pure and applied geophysics最新文献

{"title":"Amplification Effects of Mountainous Terrain on PM2.5 Pollution in Winter in Anhui Province, China","authors":"Chune Shi,&nbsp;Liying Zhao,&nbsp;Guanying Yang,&nbsp;Xiaoyun Sun,&nbsp;Zhuang Wang,&nbsp;Hao Zhang,&nbsp;Yanyu Lu,&nbsp;Jin Zhai,&nbsp;Qian Zhao,&nbsp;Hongqun Zhang,&nbsp;Qizhong Wu","doi":"10.1007/s00024-026-03977-2","DOIUrl":"10.1007/s00024-026-03977-2","url":null,"abstract":"<div><p>Although the Chinese government's implementation of strict emission reduction policies has yielded remarkable results, Anhui Province in east-central China continues to experience heavy PM_2.5 pollution (HPP) every winter. To investigate the causes of HPP in Anhui, multisource data are comprehensively analysed and numerical modelling is performed to reveal the spatiotemporal distribution and formation mechanisms of HPP days, with a particular focus on clarifying the role of mountainous terrain. With January 2019 as a case study, we assess and quantify the contributions of Mount Dabie (Mt. DB) in western Anhui and the mountains in southern Anhui (Mt. SA) to HPP events. The distribution characteristics of the HPP days and temporal evolution of HPP episodes in January 2019 reveal the strong influence of topography. Two high-risk areas are identified: the northwest area, which is primarily affected by regional pollutant transport and limited precipitation, and the southwest area, where the topography of Mt. DB enhances atmospheric convergence and reduces the number of precipitation hours, with an additional influence from regional transport. On HPP days, Mt. SA is associated with an increased PM_2.5 concentration to the northeast by 8.5% but a decreased PM_2.5 concentration to the south. Mt. DB is associated with an increased PM_2.5 concentration to the northeast (part of the northwest high-risk area) by 11% and to the southeast (part of the southwest high-risk area) by 9.1%. Our findings highlight the synergetic effect of adverse meteorological conditions and mountainous terrain, providing a critical reference for air pollution forecasting in east-central China.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 6","pages":"3123 - 3141"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394966","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 the One-to-One-Based Optimizer (OOBO) Algorithm for One-Dimensional Inversion Modeling of Magnetotellurics Data","authors":"Wahyu Eko Junian,&nbsp;Yudha Styawan,&nbsp;Nugroho Prasetyo,&nbsp;Andri Yadi Paembonan","doi":"10.1007/s00024-026-03998-x","DOIUrl":"10.1007/s00024-026-03998-x","url":null,"abstract":"<div><p>Magnetotellurics (MT) is a geophysical method used to study subsurface structures based on resistivity properties. Inversion modeling in MT is crucial to reconstructing subsurface resistivity distributions from observed data. However, traditional inversion methods often face challenges such as computational complexity, local minimum traps, and dependence on initial models. This study proposes a novel one-dimensional (1-D) MT inversion approach using a One-to-One-Based Optimizer (OOBO) algorithm. OOBO efficiently explores the solution space using information from all population members, reducing the risk of local minima and improving accuracy. The proposed method was tested on synthetic MT data with added noise 5% using a four-layer resistivity model (Model 1) and a five-layer resistivity model (Model 2). The results showed the best resistivity reconstructions with a percentage root mean square error (RMSE) value of 5.98% for Model 1 and 6.40% for Model 2, demonstrating superior accuracy compared to traditional methods such as Occam’s inversion. The algorithm was also applied to field data, demonstrating its ability to efficiently explore model spaces, even when the number of layers is unknown. OOBO’s stochastic nature allows for transparent evaluation of model uncertainty. Comparison with the probabilistic rj-McMCMT approach provided a robust cross-validation benchmark. Despite differing mathematical frameworks, both methods produced highly consistent resistivity structures. This result indicates that OOBO enhances the reliability and efficiency of MT inversion. Furthermore, OOBO demonstrates potential as a competitive approach for evaluating model uncertainty in non-linear electromagnetic problems.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 6","pages":"2873 - 2890"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394963","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":"Active Faulting and Its Relationship with Regional Hydrogeological Regimes: A Case Study in the Villa de Reyes Graben, Central México","authors":"Fabián Esteban Monge-Cerda,&nbsp;José Alfredo Ramos-Leal,&nbsp;Omar Delgado-Rodríguez,&nbsp;José Ramón Torres-Hernández,&nbsp;Mayla Alheli Ramos-Vázquez","doi":"10.1007/s00024-026-03984-3","DOIUrl":"10.1007/s00024-026-03984-3","url":null,"abstract":"<div><p>The Villa de Reyes Graben is a tectonic basin where structural studies have focused mainly on normal faulting, but little attention has been given to the active strike-slip faults in its interior. Moreover, the application of geophysical and hydrogeological methods is usually made separately, and research regarding the link between active faults and contaminant pathways to aquifers has yet to be made in this graben. This study integrates aeromagnetic, geoelectric, and hydrogeochemical data, to assess the likelihood that active faults act as contaminant pathways, and how