A community effort to construct a gravity database for the United States and an associated Web portal

Keller, G. Randy; Hildenbrand, Thomas G.; Kucks, R.P.; Webring, Michael W.; Briesacher, Allen; Rujawitz, Kristine; Hittleman, A.M.; Roman, D. R.; Winester, D.; Aldouri, Raed; Seeley, John; Rasillo, Jorge; Torres, Roberto; Hinze, William J.; Gates, Ann Q.; Крейнович, Владик; Salayandia, Leonardo

doi:10.1130/2006.2397(02)

Cited by 18 publications

(13 citation statements)

References 8 publications

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“…Over 65,000 terrain-corrected Bouguer gravity measurements for this region were obtained from the Pan-American Center for Earth and Environmental Studies (PACES) U.S. gravity database [Keller et al, 2006]. Bouguer gravity at each surface location is predicted from the density model by first calculating the vertical gravitation due to each model block with the analytic solution for a rectangular prism [Nagy et al, 2000], which reduces near-surface discretization effects.…”

Section: Flexure and Gravity Modelingmentioning

confidence: 99%

A rootless rockies—Support and lithospheric structure of the Colorado Rocky Mountains inferred from CREST and TA seismic data

Hansen

Dueker

Stachnik

et al. 2013

Geochem Geophys Geosyst

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[1] Support for the Colorado high topography is resolved using seismic data from the Colorado Rocky Mountain (CRM) Experiment and Seismic Transects. The average crustal thickness, derived from P wave receiver function imaging, is 48 km. However, a negative correlation between Moho depth and elevation is observed, which negates Airy-Heiskanen isostasy. Shallow Moho (<45 km depth) is found beneath some of the highest elevations, and therefore, the CRM are rootless. Deep Moho (45-51 km) regions indicate structure inherited from the Proterozoic assembly of the continent. Shear wave velocities from surface wave tomography are mapped to density employing empirical velocity-to-density relations in the crust and mantle temperature modeling. Predicted elastic plate flexure and gravity fields derived from the density model agree with observed long-wavelength topography and Bouguer gravity. Therefore, lowdensity crust and mantle are sufficient to support much of the CRM topography. Centers of Oligocene volcanism, e.g., the San Juan Mountains, display reduced crustal thicknesses and lowest average crustal velocities, suggesting that magmatic modification strongly influenced modern lithospheric structure and topography. Mantle velocities span 4.02-4.64 km/s at 73-123 km depth, and peak lateral temperature variations of 600 C are inferred. S wave receiver function imaging suggests that the lithosphereasthenosphere boundary is 100-150 km deep beneath the Colorado Plateau, and 150-200 km deep beneath the High Plains. A region of negative arrivals beneath the CRM above 100 km depth correlates with low mantle velocities and is interpreted as thermally modified/metasomatized lithosphere resulting from Cenozoic volcanism.

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Section: Flexure and Gravity Modelingmentioning

confidence: 99%

A rootless rockies—Support and lithospheric structure of the Colorado Rocky Mountains inferred from CREST and TA seismic data

Hansen

Dueker

Stachnik

et al. 2013

Geochem Geophys Geosyst

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“…The main objective of cyberinfrastructure is to be able to seamlessly move data between different databases (where this data is stored in different formats), to feed the combined data into a remotely located program (which may require yet another data format), and to return the result to the user; see, e.g., [1,12,25]. It is also important to gauge the quality and accuracy of this result.…”

Section: Case Of Probabilistic Prior Knowledgementioning

confidence: 99%

Towards Combining Probabilistic, Interval, Fuzzy Uncertainty, and Constraints: An Example Using the Inverse Problem in Geophysics

Kreinovich

Starks

Araiza

et al. 2006

12th GAMM - IMACS International Symposium on Scientific Computing, Computer Arithmetic and Validated Numerics (SCAN 2006)

