Acidalia: Composite
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GeoTIFF
Anc. Data
Combined Ice Consistency Map Last Updated: 2019-04-03 09:30:51
The ice consistency map is generated by combining all the individual consistency maps through the "SWIM Equation", i.e. Neutron; Thermal; Radar Surface Power; Geomorphology and Radar Dielectric Estimates. The ice consistency map is designed to show where all data sets are consistent or inconsistent with the presence of ice over a combined sensing depth of ~5m, indicating the likely presence of ice or lack thereof in that location. Acidalia: Individual Data Sets
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Description
PNG
TIFF
GeoTIFF
Anc. Data
Neutron Dataset Last Updated: 2019-04-03 09:14:11
Pathare et al. [2018] produced the most definitive global maps to date of near-surface water-equivalent hydrogen in an upper layer (W_up), lower layer (W_dn), and depth (D) to the lower layer. They refined the cross-over technique developed by Feldman et al. [2011] to produce the first maps of self-consistent W_up, W_dn, and D. These W_dn results (expressed in terms of WEH as percentages) were then scaled to fit into the SWIM equation. If W_dn ≥ 25% -> 1, 10% ≤ W_dn < 25% scaled between 0 and 1, 5% ≤ W_dn < 10% scaled between -1 and 0, and W_dn < 5% -> -1. Geomorphology Last Updated: 2019-04-03 09:27:51
The geomorphology map was generated using previous and new mapping of periglacial and glacial features. For the Arcadia and Onilus regions, a number of periglacial features were identified between 30-60°N. A sampling of 4x4° CTX image mosaics within previously mapped units was used to tally the number of periglacial and glacial landforms and to extrapolate the observations to the mapped unit boundaries. The number of landforms were then normalized to yield values between 0 and 1. Few to no periglacial features were observed equatorward of 30°N, and these areas were assigned a preliminary value of -1. For Utopia and Acidalia, the southernmost extent of latitude dependent mantle was estimated and mapped using previously published observations and scalloped terrain was mapped in Utopia. Areas north of the mantle boundary were assigned a value of 0.5 and scalloped terrain was assigned a value of 0.75. Previous mapping was also used as input for all regions, including glacial features (lobate debris aprons, lineated valley fill, and concentric crater fill) and pedestal craters; glacial features were assigned a value of 1 and pedestal craters a value of 0.75. Radar Surface Power Return Last Updated: 2019-04-03 09:34:15
The SHARAD surface power was measured and corrected for surface topography using a combination of MOLA and SHARAD surface roughness data. Low power values provide a proxy for the bulk density of the upper ~ 5m. Due to the low density of ice relative to other martian near surface materials, lower power returns are assumed to be consistent with the presence of ice. Consistency values were assigned according to the global power distribution: < -1σ = 1; -1σ - -0.5σ = 0.5; -0.5σ - 0.5σ = 0; 0.5σ = 1σ = 0.5; >1σ = -1 Thermal Analysis Last Updated: 2019-04-03 09:35:33
TES and THEMIS were integrated into a single thermal consistency map, with TES providing near-global coverage and THEMIS contributing at select locations. For regions where only TES data were considered, matches to models of low ATI units over high ATI units were assigned values of +1 and matches to models of high ATI units over low ATI units were assigned values of -1. If no good match to models was found or the top layer thickness of the matched model had a diurnal skin depth of d <1, we assigned a value of 0. At locations where THEMIS data was considered, it was compared directly to TES and we applied equivalent criteria when assigning consistency values. In the LPSC products, consideration of THEMIS data did not modify results derived from TES products only. Arcadia: Composite
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Description
PNG
TIFF
GeoTIFF
Anc. Data
Combined Ice Consistency Map Last Updated: 2019-04-03 10:34:28
The ice consistency map is generated by combining all the individual consistency maps through the "SWIM Equation", i.e. Neutron; Thermal; Radar Surface Power; Geomorphology and Radar Dielectric Estimates. The ice consistency map is designed to show where all data sets are consistent or inconsistent with the presence of ice over a combined sensing depth of ~5m, indicating the likely presence of ice or lack thereof in that location. Arcadia: Individual Data Sets
Product Name
Description
PNG
TIFF
GeoTIFF
Anc. Data
Geomorphology Last Updated: 2019-04-03 10:36:53