these active faults influence the regional hydrogeological regime. Aeromagnetic data shows similar trending anomalies compared to mapped faults and flower structures that probably influence the area. The western sector of the study area shows the lowest magnetic anomaly values, which agrees with the cluster of recent seismic activity epicenters. Geoelectric data show how water infiltration takes place through shallow non-filled fractures, which may alter the water table at greater depths. Hydrogeochemical data reveal that meteoric water influence tends to increase near the more tectonically active western sector and decrease in the less active eastern sector. Geothermometer analysis reveals that geothermal water influence is non-existent. Finally, faulting analysis suggests that surface faulting appears to merge at depth, which could indicate that the tectonics of the area are controlled by flower structures. This study reveals zones that could act as either barriers or pathways for contaminants, which could have implications for land use planning and in the assessment of water scarcity challenges in the area.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 6","pages":"2911 - 2934"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394906","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":"A Statistical Study on the Seismic Activity Characteristics of the Xiluodu Reservoir in the Lower Jinsha River","authors":"Junchao Wang,&nbsp;Longfei Duan,&nbsp;Cuiping Zhao","doi":"10.1007/s00024-026-03970-9","DOIUrl":"10.1007/s00024-026-03970-9","url":null,"abstract":"<div><p>The Xiluodu Reservoir, situated on the lower Jinsha River, is fourth-largest hydropower in the world. The dam lies in the central part of the Leibo–Yongshan block, bounded by the Ebian–Jinyang, Lianfeng, and Mabian–Yanjin faults. Following the impoundment of the Xiluodu Reservoir in May 2013, seismic activity in the surrounding area increased markedly, and the Yongshan <i>M</i>_S5.1 and <i>M</i>_S5.2 earthquakes occurred on April 5 and August 17, 2014, respectively. In this study, we collected a high-resolution seismic catalog for the Xiluodu Reservoir region from 2010 to 2019. Using statistical parameters including the coefficient of variation (<i>C</i>_V), <i>b</i>-value, Epidemic-Type Aftershock Sequence (ETAS) model, and nearest-neighbor distance, we analyzed the spatiotemporal characteristics of post-impoundment seismicity and explored its classification. The rapid rise in the<i> b</i>-value during the early filling stage suggests that reservoir loading disturbed the local stress field, triggering a surge in microseismic events. The high consistency between the ETAS model and NND results demonstrates that reservoir-induced earthquakes in the Xiluodu Reservoir area predominantly follow the background mode and exhibit an almost unipolar distribution in spatiotemporal density plots. This phenomenon reflects the distinctive nature of the driving mechanisms underlying seismic activity in the reservoir area. The area near the dam shows a very high seismic attenuation rate and a denser background pattern, while a small proportion of earthquake events in the <i>M</i>_S5.1 and <i>M</i>_S5.2 source regions show a clustered distribution, indicating the existence of a seismic triggering mechanism here.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 6","pages":"2717 - 2736"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-026-03970-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394920","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":"Effects of Green Space Dynamics on Flood Susceptibility in Thanh Hoa Province, Vietnam","authors":"Huu Duy Nguyen,&nbsp;Thi Sen Tran,&nbsp;Van Hong Nguyen,&nbsp;Quang-Thanh Bui","doi":"10.1007/s00024-026-03995-0","DOIUrl":"10.1007/s00024-026-03995-0","url":null,"abstract":"<div><p>Land use/land cover change (LULCC) in general and green space change in particular play an important role in flood control. Therefore, understanding the effects of LULCC and green space change is considered essential for sustainable land use planning, especially in the context of climate change and urban growth. The objective of this study is to evaluate the effects of green space dynamics on flood susceptibility using machine learning and remote sensing, namely the multilayer perceptron (MLP) and the recurrent neural network (RNN) in Vietnam’s Thanh Hoa Province. The novelty of this study lies in the integration of multi-temporal remote sensing data (2017 and 2024) with deep learning to evaluate the effects of green space dynamics on flood susceptibility, a problem that has not been sufficiently addressed in previous studies, which have mainly focused on single-period analysis. A total, 1164 flood inventory points and 14 conditioning factors in 2017 and 2024 were used as input data for the machine-learning model. Various statistical indices—namely root mean square error (RMSE), mean absolute error (MAE), area under the curve (AUC), and coefficient of determination (R²)—were applied to evaluate the computational performance of the MLP and the RNN. The results showed that the MLP model performed better than the RNN model with an R² value of 0.812. The results also highlighted that the green space area decreased from about 71% to 64% from 2017 to 2024. Although the area of very high flood susceptibility area showed a decreasing trend, decreasing from 3042.192 km² in 2017 to 1713.748 km² in 2024, high flood susceptibility areas increased from 1,285 km² in 2017 to 1,971 km² in 2024. Moderate flood susceptibility areas also increased from 1,204 km² in 2017 to 1382 km² in 2024. The results indicate that about 100.91 km² transitioned from low to high flood susceptibility. At the same time, the loss of forest-mainly converted to agricultural land (7756 km²) and barren land (1592 km²)—suggests a decline in green space quality, which reduces infiltration and increases flood. The methodological framework in this study can support the identification of priority urban areas and, thus, provide information to make decision-making on green strategies more effective. The results of this study serve as a guide for supporting decision-makers in sustainable urban development.