Acta Geophysica最新文献

{"title":"Past and future changes in maximum air temperature and cold days in winter in Poland","authors":"Arkadiusz M. Tomczyk,&nbsp;Mikołaj Piniewski,&nbsp;Mohammad Reza Eini","doi":"10.1007/s11600-025-01538-0","DOIUrl":"10.1007/s11600-025-01538-0","url":null,"abstract":"<div><p>The aim of the study was to determine changes in maximum air temperature in winter and the occurrence of cold days in the period 1966/67–2023/24, as well as to prediction the direction and rate of changes in the near future and (2021–50) far future (2071–2100). In the study, cold days were defined as days with Tmax &lt; 0 °C. The research indicated an increase in average Tmax across all stations over the period. The increase in average Tmax was most intense in north-eastern Poland. The observed increase in average Tmax translated into a decrease in the number of cold days across all stations, with the declines being most significant in the north-east. The studies projected that the observed increase in Tmax will continue in the coming decades of the twenty-first century. The prediction warming is most intense for the eastern regions, in both the near and far future. It is also in these regions that the projected changes in numbers of cold days are most intense.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 4","pages":"3663 - 3675"},"PeriodicalIF":2.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142285","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":"Enhancing understanding of soil electrical behavior: multi-variable analysis and correlation modeling","authors":"Soumitra Kumar Kundu,&nbsp;Rajat Debnath,&nbsp;Ashim Kanti Dey","doi":"10.1007/s11600-025-01532-6","DOIUrl":"10.1007/s11600-025-01532-6","url":null,"abstract":"<div><p>Electrical resistivity (ER) approach leverages soil’s electrical behavior to detect its anomaly in a non-destructive (NDT) manner. This behavior is influenced by varying parameters which include soil’s resistivity, particle size gradation, physical parameters (water content, porosity and density), salinity and temperature which are amongst most crucial ones. Considering all above parameters, field ER tends to provide a continuous subsurface profile in a quick, effective and reliable manner resulting in increase of usage in the past decades. ER measurements are not only confined to in situ tests, but laboratory setup is also developed to estimate ER values. In this context, researchers in the past have adopted both field- and laboratory-based resistivity values to establish correlations with varying soil properties showcasing significant limitations in the form of site-specific nature with single input variable. In this context, the present study aims to evaluate effect of multiple parameters in the form of temperature, clay content, salinity, density, air content and water content on resistivity of soil. Results derived from the current analysis clearly showcases that effect of multiple parameters has a profound impact on soil’s resistivity compared to a single parameter. Further, correlationships were also developed involving simple and multiple regression analysis which resulted in formation of multi-variable model having coefficient of correlation (R²) value of 0.97. Furthermore, sensitivity of variables was also analyzed to ascertain effect of individual parameter on resistivity. Based on derived data, it could be inferred that density and temperature were found to be the most least sensitive input variables. Thus, results derived from the present study would play a pivotal role in accurate assessment of ER values.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2635 - 2656"},"PeriodicalIF":2.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879603","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":"Geophysical-guided Wasserstein cycle-consistent generative adversarial networks for seismic impedance inversion","authors":"Urip Nurwijayanto Prabowo,&nbsp;Sudarmaji Saroji,&nbsp;Sismanto Sismanto","doi":"10.1007/s11600-025-01536-2","DOIUrl":"10.1007/s11600-025-01536-2","url":null,"abstract":"<div><p>Deep learning has shown great ability to solve the nonlinear inversion problem in geophysical fields. Insufficient-labeled data and a lack of geophysical constraints become the main challenges in training the networks. In seismic impedance inversion, combined semisupervised learning and generative adversarial networks (GANs) named cycle-consistent GAN (cyc-GAN) are proven to achieve better inversion accuracy with insufficient labeled data. The next improvement of cyc-GAN is Geo-cyc-GAN, which imposes the convolutional model as a geophysical constraint. This improvement can speed up the training process and achieve better accuracy. However, like most GAN algorithms, the cyc-GAN and Geo-cyc-GAN suffer from training instability. Therefore, we proposed geophysical-guided Wasserstein cycle-consistent generative adversarial networks (Geo-cyc-WGANs) to overcome the training instability of GAN and increase its accuracy. Geo-cyc-WGAN uses Wasserstein distance as a loss function instead of cross-entropy to improve the training stability. The experiment results of synthetic data using small labeled traces show that Geo-cyc-WGAN achieves the highest accuracy, better lateral continuity, and a more stable training process than another geophysical guide-based method. The experiment results of real data also show that Geo-cyc-WGAN can obtain better accurate impedance results than other methods.