The geomorphology map was generated using previous and new mapping of periglacial and glacial features. For the Arcadia and Onilus regions, a number of periglacial features were identified between 30-60°N. A sampling of 4x4° CTX image mosaics within previously mapped units was used to tally the number of periglacial and glacial landforms and to extrapolate the observations to the mapped unit boundaries. The number of landforms were then normalized to yield values between 0 and 1. Few to no periglacial features were observed equatorward of 30°N, and these areas were assigned a preliminary value of -1. For Utopia and Acidalia, the southernmost extent of latitude dependent mantle was estimated and mapped using previously published observations and scalloped terrain was mapped in Utopia. Areas north of the mantle boundary were assigned a value of 0.5 and scalloped terrain was assigned a value of 0.75. Previous mapping was also used as input for all regions, including glacial features (lobate debris aprons, lineated valley fill, and concentric crater fill) and pedestal craters; glacial features were assigned a value of 1 and pedestal craters a value of 0.75. MONS Analysis Last Updated: 2019-04-03 10:37:52
Pathare et al. [2018] produced the most definitive global maps to date of near-surface water-equivalent hydrogen in an upper layer (W_up), lower layer (W_dn), and depth (D) to the lower layer. They refined the cross-over technique developed by Feldman et al. [2011] to produce the first maps of self-consistent W_up, W_dn, and D. These W_dn results (expressed in terms of WEH as percentages) were then scaled to fit into the SWIM equation. If W_dn ≥ 25% -> 1, 10% ≤ W_dn < 25% scaled between 0 and 1, 5% ≤ W_dn < 10% scaled between -1 and 0, and W_dn < 5% -> -1. Thermal Analysis Last Updated: 2019-04-03 10:38:41
TES and THEMIS were integrated into a single thermal consistency map, with TES providing near-global coverage and THEMIS contributing at select locations. For regions where only TES data were considered, matches to models of low ATI units over high ATI units were assigned values of +1 and matches to models of high ATI units over low ATI units were assigned values of -1. If no good match to models was found or the top layer thickness of the matched model had a diurnal skin depth of d <1, we assigned a value of 0. At locations where THEMIS data was considered, it was compared directly to TES and we applied equivalent criteria when assigning consistency values. In the LPSC products, consideration of THEMIS data did not modify results derived from TES products only. Radar Surface Power Return Last Updated: 2019-04-03 10:39:21
The SHARAD surface power was measured and corrected for surface topography using a combination of MOLA and SHARAD surface roughness data. Low power values provide a proxy for the bulk density of the upper ~ 5m. Due to the low density of ice relative to other martian near surface materials, lower power returns are assumed to be consistent with the presence of ice. Consistency values were assigned according to the global power distribution: < -1σ = 1; -1σ - -0.5σ = 0.5; -0.5σ - 0.5σ = 0; 0.5σ = 1σ = 0.5; >1σ = -1 Est. Dielectric Properties Last Updated: 2019-04-03 10:40:39
To estimate the dielectric properties we found locations where the approximate depth of a subsurface reflector can be estimated based on the surrounding topography then based on that depth and the two-way travel time we calculate the real dielectric permittivity (ε’) of the unit above the reflector. In regions where this can be done readily, primarily around Lobate Debris Aprons, we simply took these estimates for the whole region. In regions where the dielectric cannot be found as easily we estimate the dielectric at as many points as possible then interpolate between those points across the extent of the reflector. To generate the estimated dielectric properties ice consistency map we applied equation C_rd = ε’x*0.5+2.5 to our estimates of the dielectric, effectively mapping lower dielectric to being more consistent with ice. Est. Depth to Subsurface Interface Last Updated: 2019-04-03 10:41:56
For this step we prescribe the median real dielectric permittivity estimated for individual classes of features. These classes include lobate debris aprons/lineated valley fill, Onilus upper plains mantle, Utopia mantle, and Acidalia mantle. The median dielectric permittivities are then used to calculate the depth at each point for which a subsurface reflector is mapped. Onilus: Composite
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Description
PNG
TIFF
GeoTIFF
Anc. Data
Combined Ice Consistency Map Last Updated: 2019-04-03 10:49:26
The ice consistency map is generated by combining all the individual consistency maps through the "SWIM Equation", i.e. Neutron; Thermal; Radar Surface Power; Geomorphology and Radar Dielectric Estimates. The ice consistency map is designed to show where all data sets are consistent or inconsistent with the presence of ice over a combined sensing depth of ~5m, indicating the likely presence of ice or lack thereof in that location. Onilus: Individual Data Sets
