The Mohorovĩcíc discontinuity (Moho) is the boundary between the Earth's crust and upper mantle. It is critical to determine accurate Moho depth for gaining insight into the deep structure of the Earth, material and energy exchange processes, and geodynamic problems (Ebbing et al., 2018;Gozzard et al., 2019;Xu et al., 2017). Currently, the seismic and gravimetric methods are the primary geophysical methods for determining the depth of Moho. Seismic methods are quite expensive, and seismic stations are sparse in many regions with extreme conditions (e.g., ocean, plateau, and polar). In addition, seismic methods could be ineffective for recovering three-dimensional (3D) Moho topography across vast regions. Along with seismic surveys, gravity observations can also be used to estimate the Moho depth. In recent decades, as satellite-gravity missions, the Gravity Recovery and Climate Experiment (GRACE) (Tapley et al., 2004), the Gravity field and steady-state Ocean Circulation Explorer (GOCE) (Floberghagen et al., 2011), GRACE Follow-On (GRACE-FO) (Kornfeld et al., 2019), and satellite altimetry (Sandwell et al., 2014) have advanced, it is now possible to obtain high resolution and accurate gravity data with almost global and homogeneous coverage. At the moment, several global gravity field models (GGMs) have been constructed with high accuracy and resolution by combining terrestrial, altimetric, and satellite gravity data, such as EGM2008 (Pavlis et al., 2012), EIGEN-6C4 (Förste et al., 2014), and XGM2019e_2159 (Zingerle et al., 2020. These GGMs with maximum degree/order of 2190 (Spatial resolution ∼10 km) have been
Joshimath has received much attention for its massive ground subsidence at the beginning of the year. Rapid urbanization and its unique geographical location may have been one of the factors contributing to the occurrence of this geological disaster. In high mountain valley areas, the complex occurrence mechanism and diverse disaster patterns of geological hazards highlight the inadequacy of manual monitoring. To address this problem, the inversion of deformation of the Joshimath surface in multiple directions can be achieved by multidimensional InSAR techniques. Therefore, in this paper, the multidimensional SBAS-InSAR technique was used to process the lift-track Sentinel-1 data from 2020 to 2023 to obtain the two-dimensional vertical and horizontal deformation rates and time series characteristics of the Joshimath ground surface. To discover the causes of deformation and its correlation with anthropogenic activities and natural disasters by analyzing the spatial and temporal evolution of surface deformation. The results show that the area with the largest cumulative deformation is located in the northeastern part of the town, with a maximum cumulative subsidence of 271.2 mm and a cumulative horizontal movement of 336.5 mm. The spatial distribution of surface deformation is based on the lower part of the hill and develops towards the upper part of the hill, showing a trend of expansion from the bottom to the top. The temporal evolution is divided into two phases: gentle to rapid, and it is tentatively concluded that the decisive factor that caused the significant change in the rate of surface deformation and the early onset of the geological subsidence hazard was triggered by the 4.7 magnitude earthquake that struck near the town on 11 September 2021.
The ocean covers 71% of the Earth's surface. At present, only about 20% of the seafloor topography (ST) has been directly measured by ships, and most areas are predicted from satellite altimetry‐derived gravity products. In this study, an adaptive nonlinear iterative (ANI) method is proposed to address two major problems in gravity ST inversion: linear approximation and empirical seafloor density contrast (SDC). In ANI, the SDC is adaptively estimated as an output, while higher‐order Parker expansion and modified Bott's iteration are combined to recover nonlinear topography. We apply our new method using the DTU21GRA altimetric gravity model and single‐beam bathymetry to predict the ST in a part of the South China Sea. Results reveal that the average SDC in the study area is 1.24 g/cm3, which compares well to CRUST1.0. The root‐mean‐square (RMS) error between the nonlinear model and single‐beam checkpoints is 102.1 m, which is improved by 34.5%, 29.2%, and 18.3% compared with the non‐gravity model, topo_24.1, and linear model, respectively. The RMS error between the nonlinear model and multibeam bathymetry is 91.0 m, which is better than the linear model. Analysis of two‐dimensional profiles shows that the nonlinear model reveals more terrain details than the linear model.
Marine gravimetry provides high-quality gravity measurements, particularly in coastal areas. After the update of new sensors in GFZ’s air-marine gravimeter Chekan-AM, gravimetry measurements showed a significant improvement from the first new campaign DENEB2017 with an accuracy of 0.3/2=0.21 mGal @ 1 km along the tracks, which is at the highest accuracy level of marine gravimetry. Then, these measurements were used to assess gravity data derived from satellite altimetry (about 3 mGal) and a new finding is that a bias of −1.5 mGal exists in the study area. Additionally, ship soundings were used to assess existing seafloor topography models. We found that the accuracy of SRTM model and SIO model is at a level of 2 m, while the accuracy of the regional model EMODnet reaches the lever of sub-meters. Furthermore, a bias of 0.7 m exists and jumps above 5 m in the SRTM model near the coast of Sweden. Finally, new combined gravity anomalies with sounding data are used to reveal the fine structure of ocean topography. Our estimated seafloor topography model is more accurate than existing digital elevation data sets such as EMODnet, SRTM and SIO models and, furthermore, shows some more detailed structure of seafloor topography. The marine gravimetry and sounding measurements as well as the estimated seafloor topography are crucial for future geoid determination, 3D-navigation and resource exploration in the Baltic Sea.
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