Inversion of airborne electromagnetic survey data for sea‐ice keel shape

Geophysics ◽  
1991 ◽  
Vol 56 (12) ◽  
pp. 1986-1991 ◽  
Author(s):  
Guimin Liu ◽  
Austin Kovacs ◽  
Alex Becker
Keyword(s):  
Sea Ice ◽  
Survey Data ◽  
Inductive Limit ◽  
Sea Water ◽  
Ice Thickness ◽  
Inversion Method ◽  
Perfect Conductor ◽  
Arctic Seas ◽  
Worst Case ◽  
Airborne Electromagnetic

It is possible to interpret conventional airborne electromagnetic (EM) data acquired over ice‐covered Arctic seas to obtain values of the sea ice thickness and, where needed, the actual sea ice keel geometry. To do so, we require high‐frequency (inductive limit) data that allows us to assume that the ice is virtually transparent to the EM fields while the sea water forms a perfect conductor. Practically, a 100 kHz operating frequency is needed, but data acquired at a lower frequency can be scaled to obtain the required inductive limit anomaly. The data inversion is done by linking Occam’s inversion method to a rapid numerical, two‐dimensional, forward solution for the ice keel problem. A most useful feature of the adopted inversion scheme is the minimization of the roughness or the mean square slope of the keel boundary. In some cases, where the keel might be bounded by steeply dipping walls, this constraint may result in a less accurate solution than might be obtained with a conventional technique. In most cases the advantage in stability that it provides outweighs the possible loss of accuracy that it may occasion. Tests on synthetic data show a possible worst case ice thickness error of about 15 percent. The results of inversion tests for two sets of survey data acquired near Prudhoe Bay, Alaska, also indicate an accuracy of this order of magnitude. While some portion of the inversion error must be ascribed to the roughness constraint and is therefore inherent to the inversion technique used, the remainder must be ascribed to the instrumentation and is probably remediable.

scholarly journals Airborne electromagnetic footprints in 1D earths

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. G63-G72 ◽  
Author(s):  
James E. Reid ◽  
Andreas Pfaffling ◽  
Julian Vrbancich
Keyword(s):  
Sea Ice ◽  
Frequency Domain ◽  
Half Space ◽  
Field Data ◽  
Inductive Limit ◽  
Thin Sheet ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Flight Height ◽  
Airborne Electromagnetic

Existing estimates of footprint size for airborne electromagnetic (AEM) systems have been based largely on the inductive limit of the response. We present calculations of frequency-domain, AEM-footprint sizes in infinite-horizontal, thin-sheet, and half-space models for the case of finite frequency and conductivity. In a half-space the original definition of the footprint is extended to be the side length of the cube with its top centered below the transmitter that contains the induced currents responsible for 90% of the secondary field measured at the receiver. For a horizontal, coplanar helicopter frequency-domain system, the in-phase footprint for induction numbers less than 0.4 (thin sheet) or less than 0.6 (half-space) increases from around 3.7 times the flight height at the inductive limit to more than 10 times the flight height. For a vertical-coaxial system the half-space footprint exceeds nine times the flight height for induction numbers less than 0.09. For all models, geometries, and frequencies, the quadrature footprint is approximately half to two-thirds that of the in-phase footprint. These footprint estimates are supported by 3D model calculations that suggest resistive targets must be separated by the footprint dimension for their individual anomalies to be resolved completely. Analysis of frequency-domain AEM field data acquired for antarctic sea-ice thickness measurements supports the existence of a smaller footprint for the quadrature component in comparison with the in-phase, but the effect is relatively weak. In-phase and quadrature footprints estimated by comparing AEM to drillhole data are considerably smaller than footprints from 1D and 3D calculations. However, we consider the footprints estimated directly from field data unreliable since they are based on a drillhole data set that did not adequately define the true, 3D, sea-ice thickness distribution around the AEM flight line.

scholarly journals Taking a look at both sides of the ice: comparison of ice thickness and drift speed as observed from moored, airborne and shore-based instruments near Barrow, Alaska

2015 ◽  
Vol 56 (69) ◽  
pp. 363-372 ◽  
Author(s):  
Andrew R. Mahoney ◽  
Hajo Eicken ◽  
Yasushi Fukamachi ◽  
Kay I. Ohshima ◽  
Daisuke Simizu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Probability Distributions ◽  
Median Filter ◽  
Acoustic Doppler Current Profiler ◽  
Ice Thickness ◽  
Current Profiler ◽  
Airborne Electromagnetic ◽  
Airborne Electromagnetic Survey ◽  
Derived Data ◽  
Snow Thickness

