pure and applied geophysics最新文献

{"title":"Gravity Change and Its Relation to Land Subsidence and Underground Water Table Variation at Kerman, Iran","authors":"Hamideh Cheraghi,&nbsp;Jacques Hinderer,&nbsp;Shahab Ebrahimi,&nbsp;Zahra Mousavi,&nbsp;Seyed Abdoreza Saadat,&nbsp;Siavash Arabi,&nbsp;Morteza Sedighi","doi":"10.1007/s00024-024-03605-x","DOIUrl":"10.1007/s00024-024-03605-x","url":null,"abstract":"<div><p>The gravity value at the surface of the Earth can be changed due to land subsidence and underground water depletion. Absolute gravity measurements show a gravity increase of ~ 169 µgal at Kerman station in southeastern Iran during 2004–2017. InSAR vertical map (2017–2019) reveals displacement rates of -3.5 cm/year at the Kerman site and a maximum of -25 cm/year at the center of the plain. Kerman GPS measurements (2011–2018) indicate -4.3 cm/year of vertical displacement rate. The geometrical contribution of the subsidence to the gravity variation at this site is + 140.2 and + 172.2 μgal using InSAR and GPS, respectively. In situ measurements of the groundwater table show a 17 cm/year depletion rate, leading to minimum and maximum values of − 27.8 and − 46.4 µgal in the induced gravity change assuming a 30–50% porosity range. The sum of induced hydrological and geometrical gravity changes is found to be smaller than the observed gravity variation at Kerman station, underlying a variable subsidence rate in time. The decrease in subsidence rate, observed at some urban leveling benchmarks, is probably due to the westward development of Kerman city, the lack of a proper sewage system, as well as the decrease in water extraction because of land use change. Assuming that the subsidence rate was larger at the beginning of the absolute gravity measurement period and decreases with time, most of the gravity increase at the Kerman station can be explained by subsidence with only a small water mass change contribution.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"181 12","pages":"3443 - 3461"},"PeriodicalIF":1.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrological Loading in GNSS Vertical Coordinate Time Series on the Island of Haiti","authors":"Renaldo Sauveur,&nbsp;Sajad Tabibi,&nbsp;Olivier Francis","doi":"10.1007/s00024-024-03606-w","DOIUrl":"10.1007/s00024-024-03606-w","url":null,"abstract":"<div><p>Nowadays, studies investigating variations in Total Water Storage (TWS) have gained significant recognition through the analysis of the Global Navigation Satellite System (GNSS) vertical coordinate time series. This study focuses on the island of Haiti. The vertical loading displacements caused by TWS are calculated using the Global Land Data Assimilation (GLDAS) hydrological data model. A comparison between the annual signals of the hydrological model and GNSS data from 32 stations reveals the presence of the TWS signal in the GNSS time series. Despite the island of Haiti exhibiting a relatively small GNSS signal, the correction for hydrological effects computed with GLDAS results in a reduction of the Root-Mean-Square (RMS) scatter of the GNSS time series vertical displacement for 62.5% of the stations. The station with the most notable improvement shows a significant 50% reduction in RMS, along with a correlation coefficient of 0.88 between the GNSS and hydrological displacements.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"181 12","pages":"3591 - 3604"},"PeriodicalIF":1.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying Data-Driven Modeling for Streamflow Prediction in Semi-Arid Watersheds: A Comparative Evaluation of Machine Learning and Deep Learning Methodologies","authors":"Metin Sarıgöl,&nbsp;Okan Mert Katipoğlu,&nbsp;Hüseyin Yildirim Dalkilic","doi":"10.1007/s00024-024-03607-9","DOIUrl":"10.1007/s00024-024-03607-9","url":null,"abstract":"<div><p>Modeling monthly stream flows most accurately is of vital importance for water resource management, agricultural irrigation and efficient hydroelectric energy production, especially in semi-arid areas. Soft computing approaches have recently taken an important place in estimating streamflow time series. The potential of various data-driven approaches to predict streamflow in challenging climate conditions was evaluated. The study used machine and deep learning algorithms to model average monthly stream flows in two stream gauging stations in semi-arid region of the Konya closed basin where agriculture is at the forefront, accurate and reliable estimation of the stream flows is the basis of the study. For this, the performances of emotional neural network algorithm (EmNN), long-short term memory (LSTM), Elman neural network (ENN), nonlinear autoregressive exogenous model (NARX), recurrent neural network (RNN), group method of data handling (GMDH) were compared. The study’s unique contribution lies in its comprehensive comparison of these diverse algorithms, including newer approaches like EmNN, in the specific context of semi-arid hydrology. Partial autocorrelation analysis was applied to select input