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 6","pages":"3075 - 3105"},"PeriodicalIF":2.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394865","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":"Method for Restoring the Orientation of Ocean Bottom Seismometers Using Distant Earthquakes Records","authors":"Oleg V. Ponomarev,&nbsp;Sergey V. Kolesov,&nbsp;Mikhail A. Nosov","doi":"10.1007/s00024-026-03975-4","DOIUrl":"10.1007/s00024-026-03975-4","url":null,"abstract":"<div><p>A method is presented for determining the vertical direction relative to the axes of seismometers installed in seafloor observatories. The method is based on the linear relationship between the vertical component of seafloor acceleration and pressure variations at the ocean bottom, which follows directly from Newton’s second law and holds within the frequency range of forced oscillations. The method’s performance was validated using data from ocean bottom seismometers (accelerometers) and pressure gauges of six S-net stations during three seismic events: Gulf of Alaska <b>M</b>_<b>w</b><b> 7.9</b> (23.01.2018), Chignik <b>M</b>_<b>w</b><b> 8.2</b> (29.07.2021) and Noto <b>M</b>_<b>w</b><b> 7.5</b> (01.01.2024). The results were compared with an independent alternative method, showing discrepancies within 1° for most stations. Compared with gravity-based approaches, the proposed cross-spectral method works for both accelerometers and velocimeters, enabling routine orientation checks for a wider range of OBS networks.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 7","pages":"3371 - 3382"},"PeriodicalIF":2.1,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614165","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":"Temporal Trends and Drought Risk Evaluation in the Semi-arid Kilis Region of Türkiye","authors":"Türkan Zengin Gömleksiz,&nbsp;Emrah Tuncay Özdemir","doi":"10.1007/s00024-026-03976-3","DOIUrl":"10.1007/s00024-026-03976-3","url":null,"abstract":"<div><p>Drought is an increasing challenge in semi-arid border regions where climatic stress intersects with demographic pressure. Kilis Province, located along the southern border of Türkiye, is particularly vulnerable because declining water availability coincides with rising demand associated with migration-driven population growth. This study presents a 67-year drought assessment for Kilis using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-, 6-, and 12-month timescales, together with a Landsat-based monthly Normalized Difference Vegetation Index (NDVI) time series for 2000–2025 as a complementary indicator of vegetation response. Drought characteristics were compared between two consecutive sub-periods, 1959–1991 and 1992–2025, and long-term climatic trends were evaluated using the Mann–Kendall test. The results showed a significant decrease in annual precipitation and a significant increase in annual mean temperature. Both SPI and SPEI exhibited statistically significant negative trends across all timescales, with the strongest drying signal at the 12-month scale. The inter-period comparison further indicated intensification of long-term drought conditions during the recent period. Correlations between NDVI anomalies and drought indices were weak and statistically non-significant, suggesting that NDVI should be interpreted as a complementary vegetation-response indicator rather than a direct validation tool for meteorological drought indices. Overall, the findings reveal progressive drought intensification in Kilis and provide evidence relevant to drought preparedness and water-resource planning.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 6","pages":"2957 - 2976"},"PeriodicalIF":2.1,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394776","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":"Relation Between Air Quality and Wind Speed in the Eastern Anatolia Region of Türkiye","authors":"Mehmet Ali Çelik,&nbsp;Adile Bilik,&nbsp;Muhammed Ernur Akiner","doi":"10.1007/s00024-026-03994-1","DOIUrl":"10.1007/s00024-026-03994-1","url":null,"abstract":"<div><p>This study investigates the relationship between wind speed and atmospheric constituents (nitrogen dioxide (NO₂), ozone (O₃), carbon dioxide (CO₂), and aerosol index (AI)) in the Eastern Anatolia Region of Türkiye. The analysis focuses on how topographically constrained basins and low wind speeds influence the accumulation of atmospheric constituents. Pixel-based seasonal analyses were conducted using TROPOMI satellite data (2019–2024) and TerraClimate wind speed data. The methodological framework integrates Pearson correlation, time-lag analysis, and K-means clustering to quantify both instantaneous and delayed relationships between wind speed and atmospheric variables. Results indicate that NO₂ and CO₂ concentrations increased by 40–60% based on normalized concentration differences between wind speed classes (&lt; 4 m/s vs. &gt; 4 m/s), particularly in semi-closed basins such as Elazığ, Iğdır, and Malatya. Time-lag analysis reveals that aerosol transport exhibits approximately 1-month delays, reflecting the influence of boundary-layer stability and weak convective processes. In contrast, regions with higher elevations and open topography (e.g., Erzurum and Kars) show significant decreases in atmospheric constituent concentrations under higher wind conditions. O₃ variability is primarily controlled by transport-driven photochemical processes involving NOx and volatile organic compounds (VOCs). The findings highlight that atmospheric accumulation patterns are governed not only by wind speed but also by topography and atmospheric dynamics. This study provides a regional-scale framework for understanding delayed responses to pollutants. It emphasizes the importance of integrating meteorological thresholds into air quality management and early warning systems under changing climate conditions.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 6","pages":"3159 - 3184"},"PeriodicalIF":2.1,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-026-03994-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394935","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":"Deep Learning Prediction Model for Ground Motion Amplification Effect of Sedimentary Valleys with Varying Shear Wave Velocities","authors":"Jia-wei Zhao,&nbsp;Si-bo Meng,&nbsp;Zhong-xian Liu","doi":"10.1007/s00024-026-03979-0","DOIUrl":"10.1007/s00024-026-03979-0","url":null,"abstract":"<div><p>The site effects of sedimentary valleys caused by earthquakes were widely investigated using numerical methods, which had the challenge of the high computational costs. This study explores the feasibility of deep learning methods for obtaining the nonlinear seismic response of sedimentary valleys with varying shear wave velocities. The proposed deep learning model was constructed based on the Long Short-Term Memory (LSTM) network with the hybrid input features, including time series of input waves and shear wave velocities of valleys. The output features of this model were the time series of the seismic response at representative surface locations of sedimentary valleys, and the surface locations were determined via the Principal Component Analysis (PCA). Based on the model validation, the site effect and structural fragility solved by both the proposed LSTM model and traditional one-dimensional (1-D) soil analysis method were compared and discussed. The results indicate that the LSTM model exhibits great efficiency and precision (Coefficient of determination R² = 0.96) in assessing the site response and fragility analysis. Compared to LSTM models, the 1-D soil analysis method exhibits a underestimation of the structural fragility, with a maximum difference of approximately 50%.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 6","pages":"2849 - 2872"},"PeriodicalIF":2.1,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394936","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":"An Optimization-Focused Comparative Framework for Machine Learning-Based Suspended Sediment Load Modeling","authors":"Muhammed Ernur Akiner,&nbsp;Mehdi Ghasri","doi":"10.1007/s00024-026-03973-6","DOIUrl":"10.1007/s00024-026-03973-6","url":null,"abstract":"<div><p>Urbanization in the Göksu River Basin increases sediment movement toward the Göksu Delta. The research used hydrometeorological data to assess machine learning techniques, including hybrid and deep learning systems, for Suspended Sediment Load (SSL) forecasting. The development of conventional models, including Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Support Vector Machine (SVM), received optimization algorithm improvements through the implementation of Adaptive Moment Estimation (ADAM), Sequential Minimal Optimization (SMO), and the Water Cycle Algorithm (WCA), which boosted model accuracy and development speed. The BPNN_ADAM hybrid model performed better than all other methods because it achieved superior R², adjusted R², Nash–Sutcliffe Efficiency (NSE), RMSE-observations standard deviation ratio (RSR), and Normalized Root Mean Square Error (NRMSE) results, resulting in a low Mean Absolute Error (MAE) of 0.09. The BPNN_WCA hybrid system showed consistent performance across all tested metrics, which measured both error and operational efficiency. The deep learning models Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) achieved exceptional predictive performance, with R² values close to 0.99 in both univariate and multivariate testing modes. Deep learning models (LSTM/Bi-LSTM) have NRMSE values of 8–12%, followed by optimized hybrid models such as BPNN_ADAM and BPNN_WCA, with NRMSE values of 35–50%. In contrast, standalone machine learning models, such as SVM, show significantly higher errors with NRMSE values exceeding 60–90%. The research indicated that time-lagged hydrological and sediment variables play a crucial role in improving SSL prediction accuracy while also capturing the memory of catchment systems.</p></div>","PeriodicalId":21078,"journal":{"name":"pure and applied geophysics","volume":"183 6","pages":"2763 - 2784"},"PeriodicalIF":2.1,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00024-026-03973-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394922","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}