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In many real-life situations, we have several types of uncertainty: measurement uncertainty can lead to probabilistic and/or interval uncertainty, expert estimates come with interval and/or fuzzy uncertainty, etc. In many situations, in addition to measurement uncertainty, we have prior knowledge coming from prior data processing and/or prior knowledge coming from prior interval constraints.In this paper, on the example of the seismic inverse problem, we show how to combine these different types of uncertainty. Seismic Inverse Problem: A Brief DescriptionIn evaluations of natural resources and in the search for natural resources, it is very important to determine Earth structure.Our civilization greatly depends on the things we extract from the Earth, such as fossil fuels (oil, coal, natural gas), minerals, and water. Our need for these commodities is constantly growing, and because of this growth, they are being exhausted. Even under the best conservation policies, there is (and there will be) a constant need to nd new sources of minerals, fuels, and water.The only sure-proof way to guarantee that there are resources such as minerals at a certain location is to actually drill a borehole and analyze the materials extracted. However, exploration for natural resources using indirect means began in earnest during the rst half of the 20th century.The result was the discovery of many large relatively easy to locate resources such as the oil in the Middle East.However, nowadays, most easy-to-access mineral resources have already been discovered. For example, new oil elds are mainly discovered either at large depths, or under water, or in very remote areas in short, in the areas where drilling is very expensive. It is therefore desirable to predict the presence of resources as accurately as possible before we invest in drilling.From previous exploration experiences, we usually have a good idea of what type of structures are symptomatic for a particular region. For example, oil and gas tend to concentrate near the top of natural underground domal structures. So, to be able to distinguish between more promising and less promising locations, it is desirable to determine the structure of the Earth at these locations. To be more precise, we want to know the structure at different depths z at different locations (x, y).Data that we can use to determine the Earth structure.In general, to determine the Earth structure, we can use different measurement results that can be obtained without actually drilling the boreholes: e.g., gravity and magnetic measurements, analyzing the travel-times and paths of seismic ways as they propagate through the earth, etc.To get a better understanding of the Earth structure, we must rely on active seismic data in other words, we must make arti cial explosions, place sensors around them, and measure how the resulting seismic waves propagate. The most important information about the seismic wave is the travel-time t i , i.e., the time that it takes for the wave to travel from its source to ...

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“…This example is exactly what we have been designing under the Cyberinfrastructure for the Geosciences (GEON) project sponsored by the National Science Foundation (NSF); see, e.g., [1], [2], [4], [6], [7], [9], [10], [11], [12], [13], [14], [15], and what we are currently doing under the NSF-sponsored Cyber-Share project. This is similar to what other cyberinfrastructure projects are trying to achieve.…”

Section: Cyberinfrastructure: a Brief Overviewmentioning

confidence: 99%

Degree-based ideas and technique can facilitate inter-disciplinary collaboration and education

Silva

Velasco

Kosheleva

2010

2010 Annual Meeting of the North American Fuzzy Information Processing Society

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Abstract-In many application areas, there is a need for inter-disciplinary collaboration and education. However, such collaboration and education are not easy. On the example of our participation in a cyberinfrastructure project, we show that many obstacles on the path to successful collaboration and education can be overcome if we take into account that each person's knowledge of a statement is often a matter of degree -and that we can therefore use appropriate degree-based ideas and techniques. I. CYBERINFRASTRUCTURE: A BRIEF OVERVIEWBefore we start explaining the problems of inter-disciplinary communication and our proposed solution to this problem, let us first briefly describe the context of cyberinfrastructure in which our inter-disciplinary communication took place.Practical problem: need to combine geographically separate computational resources. In different knowledge domains in science and engineering, there is a large amount of data stored in different locations, and there are many software tools for processing this data, also implemented at different locations. Users may be interested in different information about this domain.Sometimes, the information required by the user is already stored in one of the databases. For example, if we want to know the geological structure of a certain region in Texas, we can get this information from the geological map stored in Austin. In this case, all we need to do to get an appropriate response from the query is to get this data from the corresponding database.In other cases, different pieces of the information requested by the user are stored at different locations. For example, if we are interested in the geological structure of the Rio Grande Region, then we need to combine data from the geological maps of Texas, New Mexico, and the Mexican state of Chihuahua. In such situations, a correct response to the user's query requires that we access these pieces of information from different databases located at different geographic locations.In many other situations, the appropriate answer to the user's request requires that we not only collect the relevant data x 1 , . . . , x n , but that we also use some data processing algorithms f (x 1 , . . . , x n ) to process this data. For example, if we are interested in the large-scale geological structure of a geographical region, we may also use the gravity measurements from the gravity databases. For that, we need special algorithms to transform the values of gravity at different locations into a map that describes how the density changes with location. The corresponding data processing programs often require a lot of computational resources; as a result, many such programs reside on computers located at supercomputer centers, i.e., on computers which are physically separated from the places where the data is stored.The need to combine computational resources (data and programs) located at different geographic locations seriously complicates research.Centralization of computational resources -traditional approach to com...

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A community effort to construct a gravity database for the United States and an associated Web portal

Cited by 18 publications

References 8 publications

A rootless rockies—Support and lithospheric structure of the Colorado Rocky Mountains inferred from CREST and TA seismic data

A rootless rockies—Support and lithospheric structure of the Colorado Rocky Mountains inferred from CREST and TA seismic data

Towards Combining Probabilistic, Interval, Fuzzy Uncertainty, and Constraints: An Example Using the Inverse Problem in Geophysics

Degree-based ideas and technique can facilitate inter-disciplinary collaboration and education

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