Product Name
Description
PNG
TIFF
GeoTIFF
Anc. Data
Geomorphology Last Updated: 2019-04-03 10:53:30
The geomorphology map was generated using previous and new mapping of periglacial and glacial features. For the Arcadia and Onilus regions, a number of periglacial features were identified between 30-60°N. A sampling of 4x4° CTX image mosaics within previously mapped units was used to tally the number of periglacial and glacial landforms and to extrapolate the observations to the mapped unit boundaries. The number of landforms were then normalized to yield values between 0 and 1. Few to no periglacial features were observed equatorward of 30°N, and these areas were assigned a preliminary value of -1. For Utopia and Acidalia, the southernmost extent of latitude dependent mantle was estimated and mapped using previously published observations and scalloped terrain was mapped in Utopia. Areas north of the mantle boundary were assigned a value of 0.5 and scalloped terrain was assigned a value of 0.75. Previous mapping was also used as input for all regions, including glacial features (lobate debris aprons, lineated valley fill, and concentric crater fill) and pedestal craters; glacial features were assigned a value of 1 and pedestal craters a value of 0.75. MONS Analysis Last Updated: 2019-04-03 10:55:35
Pathare et al. [2018] produced the most definitive global maps to date of near-surface water-equivalent hydrogen in an upper layer (W_up), lower layer (W_dn), and depth (D) to the lower layer. They refined the cross-over technique developed by Feldman et al. [2011] to produce the first maps of self-consistent W_up, W_dn, and D. These W_dn results (expressed in terms of WEH as percentages) were then scaled to fit into the SWIM equation. If W_dn ≥ 25% -> 1, 10% ≤ W_dn < 25% scaled between 0 and 1, 5% ≤ W_dn < 10% scaled between -1 and 0, and W_dn < 5% -> -1. Thermal Analysis Last Updated: 2019-04-03 10:57:21
TES and THEMIS were integrated into a single thermal consistency map, with TES providing near-global coverage and THEMIS contributing at select locations. For regions where only TES data were considered, matches to models of low ATI units over high ATI units were assigned values of +1 and matches to models of high ATI units over low ATI units were assigned values of -1. If no good match to models was found or the top layer thickness of the matched model had a diurnal skin depth of d <1, we assigned a value of 0. At locations where THEMIS data was considered, it was compared directly to TES and we applied equivalent criteria when assigning consistency values. In the LPSC products, consideration of THEMIS data did not modify results derived from TES products only. Radar Surface Power Return Last Updated: 2019-04-03 11:01:38
The SHARAD surface power was measured and corrected for surface topography using a combination of MOLA and SHARAD surface roughness data. Low power values provide a proxy for the bulk density of the upper ~ 5m. Due to the low density of ice relative to other martian near surface materials, lower power returns are assumed to be consistent with the presence of ice. Consistency values were assigned according to the global power distribution: < -1σ = 1; -1σ - -0.5σ = 0.5; -0.5σ - 0.5σ = 0; 0.5σ = 1σ = 0.5; >1σ = -1 Est. Dielectric Properties Last Updated: 2019-04-03 11:44:57
To estimate the dielectric properties we found locations where the approximate depth of a subsurface reflector can be estimated based on the surrounding topography then based on that depth and the two-way travel time we calculate the real dielectric permittivity (ε’) of the unit above the reflector. In regions where this can be done readily, primarily around Lobate Debris Aprons, we simply took these estimates for the whole region. In regions where the dielectric cannot be found as easily we estimate the dielectric at as many points as possible then interpolate between those points across the extent of the reflector. To generate the estimated dielectric properties ice consistency map we applied equation C_rd = ε’x*0.5+2.5 to our estimates of the dielectric, effectively mapping lower dielectric to being more consistent with ice. Est. Depth to Subsurface Interface Last Updated: 2019-04-03 11:46:08
For this step we prescribe the median real dielectric permittivity estimated for individual classes of features. These classes include lobate debris aprons/lineated valley fill, Onilus upper plains mantle, Utopia mantle, and Acidalia mantle. The median dielectric permittivities are then used to calculate the depth at each point for which a subsurface reflector is mapped. Utopia: Composite
Product Name
Description
PNG
TIFF
GeoTIFF
Anc. Data
Combined Ice Consistency Map Last Updated: 2019-04-03 11:49:20