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2621 - 2634"},"PeriodicalIF":2.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879568","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":"Estimation of return levels and associated uncertainties of extreme temperatures using a time-varying framework: a case study in Iran","authors":"Sedigheh Anvari,&nbsp;Jesper Rydén","doi":"10.1007/s11600-025-01544-2","DOIUrl":"10.1007/s11600-025-01544-2","url":null,"abstract":"<div><p>In recent decades, Iran has seen unprecedented extreme temperatures (ETs) in different climatic zones, resulting in significant shifts and inconsistencies in their distributions. So, estimating ETs and associated uncertainties within a non-stationary (NS) context becomes a crucial step in modeling of hydro-climatic events like floods, droughts etc. This study examines the time-varying evaluation of extreme hot and cold temperatures (EHTs and ECTs) at 12 weather stations in Kerman province, Iran. Moreover, two recently proposed methodologies are investigated: conditional and integrated (unconditional), for estimating return levels (RLs) and their corresponding confidence intervals (CIs) within a NS framework. Analyses were conducted using Generalized Extreme Value (GEV) distribution under two assumptions: stationary (S-GEV) and non-stationary (NS-GEV). The EHTs and ECTs time series from 1979 to 2019 underwent testing for trends, homogeneity, and stationarity. The maximum likelihood estimator (MLE) was adopted to estimate the distribution parameters. The NS impacts of EHTs and ECTs were quantified by calculating the difference between stationary and non-stationary RLs, denoted as SRL and NSRL, respectively. Analysis of trends and stationarity indicated that the EHTs and ECTs time series were non-stationary. The Akaike information criterion (AIC) favored the NS-GEV model over the S-GEV model. Our results demonstrated that NS-GEV frequency analyses have a growing impact on the RL for both EHTs and ECTs. One finding was that the visualization of conditional RL plots turned out to be a valuable approach to assess uncertainties in future scenarios; another that climatology (e.g. arid and excessive arid areas across Kerman) seems to influence shapes and features of RL in future outcomes. Our findings can significantly contribute to policy-making and strategic planning in water resource management, particularly in areas such as infrastructure development and risk assessment.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 4","pages":"3647 - 3662"},"PeriodicalIF":2.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142126","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":"The potential role of El Niño-Southern Oscillation in triggering Greenland glacial earthquakes","authors":"Bhaskar Kundu,&nbsp;Batakrushna Senapati,&nbsp;Nagaraju Chilukoti,&nbsp;Sambit Sahoo","doi":"10.1007/s11600-025-01541-5","DOIUrl":"10.1007/s11600-025-01541-5","url":null,"abstract":"<div><p>Glacial earthquakes, which are primarily linked to the iceberg calving process, occur more frequently in polar regions with large ice masses. These events are triggered by the sudden movement of glaciers or ice sheets and are highly sensitive to climatic forces. This study focuses on the seasonal and inter-annual patterns of glacial earthquakes from Greenland, investigating the role of the El Niño-Southern Oscillation (ENSO) teleconnection induced by the tropically excited Arctic warming mechanism (TEAM). Our analysis demonstrates that El Niño events elevate surface temperatures over Greenland in winter, driven by increased poleward energy transport in the extra-tropics. This enhanced energy transfer contributes to significant winter warming in the region, establishing a clear link between ENSO-driven climate variability and the frequency of glacial earthquakes.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2287 - 2297"},"PeriodicalIF":2.3,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879602","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":"Evaluating the effectiveness of ensemble machine learning approaches for pore pressure prediction using petrophysical log data in carbonate reservoir","authors":"Pydiraju Yalamanchi,&nbsp;Saurabh Datta Gupta,&nbsp;Rajeev Upadhyay","doi":"10.1007/s11600-025-01530-8","DOIUrl":"10.1007/s11600-025-01530-8","url":null,"abstract":"<div><p>Precise estimation of pore pressure (PP) holds significant importance in assessing the geomechanical parameters of reservoirs, playing a crucial role in the planning and execution of drilling and development activities in oil and gas fields. Recognizing its necessity various empirical and