AbstractData from the Seasonal Ice Zone Observing Network (SIZONet) acquired near Barrow, Alaska, during the 2009/10 ice season allow novel comparisons between measurements of ice thickness and velocity. An airborne electromagnetic survey that passed over a moored Ice Profiling Sonar (IPS) provided coincident independent measurements of total ice and snow thickness and ice draft at a scale of 10 km. Once differences in sampling footprint size are accounted for, we reconcile the respective probability distributions and estimate the thickness of level sea ice at 1.48 ± 0.1 m, with a snow depth of 0.12 ± 0.07 m. We also complete what we believe is the first independent validation of radar-derived ice velocities by comparing measurements from a coastal radar with those from an under-ice acoustic Doppler current profiler (ADCP). After applying a median filter to reduce high-frequency scatter in the radar-derived data, we find good agreement with the ADCP bottom-tracked ice velocities. With increasing regulatory and operational needs for sea-ice data, including the number and thickness of pressure ridges, coordinated observing networks such as SIZONet can provide the means of reducing uncertainties inherent in individual datasets.

The sea-ice detection capability of synthetic aperture radar

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Tao Xie ◽  
Li Zhao ◽  
William Perrie ◽  
He Fang
Keyword(s):  
Dielectric Constant ◽  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Sea Water ◽  
Dielectric Constants ◽  
Synthetic Aperture ◽  
Ice Thickness ◽  
Reflection And Transmission ◽  
Pulse Radar ◽  
Ice Detection

Climate change, increasing activities in areas like offshore oil and gas exploration, marine transport, eco-tourism, in additional to the usual activities of northerners resident are leading to reductions in sea ice. Therefore, there is an urgent need for improvement in the sea ice detection in polar areas. Starting from the mechanism of electromagnetic scattering, based on an empirical dielectric constant model, we apply EM multi-reflection and transmission formulas for coefficients between the air-ice interface and sea water-ice interface to develop a model for estimating the capability of detection of sea ice and ice thickness based on a pulse radar system, synthetic aperture radar (SAR). Although the dielectric constant of sea ice is less than that of sea water, this model can provide a rational methodology as the normalized radar cross section (NRCS) of sea ice is larger than that of sea water due to multiple reflections. The numerical simulations of this model showed that the convergence rate is rapid. With 3 or 4 reflections and transmissions (depending on temperature, salinity, and dielectric constants of sea ice and water), truncation errors can be satisfied using theoretical considerations and practical applications. The model is applied to estimate the capability of SAR to discriminate ice from water. The numerical results suggested that the model ability to measure ice thickness decreases with increasing radar incident angles and increases with increasing radar pulse width. Reflection and transmission coefficients decrease monotonically with ice thickness and are saturated for ice thicknesses above a certain critical value which depends on SAR incidence angle, frequency and dielectric constants of sea ice. The capability to detect ice thickness for given different bands of pulse radar widths can be estimated with this model.

scholarly journals Impact of Arctic sea ice floe-scale anisotropy on airborne electromagnetic surveys

2020 ◽  
pp. 1-13
Author(s):  
Jean Negrel ◽  
Dmitry V. Divine ◽  
Sebastian Gerland
Keyword(s):  
Sea Ice ◽  
Satellite Images ◽  
Regional Scale ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Flight Pattern ◽  
Arctic Sea ◽  
Airborne Electromagnetic ◽  
Radar Satellite ◽  
Airborne Survey

Abstract Airborne electromagnetic induction sensors have demonstrated their extensive capacities to measure sea-ice thickness distributions. However, biases can emerge when comparing these 1-D measurements to a broader 2-D regional scale due to the spatial anisotropy inherent to sea-ice cover. Automated processing of available sea-ice maps could significantly ease the decision on how to set up an optimised flight pattern, which would result in representative ice thickness numbers for the region. In this study, first we investigate the extent to which the sea-ice anisotropy can influence the representativeness of an airborne survey compared to the regional situation. Second, we propose a method to process sea-ice maps prior to flights to help preparing the most representative flight plan possible for the local area. The method is based on automated segmentation of radar satellite images and extensive simulation of flight transects over the image. The spatial analysis of these transects enables for the identification of the most representative survey trajectories for the area. The method was applied for seven different synthetic aperture radar satellite images over Arctic sea ice north of Svalbard. The results indicate that the proposed method improved the representativeness of the airborne survey by identifying the most suitable transect over the ice pack.

