Many previous studies have primarily focused on the use of deep learning for interferometric processing or separate recognition purposes rather than targeted measurements of detected wellpads. The present study centered around the integration of deep learning recognition and interferometric measurements for Tengiz oilfield wellpads. This study proposes the optimization, automation, and acceleration of targeted ground deformation wellpad monitoring. Mask Region-based Convolutional Neural Network (R-CNN)-based deep learning wellpad recognition and consequent Small Baseline Subset Synthetic Aperture Radar Interferometry (SBAS-InSAR) analyses were used for the assessment of ground deformation in the wellpads. The Mask R-CNN technique allowed us to detect 159 wells with a confidence level of more than 95%. The Mask R-CNN model achieved a precision value of 0.71 and a recall value of 0.91. SBAS-InSAR interferometric measurements identified 13 wells for Sentinel-1 (SNT1), 8 wells for COSMO-SkyMed (CSK), and 20 wells for TerraSAR-X (TSX) located within the −54–−40 mm/y class of vertical displacement (VD) velocity. Regression analyses for the annual deformation velocities and cumulative displacements (CD) of wells derived from SNT1, CSK, and TSX satellite missions showed a good agreement with R² > 95. The predictions for cumulative displacements showed that the vertical subsidence processes will continue and reach −339 mm on 31 December 2023, with increasing spatial coverage and the potential to impact a higher number of wells. The hydrological analyses in the Tengiz oilfield clearly demonstrated that water flow has been moving towards the detected hotspot of subsidence and that its accumulation will increase with increasing subsidence. This detected subsidence hotspot was observed at a crossing with a seismic fault that might always be subject to reactivation. The role of this seismic fault should also be investigated as one of the ground deformation-controlling factors, even though this area is not considered seismically active. The primary practical and scientific values of these studies were identified for the operational risk assessment and maintenance needs of oilfield and gas field operators.

The primary objective of this study was to assess differential vertical and horizontal deformations for the offshore Kashagan oilfield located in the Northern Caspian Sea. Sentinel-1 (SNT1) and COSMO-SkyMed (CSK) synthetic-aperture radar (SAR) images (9 January 2018–6 April 2022) were processed using persistent scatterer interferometric SAR (PS-InSAR) technique with further 2D decomposition of line-of-sight (LOS) measurements to differential vertical and horizontal deformations. Differential vertical deformation velocity was observed to be between −4 mm/y and 4 mm/y, whereas horizontal was between −4 mm/y and 5 mm/y during 2018–2022. However, it was possible to observe the spatial deformation patterns with the subsidence hotspots reaching differential cumulative vertical displacement of −20 mm from both satellite missions. PS-InSAR differential vertical deformation measurements derived from SNT1 and CSK satellite images showed identical spatial patterns with moderate agreement, whereas poor agreement was observed for differential horizontal deformations. The differential vertical deformation hotspots were observed for the oilfield areas installed on piles with obviously higher vulnerability to dynamic movements. Through this study, based on the interferometric measurements, marine geotechnical expert feedback, and no reported deformation-related incidents since 2013, it was possible to conclude that the Kashagan oilfield had not been impacted by significant differential vertical and horizontal deformations on the oilfield. However, since long-term GPS measurements were not accessible from the oilfield to be used as the reference for PS-InSAR measurements, we were not able to judge the long-term displacements of the entire oilfield or possible oscillations, even though it is built on the artificial island. Considering the broad range of PS-InSAR measurements using time-series radar images, the interferometric measurements could play a significant role in the prioritization of insitu risk assessment activities, operational cost reduction, strengthening of safety factors, and planning of further targeted insitu measurements.

Plastic pollution poses a major environmental threat to coastal ecosystems, particularly in enclosed and semi-enclosed seas where limited water exchange promotes debris accumulation. This study presents a high-resolution spatial analysis of coastal plastic debris along the Khachmaz coastline in the western Caspian Sea. The analysis integrates unmanned aerial vehicle (UAV) imagery, YOLO-based deep learning detection, and spatial statistical methods. High-resolution UAV orthophotos enabled the automated detection of individual plastic debris items, which were converted into spatial point data for further analysis. Spatial patterns were assessed using areal density estimation, nearest neighbor analysis, kernel density estimation, and Ripley’s L-function to examine clustering across multiple spatial scales. A total of 2389 plastic debris items were identified within 0.0439 km², corresponding to an average density of 54,382 items per km². The results show that plastic debris is unevenly distributed, forming distinct clusters with four primary accumulation hotspots. Significant clustering occurs at spatial scales up to 20 m, with the strongest aggregation observed at distances below 5 m. Spatial overlay analysis indicates a strong association between plastic debris, reed-dominated coastal vegetation, and proximity to the shoreline, suggesting the potential role of localized retention processes and shoreline dynamics in debris accumulation. The combined use of UAV-based deep learning and spatial statistical analysis provides an integrated application framework for monitoring coastal plastic debris and supports targeted, sustainability-oriented coastal management strategies in the Caspian Sea region.

The Absheron Peninsula contains a dense network of Caspian ports and industrial terminals where vertical land motion (VLM) may affect infrastructure performance. We analyzed fourteen port-associated coastal analysis zones using ascending and descending Sentinel-1 SBAS-InSAR observations from 2017 to 2024. Two-geometry decomposition was used to derive the vertical-deformation component from the complementary ascending and descending viewing geometries. The two-dimensional solution also provides an east–west component, but the present study is specifically focused on vertical land motion; therefore, east–west deformation is not analyzed or presented as a separate result. The two-geometry decomposition does not provide an independently resolved north–south component. VLM was evaluated from peninsula scale to individual port sectors and compared with supplied cumulative GPS vertical-displacement measurements at two stations. Port-mean velocities range from −2.89 to +1.69 mm/year, while localized port measurements reach approximately −23 mm/year and cumulative displacement about −180 mm. These port-scale extremes are distinct from larger isolated regional map values outside the port-associated analysis zones. Strong inter-port and intra-port variability shows that port-wide means can mask localized deformation. The study provides a network-wide radar screening baseline for prioritizing engineering and geodetic follow-up. Because the InSAR solution is relative to automatically selected reference pixels and the ground comparison is limited to supplied cumulative GPS measurements from two stations with incomplete station metadata, the results are interpreted as relative deformation indicators rather than a geocentric structural-safety assessment.