combinations, and lagged values exceeding 95% confidence limits were presented to the models as the most essential features. Artificial intelligence (AI) models use lagged stream flows to predict the streamflow time series. Statistical parameters, scatter diagrams and a time series approach are used to compare model performance. The GMDH model produced the following test results for 1604 no station: KGE: 0.656, R²: 0.608, NSE: 0.343, RMSE: 27.021, MAE: 3.834, MAPE: 0.662, MBE: −0.217, BF: 0.972. Similarly, for 1623 no station, the GMDH model yielded the following test results: KGE: 0.770, R²: 0.615, NSE: 0.531, RMSE: 0.006, MAE: 0.047, MAPE: 0.217, MBE: −0.012, BF: 0.956. In addition, the EmNN algorithm was the approach with second prediction accuracy. The findings of the study are important resources for optimizing the selection of AI models for streamflow prediction in semi-regional areas. The study also provides critical information for policymakers and decision-makers in similar climate zones worldwide for water resource management.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"181 12","pages":"3561 - 3589"},"PeriodicalIF":1.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of Excavated Radionuclide Retention Ponds in a Uranium Mine in the Process of Decommissioning Using Geophysical Methods","authors":"Leonides Guireli Netto,&nbsp;César Augusto Moreira,&nbsp;Henrique Marquiori Bianchi,&nbsp;Otávio Coaracy Brasil Gandolfo,&nbsp;Lenon Melo Ilha","doi":"10.1007/s00024-024-03602-0","DOIUrl":"10.1007/s00024-024-03602-0","url":null,"abstract":"<div><p>The challenges inherent in mining environmental liabilities, especially in radioactive mineral contexts, highlight the crucial importance of rehabilitating and properly managing degraded areas and tailings. In radioactive minerals mining, the challenges are accentuated due to the complexity of the materials and the environmental risks associated with persistent radioactivity. This scenario underlines the critical need for precise environmental management strategies, highlighting the importance of geophysical techniques for monitoring and mitigating environmental risks in radionuclide retention ponds. Geophysical techniques, such as electrical tomography and seismic tomography refraction, are interesting tools for identifying anomalies in the subsoil, such as leaks, fractures and contamination zones, which are not visible on the surface. These methods provide a non-invasive means of continuously monitoring the integrity of tailings storage facilities, allowing for early detection of potential failures or contamination pathways. By offering a more spatial understanding of subsurface conditions compared to traditional geotechnical instrumentation, geophysics plays an important role in mitigating environmental impacts, reducing risks to nearby ecosystems and informing rehabilitation efforts in radioactive mineral mining areas. This study applied electrical and seismic methods to assess two retention ponds at a uranium mine, demonstrating how these techniques can help in the safe decommissioning of mining facilities and the sustainable management of environmental liabilities. With a focus on two retention ponds of a uranium mine in South America in the process of decommissioning, the results revealed conductive electrical anomalies and variations in the geological layers identified by electrical tomography and refraction seismic, respectively, indicating potentially contaminated areas and alterations in the degree of fracturing of the foundation rock of the ponds. Comparing these results with a structural survey of fracture orientations in the study area demonstrates the preferential path of underground flow, conditioned by the fracturing pattern of the weathered rocks. These findings emphasize the importance of geophysics in the decommissioning phase of nuclear facilities, not only to monitor stored environmental liabilities, but also to assist in the recovery of degraded environments in the proximity of the mines.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"181 11","pages":"3313 - 3330"},"PeriodicalIF":1.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wind and Wave Hindcast and Observations During the Black Sea Storms in November 2023","authors":"Maria V. Yurovskaya,&nbsp;Mikhail V. Shokurov,&nbsp;Vladislav S. Barabanov,&nbsp;Yury Yu. Yurovsky,&nbsp;Vladimir N. Kudryavtsev,&nbsp;Oleg T. Kamenev","doi":"10.1007/s00024-024-03592-z","DOIUrl":"10.1007/s00024-024-03592-z","url":null,"abstract":"<div><p>The Black Sea coasts from the northwest of Turkey through Crimea to Georgia were strongly affected by severe storms in Autumn, 2023. The aim of the work is to compare the performance of different wave model approaches and wind datasets in extreme weather conditions in the Black Sea. The study covers the continuous period from the 1st to the 30th of November including two strong storms with wave heights up to 9–10 m. Wave simulations are performed using WAM and the 2D parametric model for surface wave development suggested in Kudryavtsev