽  
Surface Data ◽  
Steady State Heat

AbstractThermal infrared sensing can provide much information about sea ice, and some of the physical conditions associated with sea ice suggest that surface temperature may be a good indicator of ice thickness. However, steady-state heat-flow calculations suggest that the variable thickness of the snow-cover and its low. variable thermal conductivity would preclude the use of surface temperature alone as a suitable indicator of ice thickness. Measurements of surface temperature, snow depth, and ice thickness suggest that, in an area of relatively uniform ice thickness, surface temperature might be useful as an indicator of snow depth if some surface data can be obtained.

scholarly journals The impact of snow depth, snow density and ice density on sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise

2015 ◽  
Vol 9 (1) ◽  
pp. 37-52 ◽  
Author(s):  
S. Kern ◽  
K. Khvorostovsky ◽  
H. Skourup ◽  
E. Rinne ◽  
Z. S. Parsakhoo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ice Thickness ◽  
Data Sets ◽  
Snow Density ◽  
Radar Altimetry ◽  
Sea Ice Thickness ◽  
The Mean ◽  
Satellite Radar ◽  
Satellite Radar Altimetry

Abstract. We assess different methods and input parameters, namely snow depth, snow density and ice density, used in freeboard-to-thickness conversion of Arctic sea ice. This conversion is an important part of sea ice thickness retrieval from spaceborne altimetry. A data base is created comprising sea ice freeboard derived from satellite radar altimetry between 1993 and 2012 and co-locate observations of total (sea ice + snow) and sea ice freeboard from the Operation Ice Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) airborne campaigns, of sea ice draft from moored and submarine upward looking sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer (AMSR-E) and the Warren climatology (Warren et al., 1999). We compare the different data sets in spatiotemporal scales where satellite radar altimetry yields meaningful results. An inter-comparison of the snow depth data sets emphasizes the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. We test different freeboard-to-thickness and freeboard-to-draft conversion approaches. The mean observed ULS sea ice draft agrees with the mean sea ice draft derived from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the approaches are able to reproduce the seasonal cycle in sea ice draft observed by moored ULS. A sensitivity analysis of the freeboard-to-thickness conversion suggests that sea ice density is as important as snow depth.

scholarly journals Electromagnetic reflection survey 1986 at the Inland Ice margin of the Pâkitsoq basin, central West Greenland

1987 ◽  
Vol 135 ◽  
pp. 87-95
Author(s):  
L Thorning ◽  
E Hansen
Keyword(s):  
Power Plant ◽  
Hydroelectric Power Plant ◽  
Field Work ◽  
Ice Thickness ◽  
Data Sets ◽  
Hydroelectric Power ◽  
Subglacial Topography ◽  
Series Of Experiments ◽  
Inland Ice

The first successful application of electromagnetic ref1ection (EMR) techniques for determination of ice thickness in the outermost margin of the Inland Ice adjacent to the Pâkitsoq basin took place in luly 1985 (Thorning et al., 1986). Although the survey was planned as a series of experiments to examine why previous attempts had not worked, the EMR data acquired were of very good quality and could be compiled into a preliminary map of ice thickness and a map of the subglacial topography over part of the region. Thus, by early 1986 it Rapp. Grønlands geol. Unders. 135, 87-95 (1987) was known that the method worked and could be compiled through to the final product. With the increasing interest in this region, which is the planned location of the first hydroelectric power plant in Greenland, it was necessary to return in 1986 to survey the area in greater detail and to extend the coverage to the east. This note describes the field work carried out in April 1986 and the subsequent compilation and analysis of the combined EMR data sets from 1985 and 1986.

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