scholarly journals In situ measurements of the direct-current conductivity of Antarctic Sea ice: implications for airborne electromagnetic Sounding of Sea-ice thickness

2006 ◽  
Vol 44 ◽  
pp. 217-223 ◽  
Author(s):  
J.E. Reid ◽  
A. Pfaffling ◽  
A.P. Worby ◽  
J.R. Bishop
Keyword(s):  
Sea Ice ◽  
Direct Current ◽  
Sea Water ◽  
Vertical Direction ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Direct Current Conductivity

AbstractAirborne, Ship-borne and Surface low-frequency electromagnetic (EM) methods have become widely applied to measure Sea-ice thickness. EM responses measured over Sea ice depend mainly on the Sea-water conductivity and on the height of the Sensor above the Sea-ice–sea-water interface, but may be Sensitive to the Sea-ice conductivity at high excitation frequencies. We have conducted in Situ measurements of direct-current conductivity of Sea ice using Standard geophysical geoelectrical methods. Sea-ice thickness estimated from the geoelectrical Sounding data was found to be consistently underestimated due to the pronounced vertical-to-horizontal conductivity anisotropy present in level Sea ice. At five Sites, it was possible to determine the approximate horizontal and vertical conductivities from the Sounding data. The average horizontal conductivity was found to be 0.017 Sm–1, and that in the vertical direction to be 9–12 times higher. EM measurements over level Sea ice are Sensitive only to the horizontal conductivity. Numerical modelling has Shown that the assumption of zero Sea-ice conductivity in interpretation of airborne EM data results in a negligible error in interpreted thickness for typical level Antarctic Sea ice.

scholarly journals GROUND PENETRATING RADAR DATA INTERPRETATION USING ELECTROMAGNETIC FIELD ANALYSIS FOR SEA ICE THICKNESS MEASUREMENT

2019 ◽  
Vol XLII-3/W7 ◽  
pp. 47-50 ◽  
Author(s):  
M. Matsumoto ◽  
M. Yoshimura ◽  
K. Naoki ◽  
K. Cho ◽  
H. Wakabayashi
Keyword(s):  
Electromagnetic Field ◽  
Sea Ice ◽  
Ground Penetrating Radar ◽  
Sea Water ◽  
Thickness Measurement ◽  
Field Analysis ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ground Penetrating ◽  
Snow And Ice

<p><strong>Abstract.</strong> Observation of sea ice thickness by remote sensing is one of key issues to understand an effect of global warming. However, ground truth must be necessary to discuss this kind of approach. Although there are several methods to acquire ice thickness, Ground Penetrating Radar (GPR) can be good solution because it can discriminate snow-ice and ice-sea water interface thanks to comparative higher spatial resolution than the other methods. In this paper, we carried out GPR measurement in brackish lake and an electromagnetic field analysis in order to interpret the GPR data. The simulation model was assumed considering the actual snow and ice thickness acquired in field measurement. From the simulation results, although it seems difficult to identify the reflection at snow and ice interface due to a thin layer thickness and a low dielectric constant, snow and ice thickness may be estimated by using multiple reflection components.</p>

scholarly journals Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas

2020 ◽  
Author(s):  
Robbie D. C. Mallett ◽  
Julienne C. Stroeve ◽  
Michel Tsamados ◽  
Jack C. Landy ◽  
Rosemary Willatt ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Ice Thickness ◽  
Arctic Seas ◽  
Sea Ice Thickness ◽  
Marginal Seas ◽  
Thickness Variability ◽  
Satellite Radar ◽  
Arctic Climate Change ◽  
Strong Control

Abstract. Mean sea ice thickness is a sensitive indicator of Arctic climate change and in long-term decline despite significant interannual variability. Current thickness estimations from satellite radar altimeters employ a snow climatology for converting range measurements to sea ice thickness, but this introduces unrealistically low interannual variability and trends. When the sea ice thickness in the period 2002–2018 is calculated using new snow data with more realistic variability and trends, we find mean sea ice thickness in three of the seven marginal seas to be declining between 70–100 % faster than when calculated with the conventional method. When analysed as an aggregate, the mean ice thickness in the marginal seas is now in statistically significant decline for four of seven winter months. This is observed despite a 58% increase in interannual variability between the methods in the same time period. On a seasonal timescale we find that snow data exerts an increasingly strong control on thickness variability over the growth season, contributing only 20 % in October but 72 % by April. Higher variability and faster decline in the sea ice thickness of the marginal seas has wide implications for our understanding of the polar climate system and our predictions for its change, as well as for stakeholders involved in Arctic shipping and natural resource extraction.

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