et al. (2021a). The wave models are forced by hourly wind fields from four datasets: ECMWF Reanalysis (ERA5), ECMWF Level-4 bias-corrected operational model, NCEP (CFSv2), and the regional WRF-ARW model with 6-hour NCEP/NCAR atmospheric forecast as input. The high-resolution Level-4 wave analysis for the Black Sea produced by CMEMS (also using WAM Cycle 6) is also considered. Simulation results are validated against along-track altimeter measurements of significant wave height, CFOSAT SWIM information on dominant wavelength and wave direction, and in-situ data from an oceanographic platform near Crimea. All models demonstrate their overall good performance, though third-generation wave spectral models give an expectedly higher correlation between simulations and observed data, while the parametric model is less accurate. Some recommendations to combine wind and wave models for the most accurate predictions are further given. As known, the wind speed fields produced by ECMWF are underestimated at winds higher than 15–20 m/s. While the wind correction is crucial when using the parametric model, WAM better reproduces the observed extreme waves without it. As also obtained, WAM simulations forced by NCEP and WRF winds lead to an overestimation of the largest storm waves. Increased resolution of the wind fields does not lead to significant improvement in the quality of wave predictions, which can be explained by the wind accumulation effect during wave development.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"181 11","pages":"3149 - 3171"},"PeriodicalIF":1.9,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of India Meteorological Department: High Resolution Rapid Refresh (IMD-HRRR) Modeling System for Very Short Range Weather Forecasting","authors":"Akhil Srivastava,&nbsp;Prashant Kumar,&nbsp;Sambit Kumar Panda,&nbsp;Ananda Kr. Das,&nbsp;D. R. Pattanaik,&nbsp;M. Mohapatra","doi":"10.1007/s00024-024-03549-2","DOIUrl":"10.1007/s00024-024-03549-2","url":null,"abstract":"<div><p>The aim of this study is the operational dynamical nowcasting application over different parts of India from Indian Meteorological Department (IMD) using Doppler Weather Radar (DWR) network of multiple radars and numerical weather prediction (NWP) model. The High Resolution Rapid Refresh (HRRR) approach has been adopted to achieve this objective in which the DWR data are hourly assimilated at convective-scale in the Weather Research and Forecasting (WRF) model. The designated NWP setup implemented for very short-range to nowcasting of weather is defined as the <i>IMD-HRRR modelling system</i>. Various quality controls are employed before assimilating DWR data in the IMD-HRRR system. Three different domains are specified over India that cover the entire Indian landmass, and next 12-h predictions are provided from hourly cyclic assimilation experiments. The results of all domains suggested that the IMD-HRRR predictions are not degraded with forecast lengths (up to 12 h) when compared against observations e.g. Synop, Metar, Buoy, total precipitable water (TPW) from GPS stations. Minimum errors are achieved when model predictions are compared against Buoy and TPW observations. The correlation values are higher than 0.9 for all domains. Furthermore, the IMD-HRRR model forecasts are also compared with observed DWR radial winds to demonstrate auxiliary applications of the DWR data for model verifications at high spatio-temporal resolution.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"181 11","pages":"3393 - 3408"},"PeriodicalIF":1.9,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Characterization of CTBT-Relevant Radioxenon Detections at IMS Stations Using Isotopic Activity Ratio Analysis","authors":"Boxue Liu,&nbsp;Martin Kalinowski,&nbsp;Yunwei Sun,&nbsp;Charles R. Carrigan,&nbsp;Christos Saragiotis,&nbsp;Jun Wang,&nbsp;Martin Ertl,&nbsp;Yuichi Kijima,&nbsp;Robin Schoemaker,&nbsp;Jolanta Kuśmierczyk-Michulec,&nbsp;Anne Tipka,&nbsp;Tarabay Antoun","doi":"10.1007/s00024-024-03437-9","DOIUrl":"10.1007/s00024-024-03437-9","url":null,"abstract":"","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"181 11","pages":"3431 - 3431"},"PeriodicalIF":1.9,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-024-03437-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Prediction Model of Seismic Variation in Complex Terrain based on the BP Neural Network with Cavities","authors":"Yanan Li,&nbsp;Hong Zhou","doi":"10.1007/s00024-024-03589-8","DOIUrl":"10.1007/s00024-024-03589-8","url":null,"abstract":"<div><p>Surface irregularities and subsurface cavities have a significant impact on seismic wave propagation, leading to either amplification or reduction of ground motion. This study focuses on creating a ground motion prediction model using artificial neural network techniques driven by a synthetically generated database. In this study, we focus on the Erlang Mountain region in Sichuan Province, China, to simulate surface ground motion using the spectral element method, considering the presence of underground cavities in the research area. The classical back propagation neural network model is used to predict changes in ground motion. The model is designed to forecast the PGA influence coefficient, and 5% damped PSV amplification ratio (for periods ranging from 0.33 to 10 s). Input parameters include the buried depth of the cavity, the distance between the surface and the cavity in the mountain projection, elevation, the first gradient of the elevation, and the second-order gradient in two orthogonal directions. The model’s performance falls within acceptable error limits. Additionally, the significance of input features is analyzed, and the model’s applicability in other regions of the ErLang Mountain is validated.