Evaluation of Isfahan City Subsidence Rate Using InSAR and Artificial Intelligence

Sorkhabi, Omid Memarian; Nejad, Ali Sadeghy; Khajehzadeh, Mohammad

doi:10.1007/s12205-022-2013-1

Cited by 19 publications

(6 citation statements)

References 23 publications

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“…The next feed‐forward step begins, after updating the parameters. Since repeating a good number of these steps, the network training ends (Sorkhabi, Nejad, & Khajehzadeh, 2022). In general, a convolutional neural network is a hierarchical neural network in which the convolutional layers are interspersed with the pooling layers, followed by a number of fully connected layers.…”

Section: Methodsmentioning

confidence: 99%

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

Sorkhabi

Milani

Alizadeh

2022

Earth and Space Science

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Observations and processing of Iran's national geodynamic network, global positioning systems (GPSs) track and record signals received from GPS satellites 24 hr a day. The information collected at each station is received in the form of observation and navigation files, meteorological data and tilt meters in the main processing centers, using the telephone line and modem. These centers are located in Tehran and the surveying managements of the surveying organization in the cities of Mashhad, Tabriz, Hamedan and Ahvaz, which receive this information daily. In these data centers, after performing quality control and format conversion, the received data are ready to enter the GPS processing software. Processing in these centers is done on a daily and final basis, the results of which are presented in the form of stations exact coordinates in the international terrestrial reference frame (ITRF) coordinate system, coordinate time series and velocity vectors along with the error ellipses of each station. The results of processing global geodynamic networks are in the form of each station time series, displacement and velocity vectors, and exact ITRF coordinate system and their conversion in the world geodetic system 1984 (WGS84) coordinate system along with their accuracy. These results, as well as observation and navigation files, are stored in data centers (Djamour et al., 2010).Artificial neural network (ANN) has been successfully used in various fields of geodesy. Coordinate transformation with ANN was found to be a practical tool, effective and feasible (Cakir and Konakoglu. 2019). Geoid undulation determination with ANN has root mean square error of 10.81 cm (Konakoglu et al., 2022). Ionospheric variations with ANN has an R value greater than 0.74 (Inyurt and Sekertekin. 2019).Estimation of geodetic velocity is of special importance for geodynamic studies especially fault displacement. GV is determined based on GPS and global navigation satellite system (GNSS) observations at different time intervals and can be used to measure tectonic movements (Sorkhabi, Alizadeh, et al., 2022). GNSS stations, due to their high cost and difficulty in measuring and maintaining them, need methods that can accurately estimate GV in gap areas. Memarin Sorkhabi and Djamour (2015) in northwestern Iran estimated geodetic velocity (GV) with an ANN reaching root mean square error (RMSE) less than 3.5 mm. In 2021, Konakoglu estimated the GV with the multi-layer perceptron neural network method, which was more accurate than classical mathematical methods. MariaMrówczyńska et al. (2020) used ANN and principal component analysis (PCA) to estimate GV, which performs both compression and reconstruction methods with high accuracy. Back propagation artificial neural network with 42 GPS stations has an RMSE of less than 2 mm in determining the GV of northwestern Iran (Sorkhabi, Alizadeh, et al., 2022).Various deep learning methods have been developed that can be used according to the type of problem. The comparison of this research with the pr...

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Section: Methodsmentioning

confidence: 99%

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

Sorkhabi

Milani

Alizadeh

2022

Earth and Space Science

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“…These representations can then be expanded to the input dimensions, retaining only selected characteristics of the original image. In the field of InSAR processing, CNNs have previously been used to filter wrapped interferograms [44]- [46], to unwrap interferograms [47]- [49], and to detect deformation related to ground subsidence [50], [51], volcanic activity [52], [53], mining [47], [54], landslides [55], and earthquakes [56]. Chen et al [57] implemented a multi-layer perceptron neural network to remove elevation-dependent atmospheric noise from interferograms.…”