intelligent methods have been introduced to enhance the precision of PP prediction. The main objective of this study is to assess the effectiveness of ensemble machine learning (ML) models by conducting a comparative analysis of individual ML models for predicting PP. To identify the most influential input variables for constructing ML models, a feature selection analysis was performed. The findings suggest that a combination of 8-input variables holds the most influence on ML model construction. Three individual ML models namely least-square support vector machine, multi-layer perceptron artificial neural network and decision tree regression (DTR) were employed for PP prediction by using petrophysical log data (8 input variables). The dataset of wells A and B was for training, and testing these models. The results from individual models showed that the DTR algorithm provides the most accurate PP prediction, boasting an <span>({R}^{2})</span> value of 0.972 for training dataset, and an RMSE of 110.698 Psi. The performance of individual models can be enhanced using ensemble models, including simple averaging ensemble (SAE), weighted averaging ensemble (WAE), stacking ensemble (SE), random forest (RF). The results reveal that all ensemble models deliver more accurate PP predictions than individual models. Among them, the RF model stands out with an <span>({R}^{2})</span> of 0.999 for both training and testing datasets. It also demonstrates lower RMSE values of 8.948 Psi, and 21.568 Psi for training, and testing datasets, respectively, making it more accurate than SAE, WAE, SE and individual ML models. Furthermore, the generalization analysis demonstrates that the 8-input variable RF model exhibits excellent performance, providing more accurate PP predictions when applied to the well C dataset within the study area.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2591 - 2619"},"PeriodicalIF":2.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879598","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":"Edge recognition of magnetic anomaly source body based on convolutional neural networks in Red Sea Basin","authors":"Tao Cheng,&nbsp;Weixiang Tao,&nbsp;Xinyi Zhou,&nbsp;Xin Feng,&nbsp;Shuai Wang,&nbsp;Zhaoxi Chen","doi":"10.1007/s11600-025-01537-1","DOIUrl":"10.1007/s11600-025-01537-1","url":null,"abstract":"<div><p>The Red Sea Basin is one of the youngest marine basins, experiencing three stages of rift formation, early magmatic activity, and rift expansion. The fault system and uplift pattern are controversial research points. It can provide effective basis for delineating geological units and dividing fault structures by recognizing the edge information of field source bodies with magnetic anomaly data. However, traditional methods for identifying the boundaries of magnetic anomaly source bodies are affected by factors such as the depth of the source body, magnetization direction, and mutual interference between magnetic anomalies, which can lead to errors in subsequent interpretation work. The latest development of convolutional neural networks has strong feature representation and deep learning capabilities. This paper proposes an edge recognition method based on convolutional neural networks. Firstly, a network architecture for identifying the boundaries of magnetic anomaly sources was designed based on the U-Net network. Then, models with different parameters such as location, scale, quantity, and physical properties were selected to construct a large amount of high-quality sample data for training the network. Finally, a model experiment was designed, taking into account the effects of burial depth and tilted magnetization. The effectiveness of the proposed method was verified by comparing it with traditional edge recognition methods. Finally, based on the geological gravity data of the Red Sea and the Gulf of Aden, the division of the Red Sea and Gulf of Aden fault and uplift system was completed.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2581 - 2590"},"PeriodicalIF":2.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879597","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":"Flood vulnerability assessment of buildings using geospatial data and machine learning classifiers","authors":"Tze Huey Tam,&nbsp;Muhammad Zulkarnain Abd Rahman,&nbsp;Sobri Harun,&nbsp;Ismaila Usman Kaoje,&nbsp;Mohd Radhie Mohd Salleh,&nbsp;Mohd Asraff Asmadi","doi":"10.1007/s11600-024-01527-9","DOIUrl":"10.1007/s11600-024-01527-9","url":null,"abstract":"<div><p>Quantifying a building's vulnerability to flooding is crucial for implementing effective structural mitigation strategies. Detailed conventional onsite building damage assessment methods can be time-consuming and unsuitable for rapid assessments, with geospatial data providing a more up-to-date alternative. This study aimed to assess the physical flood vulnerability of buildings in Kota Bharu, Kelantan, Malaysia, using a combination of geospatial data and several machine learning classifiers. A detailed land use and cover classification was performed using