Land motions are significantly widespread in the Yellow River delta (YRD). There is, however, a lack of understanding of the delta-wide comprehensive deformation mode and its dynamic mechanism, especially triggered by groundwater extraction. This paper adopts an integrated analysis of multidisciplinary data of image geodesy, geophysics, geology and hydrogeology to provide insights into Earth surface displacement patterns and dynamics in the YRD. Delta-scale land motions were measured for the first time using L-band ALOS images processed using multi-temporal InSAR, illustrating multiple obvious surface sinking regions and a maximum annual subsidence velocity of up to 130 mm. Then, the InSAR-constrained distributed point source model with optimal kernel parameters, a smoothness factor of 10 and a model grid size of 300 m was established and confirmed to be rational, reliable and accurate for modeling analysis over the YRD. Remarkable horizontal surface displacements, moving towards and converging on a sinking center, were recovered by means of modeling and measured using InSAR, with a maximum rate of up to 60 mm per year, which can trigger significant disasters, such as ground fissures and building damage. In addition, the annual total water storage variation at the delta scale, the most meaningful outcome, can be calculated and reaches a total of approximately 12,010 × 10³ m³ in Guangrao city, efficiently filling the gap of GRACE and in situ investigations for delta-wide aquifer monitoring. Finally, a comparative analysis of time series InSAR measurements, modeling outcomes, and fault and groundwater data was conducted, and the strong agreement demonstrates that faults control aquifer distribution and hence the spatial distribution of groundwater-withdrawal-related regional land subsidence. Moreover, the obvious asymmetric displacements, demonstrating a northeasterly displacement trend, further reveal that faults control aquifer distribution and Earth surface deformation. These findings are useful for understanding the land motion patterns and dynamics, helping to sustainably manage groundwater and control disasters in the YRD and elsewhere worldwide.

The development of remaining oil plays an important role in increasing the late production, and low-grade faults seriously impacts residual oil exploration and development. Low-grade faults have a small fault displacement, brief extension, and strong concealment, which hinders their prediction using the typical semantic segmentation network. To intelligently identify low-grade faults, we designed a codec target edge detection technique. For the network to fully learn the low-grade faults information, we constructed the encoder using dilated convolution. Next, we introduced an attention mechanism to the decoder to improve capture of location information from a shallow network and semantic information from a deep network. Finally, the multi-scale fusion decoder outputs fault information of different scales, which further improves the identification accuracy of low-grade faults. The training model is applied to simulated data and actual seismic data through ablation experiments. The results show that this method can effectively identify low-grade faults and overcomes problems such as blurred fault cross-location, thicker edge contour lines, lower detection accuracy, and less training data. Compared with conventional a Holistically-Nested Edge Detection (HED) and a semantic segmentation (UNet), fault misidentification is reduced, fault continuity is increased, and fault accuracy is improved. providing technical support for the exploration and development of remaining oil and increasing the recovery rate of old oil fields.

To understand the influence of sea ice on shipborne gravity measurements and the accuracy of the satellite-altimetry-derived gravity field in the Arctic Ocean, we compared shipborne gravity measurements with those obtained from satellite altimetric gravity measurements. The influence of sea ice on the shipborne gravity measurements was mainly concentrated in the 0–6 km wavelength range, and the standard deviation of the noise amplitudes was 2.62 mGal. Compared to ice-free regions, the accuracies in the region with floating ice were reduced by 13% for DTU21 and 6% for SV31. Due to the influence of sea ice, satellite altimetric gravity data lose significant information in the 9–12 km wavelength range. The coherence curve of the shipborne gravity with bathymetry was nearly the same as that of the satellite altimetric gravity. The satellite data contain nearly all of the significant information that is present in the shipborne data. The differences between the shipborne and satellite gravity data are small and can be used to study the crustal structure of the Arctic.

Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample set and a self-constructed convolutional neural network to recognize low-grade faults. We used Wu’s method to generate 500 pairs of low-grade fault samples to provide the data for deep learning. By combining the attention mechanism with UNet, an SE-UNet with efficient allocation of limited attention resources was constructed, which can select the features that are more critical to the current task objective from ample feature information, thus improving the expression ability of the network. The network model is applied to real data, and the results show that the SE-UNet model has better generalization ability and can better recognize low-grade and more continuous faults. Compared with the original UNet model, the SE-UNet model is more accurate and has more advantages in recognizing low-grade faults.

To understand the tectonic–magmatic history, crustal structure and crustal accretion mode of the Eurasian Basin in the Arctic, we calculated the crustal thickness, residual bathymetry (RB) and non-isostatic topography of the Eurasian Basin by using the latest bathymetry, free-air gravity anomaly, crustal age and sediment thickness data. The tectonic–magmatic process of the Eurasian Basin can be divided into two stages. During magnetic isochrons 24-13 (C24-C13), the crustal structure of the basin was characterized by regional variations, while the crustal structure of the basin was characterized by local variations during magnetic isochrons 13-0 (C13-0). On the whole, the western part of the basin had a thicker crustal thickness and higher RB than the eastern part of the basin during C24-C13, which should result from the northward movement of Greenland. During magnetic isochrons 24-20 (C24-C20), the crustal structure of the eastern part of the basin had abnormally strong asymmetry. We speculate that there may be mantle upwelling beneath the Kara Sea Shelf in the south of the Eastern Eurasian Basin, which provides a large amount of melt for the crustal accretion of the southern part of the Eastern Eurasian Basin. The melt focusing supply could generate abnormally thick crust (>7 km) during magma enhancement period. The Western Eurasian Basin had stronger spatial variability and more frequency asymmetric polarity reversal than the Eastern Eurasian Basin during magnetic isochrons 6-0 (C6-0). We attribute this to the inflow of the North Atlantic mantle.

Qiongdongnan Basin (QDNB), located at the northwestern corner of the South China Sea (SCS), is a key juncture between the extensional tectonic regime in the northern continental margin and the shear tectonic regime in the western continental margin. Analyzing the crustal density structure and tracking the thermodynamic controlling factors are effective approaches to reveal the nonuniform breakup process of the northwestern SCS. Herein, focusing on the obvious tectonic deformation with distinct eastern and western parts in the QDNB, we present the crustal density structures of five profiles and identify the high-density anomaly related to the synrifting mantle underplating and postrifting magmatic intrusions. The crustal density model was constructed from the Bouguer gravity anomaly, ocean bottom seismic profiles, and multichannel seismic reflection profiles. The northern part of QDNB, with normal crustal density, lower surface heat flow of <55 mW/m², and limited extension factor of 1.25–1.70, is recognized as the initial nonuniform extension continental crust. The mantle underplating beneath the QDNB is identified as a high mantle density of 3.30–3.40 g/cm³ and a high lower crustal density of 2.92–2.96 g/cm³, which is usually recognized by the high-velocity layers in the northeastern margin of SCS. The magmatic intrusions are identified as the high-density bodies ranging from 3.26 g/cm³ at the base to 2.64 g/cm³ at the top, which become stronger from the west to east. The central part of Xisha Trough is featured by the cooling of the heavily thinned lower crust in the final continental rifting stage, which is close to the cold and rigid oceanic crust. Lateral variations in the deep magmatic anomaly should be the crucial factor for the nonuniform breakup process in the northwestern margin of SCS.