Section: B Atmospheric Noise Correction With Machine Learningmentioning

confidence: 99%

Removing Atmospheric Noise from InSAR Interferograms in Mountainous Regions with a Convolutional Neural Network

Brencher¹,

Henderson²,

Shean³

2023

Preprint

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<p>Atmospheric errors in interferometric synthetic aperture radar (InSAR)-derived estimates of surface deformation often obscure real displacement signals, especially in mountainous regions. As climate change disproportionately impacts the mountain cryosphere, developing a technique for atmospheric correction that performs well in high-relief terrain is increasingly important. Here, we developed and implemented a statistical machine learning-based atmospheric correction that relies on the differing spatial and topographic characteristics of periglacial features and atmospheric noise. Our correction is applied at the native spatial and temporal resolution of the InSAR data (40 m, 12 days), does not require external atmospheric data, and can correct both stratified and turbulent atmospheric noise. Using Sentinel-1 data from 2015-2022, we trained a convolutional neural network (CNN) on atmospheric noise from 136 short-baseline interferograms and displacement signals from time-series inversion of 337 interferograms. The CNN correction was then tested on a densely connected network of 202 Sentinel-1 interferograms which were inverted to create a displacement time series. We used the Rocky Mountains in Colorado as our training, validation, and testing areas. When applied to our validation data, our correction offers a 690% improvement in performance over a global meteorological reanalysis-based correction and a 209% improvement over a high-pass filter correction. We found that our correction reveals previously hidden time-dependent kinematic behavior of three representative rock glaciers in our testing dataset. Our flexible, robust approach can be used to correct arbitrary InSAR data to analyze subtle surface deformation signals for a range of science and engineering applications. </p>

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“…According to experts, the biggest deficit of water reservoirs and land subsidence in Isfahan province is along the Zainderoud river and Baghat, and with the continuation of this trend, the subsidence will cover all of Isfahan province in the next 15 years (Fatolahzadeh et al, 2022). Based on the results of the studies of the Geological and Mineral Exploration Organization of the country, the Isfahan plain, especially the areas fed by the Zayandeh River, the northern areas of Isfahan city and Rahnan region, Shahid Beheshti Airport, Kohpayeh, as well as the cities of Doman, Ardestan, Kashan, Varzaneh, Barkhar, Khorasgan, Shahreza, Sejzai and Dasht Mehyar are very affected by the subsidence phenomenon (Sorkhabi et al, 2022). Also, based on these studies, the eight open areas that have a better situation in terms of aquifer level include Anark, Nain, Khor, Jandaq, Bayazeh, Bakhtiari, Gavkhoni and Chupanan areas, but the 10 critical prohibited areas are the plains of North Mahyar, South Mahyar, Kashan, Golpayegan, It includes Isfahan-Barkhar, Ardestan, Bad-Khalidabad, Doman, Ant-Khort and Najafabad, all of which are involved in land subsidence (Rafiee et al, 2022).…”

Section: Land Subsidence In Esfahanmentioning

confidence: 99%

Review on current status and challenging issues of land subsidence in Iran

Sadjadi¹

2022

10.5267/j.jfs

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Land subsidence in Iran is one of the environmental issues of recent years in Iran, which is mostly due to shortage of the groundwater resources. Iran is considered as the country with the most land subsidence. The amount of land subsidence in Iran is believed to be far more than the average of developed countries. Presently, over 300 plains of Iran are suffering from land subsidence and in some plains of Iran, the conditions have passed the subsidence and the ground has entered the critical stage of creating sinkholes. This paper presents an overview of the current status and challenging issues of land subsidence in Iran.

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Evaluation of Isfahan City Subsidence Rate Using InSAR and Artificial Intelligence

Cited by 19 publications

References 23 publications

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

Investigating the Efficiency of Deep Learning Methods in Estimating GPS Geodetic Velocity

Removing Atmospheric Noise from InSAR Interferograms in Mountainous Regions with a Convolutional Neural Network

Review on current status and challenging issues of land subsidence in Iran

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