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"181 10","pages":"3133 - 3147"},"PeriodicalIF":1.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrogeological Responses to the 2022 Tonga Volcanic Eruption in an Aseismic Region","authors":"Ella Gorbunova,&nbsp;Alina Besedina,&nbsp;Sofia Petukhova,&nbsp;Dmitry Pavlov","doi":"10.1007/s00024-024-03590-1","DOIUrl":"10.1007/s00024-024-03590-1","url":null,"abstract":"<div><p>Complex measurements being performed with a network of stations at different epicenter distances allowed to register numerous effects in various geophysical fields at the moment of peak activity of the Tonga Volcano 15/01/2022. Monitoring was also performed at the \"Mikhnevo\" Geophysical Observatory of Sadovsky Institute of Geospheres Dynamics of Russian Academy of Sciences. The aim was to detect the links between disturbances in the atmosphere, changes in ambient seismic noise, and variations in underground water level. For the first time in an aseismic region the reaction of unevenly aged aquifers to a global disturbance in the form of propagating Lamb atmospheric waves (direct and antipodal ones) as they passed around the Earth have been registered. The responses recorded in different geophysical fields were compared to background values of probability density function of power spectral density. A difference in the intensities of the responses of the confined and weakly confined aquifers to two passages of atmospheric fronts from the Tonga volcanic eruption was established. This difference corresponds to the values of barometric efficiency. Delays of the hydrogeological responses with respect to the passage of Lamb waves were observed. Synchronous measurements of hydrogeological responses to passing atmospheric disturbances produced by the Tonga volcanic eruption performed in an aseismic region add to the world database of interacting geospheres and require further consideration involving the analysis of available experimental data.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"181 10","pages":"3005 - 3018"},"PeriodicalIF":1.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Spatial and Temporal Characteristics of Meteorological Drought in Afghanistan","authors":"Gokmen Tayfur,&nbsp;Ehsanullah Hayat,&nbsp;Mir Jafar Sadegh Safari","doi":"10.1007/s00024-024-03578-x","DOIUrl":"10.1007/s00024-024-03578-x","url":null,"abstract":"<div><p>Afghanistan is suffering from periodic events of drought, which has exacerbated in recent years due to extreme climate events in the region. Having an arid to semi-arid climate, the country faces significant challenges of water resources management, especially for irrigation as reliance on agriculture is cumbersome. This study is undertaken to characterize historical meteorological drought in Afghanistan to provide an insight on where and when meteorological drought events happened in different River Basins (RBs). The study mainly employs the gamma-Standardized Precipitation Index (gamma-SPI) to analyze historical meteorological droughts across Afghanistan from 1979 to 2019. Monthly precipitation data is obtained from the Ministry of Energy and Water (MEW) of Afghanistan, which is a combination of observed data from ground stations and gap-filled data by the MEW for the study period. Gridded gamma-SPI values are interpolated and mapped to visualize patterns of spatial drought across the entire country. The results indicate that countrywide extreme drought events occurred in 1999, 2000, 2001, 2010, 2016, 2017, and 2019, particularly affecting southern, western, and southwestern regions. Decreasing rainfall occurred in all five RBs, with the most considerable decline observed in the 1999–2008 period. The study reveals the increasing frequency and severity of meteorological droughts in Afghanistan. It also emphasizes on the vulnerability of agriculture and water sectors due to the drought events. The findings of the study suggest the need for better drought monitoring, preparedness, awareness, and adaptation of strategies to ensure water security and agricultural sustainability in the face of climate change.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"182 2","pages":"729 - 751"},"PeriodicalIF":1.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143638211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}