The ice consistency map is generated by combining all the individual consistency maps through the "SWIM Equation", i.e. Neutron; Thermal; Radar Surface Power; Geomorphology and Radar Dielectric Estimates. The ice consistency map is designed to show where all data sets are consistent or inconsistent with the presence of ice over a combined sensing depth of ~5m, indicating the likely presence of ice or lack thereof in that location. Utopia: Individual Data Sets
Product Name
Description
PNG
TIFF
GeoTIFF
Anc. Data
Geomorphology Last Updated: 2019-04-03 11:51:28
The geomorphology map was generated using previous and new mapping of periglacial and glacial features. For the Arcadia and Onilus regions, a number of periglacial features were identified between 30-60°N. A sampling of 4x4° CTX image mosaics within previously mapped units was used to tally the number of periglacial and glacial landforms and to extrapolate the observations to the mapped unit boundaries. The number of landforms were then normalized to yield values between 0 and 1. Few to no periglacial features were observed equatorward of 30°N, and these areas were assigned a preliminary value of -1. For Utopia and Acidalia, the southernmost extent of latitude dependent mantle was estimated and mapped using previously published observations and scalloped terrain was mapped in Utopia. Areas north of the mantle boundary were assigned a value of 0.5 and scalloped terrain was assigned a value of 0.75. Previous mapping was also used as input for all regions, including glacial features (lobate debris aprons, lineated valley fill, and concentric crater fill) and pedestal craters; glacial features were assigned a value of 1 and pedestal craters a value of 0.75. MONS Analysis Last Updated: 2019-04-03 11:52:57
Pathare et al. [2018] produced the most definitive global maps to date of near-surface water-equivalent hydrogen in an upper layer (W_up), lower layer (W_dn), and depth (D) to the lower layer. They refined the cross-over technique developed by Feldman et al. [2011] to produce the first maps of self-consistent W_up, W_dn, and D. These W_dn results (expressed in terms of WEH as percentages) were then scaled to fit into the SWIM equation. If W_dn ≥ 25% -> 1, 10% ≤ W_dn < 25% scaled between 0 and 1, 5% ≤ W_dn < 10% scaled between -1 and 0, and W_dn < 5% -> -1. Thermal Analysis Last Updated: 2019-04-03 11:54:38
TES and THEMIS were integrated into a single thermal consistency map, with TES providing near-global coverage and THEMIS contributing at select locations. For regions where only TES data were considered, matches to models of low ATI units over high ATI units were assigned values of +1 and matches to models of high ATI units over low ATI units were assigned values of -1. If no good match to models was found or the top layer thickness of the matched model had a diurnal skin depth of d <1, we assigned a value of 0. At locations where THEMIS data was considered, it was compared directly to TES and we applied equivalent criteria when assigning consistency values. In the LPSC products, consideration of THEMIS data did not modify results derived from TES products only. Radar Surface Power Return Last Updated: 2019-04-03 11:56:11
The SHARAD surface power was measured and corrected for surface topography using a combination of MOLA and SHARAD surface roughness data. Low power values provide a proxy for the bulk density of the upper ~ 5m. Due to the low density of ice relative to other martian near surface materials, lower power returns are assumed to be consistent with the presence of ice. Consistency values were assigned according to the global power distribution: < -1σ = 1; -1σ - -0.5σ = 0.5; -0.5σ - 0.5σ = 0; 0.5σ = 1σ = 0.5; >1σ = -1 Est. Dielectric Properties Last Updated: 2019-04-03 12:00:58
To estimate the dielectric properties we found locations where the approximate depth of a subsurface reflector can be estimated based on the surrounding topography then based on that depth and the two-way travel time we calculate the real dielectric permittivity (ε’) of the unit above the reflector. In regions where this can be done readily, primarily around Lobate Debris Aprons, we simply took these estimates for the whole region. In regions where the dielectric cannot be found as easily we estimate the dielectric at as many points as possible then interpolate between those points across the extent of the reflector. To generate the estimated dielectric properties ice consistency map we applied equation C_rd = ε’x*0.5+2.5 to our estimates of the dielectric, effectively mapping lower dielectric to being more consistent with ice. Est. Depth to Subsurface Interface Last Updated: 2019-04-03 12:03:11
For this step we prescribe the median real dielectric permittivity estimated for individual classes of features. These classes include lobate debris aprons/lineated valley fill, Onilus upper plains mantle, Utopia mantle, and Acidalia mantle. The median dielectric permittivities are then used to calculate the depth at each point for which a subsurface reflector is mapped.