high-spatial-resolution satellite imagery and airborne LiDAR to identify affected buildings in the area. Building parameters (height and area) and relevant image features were obtained from the building footprint in the geospatial data and used to classify the vulnerability index with the machine learning classifiers. The estimated vulnerability results were validated using in situ building damage data from previous flood events. The results showed that the combination of geospatial data and the machine learning framework achieved 95% accuracy using the random forest classifier and digitised building footprint. Furthermore, building size was found to be the most important factor in determining vulnerability. This geospatial-based approach to building vulnerability assessment demonstrated a good correlation with in situ flood vulnerability data, indicating its feasibility for rapid and large-scale building flood vulnerability assessments.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2879 - 2907"},"PeriodicalIF":2.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879506","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":"CO2 characterization using seismic inversion based on global optimization techniques for enhanced reservoir understanding: a comparative study","authors":"Ajay Pratap Singh,&nbsp;Ravi Kant,&nbsp;Satya Prakash Maurya,&nbsp;Brijesh Kumar,&nbsp;Nitin Verma,&nbsp;Raghav Singh,&nbsp;Kumar Hemant Singh,&nbsp;Manoj Kumar Srivastava,&nbsp;Gopal Hema","doi":"10.1007/s11600-025-01529-1","DOIUrl":"10.1007/s11600-025-01529-1","url":null,"abstract":"<div><p>Characterization of CO₂ in subsurface reservoirs is an important aspect of ensuring the effectiveness and safety of storage operations. Seismic inversion technique, widely applied in the petroleum industry for tasks such as quantitative reservoir characterization and improved oil recovery, is now finding potential application in estimating the extension of CO₂ plumes within an underground reservoir. Seismic inversion, coupled with global optimization techniques, offers a powerful approach to enhance reservoir understanding in CCS projects. This paper presents a comprehensive study on the application of a global optimization workflow to increase subsurface resolution in the CO₂ storage. Global optimization techniques including simulated annealing and particle swarm optimization are employed to optimize the subsurface model and estimate the P-wave impedance. We used the Sleipner field in the Norwegian North Sea which is extracting gas with high CO₂ content, and for environmental reasons, they have been injecting more than 11 million tons of CO₂ into the Utsira sand saline aquifer above the hydrocarbon reserves since 1996. To monitor the spread of this CO₂ plume and ensure the safety of the upper layers, a series of seven 3D seismic surveys have been conducted. Our study concentrated on vintage data from 1994 (before CO₂ injection) and 1999 and 2006 (after an 8.4 Mt CO₂ injection). The workflow incorporates prior information from well logs, facilitating faster convergence and detailed subsurface representations. The findings suggest that the application of global optimization techniques is advantageous for optimizing earth’s subsurface models, particularly in the context of CO₂ storage initiatives. Although we faced challenges due to the absence of time-lapse well-log data in the specific area of interest, we successfully applied our inverse workflow to generate acoustic impedance data, to the best of our knowledge. These findings offer valuable insights for enhancing the understanding of CO₂ dispersion within a reservoir.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2551 - 2568"},"PeriodicalIF":2.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879504","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":"High-accuracy velocity analysis for multiple AVO seismic data","authors":"Yankai Xu,&nbsp;Jiawei Li,&nbsp;Jiao Qi,&nbsp;Siyuan Cao,&nbsp;Hongwei Liu,&nbsp;Weiling Li,&nbsp;Hongduo Zhu","doi":"10.1007/s11600-024-01524-y","DOIUrl":"10.1007/s11600-024-01524-y","url":null,"abstract":"<div><p>The velocity analysis plays an important role in seismic data processing. Although the existing methods have improved noise resistance and accuracy, they are not well applied to data containing multiple amplitude-variations-with-offset (AVO). To solve the problem, we propose a high-accuracy velocity analysis method with polarity correlation in this paper. Firstly, we use singular values of seismic data to construct the weighting function method, which reduced the influence of noise and improved the accuracy of the velocity spectra. Then, a polarity-related information is proposed with multiple AVO and used in the weighting function. Synthetic and field data results show that the proposed method has higher accuracy with strong noise and multiple AVO.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"73 3","pages":"2569 - 2579"},"PeriodicalIF":2.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879507","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}