Displacements due to surface temperature variation on a uniform elastic sphere with its centre of mass stationary

Fang, Ming; Dong, Danan; Hager, Bradford H.

doi:10.1093/gji/ggt335

Cited by 36 publications

(29 citation statements)

References 16 publications

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“…If there happened to be a source of deformation different but in phase with the GRACE components, then the seasonal signal extracted from the GNSS data might not be entirely caused by surface loading. A prime candidate for this is thermoelastic deformation of solid Earth (Ben‐Zion & Leary, ; Fang et al, ; Tsai, ) and /or eventually of the GNSS monuments (Yan et al, ). Since, in the two study areas presented here, the temporal pattern and the amplitude of the GNSS seasonal signal agree fairly well with the prediction made from GRACE, we are confident that thermoelastic deformation is not a dominant effect in these regions.…”

Section: Discussionmentioning

confidence: 99%

Identification and Extraction of Seasonal Geodetic Signals Due to Surface Load Variations

et al. 2018

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Deformation of the Earth's surface associated with redistributions of continental water mass explains, to first order, the seasonal signals observed in geodetic position time series. Discriminating these seasonal signals from other sources of deformation in geodetic measurements is essential to isolate tectonic signals and to monitor spatio‐temporal variations in continental water storage. We propose a new methodology to identify and extract these seasonal signals. The approach uses a variational Bayesian Independent Component Analysis (vbICA) to extract the seasonal signals and a gravity‐based deformation model to identify which of these signals are caused by surface loading. We test the procedure on two study areas, the Arabian Peninsula and the Nepal Himalaya, and find that the technique successfully extracts the seasonal signals with one or two independent components, depending on whether the load is stationary or moving. The approach is robust to spatial heterogeneities inherent to geodetic measurements and can help extract systematic errors in geodetic products (e.g., draconitic errors). We also discuss how to handle the degree‐1 deformation field present in the geodetic data set but not captured by the gravity‐based model.

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Section: Discussionmentioning

confidence: 99%

Identification and Extraction of Seasonal Geodetic Signals Due to Surface Load Variations

et al. 2018

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“…In addition to the contribution of the influence from regional groundwater variations, some factors unidentified by GRACE and SLMs, such as thermal elastic deformation [49], local load change and the difference in data processing strategy, may also lead to small differences in the results. …”

Section: Analysis and Discussion Of Abnormal Stationmentioning

confidence: 99%

Characterizing the Seasonal Crustal Motion in Tianshan Area Using GPS, GRACE and Surface Loading Models

Zhao

Bao

et al. 2017

Remote Sensing

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Abstract:Complex tectonic and non-tectonic movements exist in the Tianshan area. However, we have not acquired good knowledge of such movements yet. In this study, we combine Global Positioning System (GPS), the Gravity Recovery and Climate Experiment (GRACE) and Surface Loading Models (SLMs) data to study the seasonal vertical crustal displacements in the Tianshan area. The results show that all three datasets exhibit significant annual variations at all 26 local GPS stations. Correlation coefficients higher than 0.8 between the GRACE and GPS data were observed at 85% of the stations, and it became 92% when comparing GPS and SLMs. The Weighted Root Mean Squares (WRMS) reductions were 41% and 47% after removing the annual displacements of GRACE and SLMs from the GPS time series, respectively. The consistency between the GPS and SLMs data was higher than that between the GPS and GRACE data, which is mainly due to the dominant position of atmospheric loading in the study area. For the abnormal station XJYN (43 • N, 81 • E), the GPS time series showed an abnormal uplift from early 2013 to early 2015, but this not shown in the GRACE and SLMs results. We attribute this discrepancy to groundwater variations, which are not resolvable by GRACE and SLMs for small-scale regions.

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“…The Earth's surface temperature could change in both space and time, and thus produces thermoelastic deformation in the Earth's interior. Thermoelastic deformations in the Earth's interior are mainly induced by tractions in the thermal boundary layer, with its decay with depth depending on the horizontal wavelength of the surface temperature field (Ben‐Zion & Leary, ; Berger, ; Fang et al, ). Observations from borehole strainmeters (Lu & Wen, ) have shown that the daily‐cycle S 1 strain tide produced by thermoelastic deformation exhibits amplitude as large as that produced by the atmospheric pressure.…”

Section: Discussionmentioning

confidence: 99%

Loading‐Induced Earth's Stress Change Over Time

Zhou

Wen

2018

JGR Solid Earth

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We establish a physical framework and build a preliminary database of stress changes on the Earth from 2000 to 2017 in a global scale and at various depths. We consider six loading forces that would generate stress changes on the Earth: hydrological loading, atmospheric pressure, ocean water (including tides and nontidal variation), solid lunisolar tides, pole tide, and postglacial rebound (PGR). The maximum amplitudes of normal deviatoric stress changes on the Earth's surface caused by hydrological loading, atmospheric pressure, and solid lunisolar tides reach 10 À2 bar, those by pole tide 10 À4 -10 À3 bar, and those by ocean tides 10 À1 bar mostly in the coastal regions. The shear stress changes are about 1 order of magnitude smaller than the normal deviatoric stresses. The PGR-induced stress rates are 10 À3 -10 À2 bar/year. Stress variations caused by the different forces also exhibit different spatial patterns, with large hydro-induced stresses mainly distributed in torrid and frigid zones, atmosphere-induced stresses in temperate and frigid zones, and ocean-induced stresses in coastlands and PGR-induced stress rates in Greenland, north Europe, North America, and Antarctica. The hydro-induced stresses exhibit significant seasonal variations in Amazon Basin, northern India, and central Africa and persistent increase/decrease in Antarctica, Greenland, and Alaska due to ice shelf melting. All loading-induced stress changes exhibit significant depth variations. The stress database would provide a resource for better understanding the triggering mechanisms of various tectonic events, and the physical framework we establish to build the database could be useful for better studying properties and physical states of Earth's interior.

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Displacements due to surface temperature variation on a uniform elastic sphere with its centre of mass stationary

Cited by 36 publications

References 16 publications

Identification and Extraction of Seasonal Geodetic Signals Due to Surface Load Variations

Identification and Extraction of Seasonal Geodetic Signals Due to Surface Load Variations

Characterizing the Seasonal Crustal Motion in Tianshan Area Using GPS, GRACE and Surface Loading Models

Loading‐Induced Earth's Stress Change Over Time

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