Acta Geophysica最新文献_第8页

Assessing the land surface temperature dynamics and urban heat island effect in Varanasi, India 评估印度瓦拉纳西的地表温度动态和城市热岛效应

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-11 DOI: 10.1007/s11600-026-02018-9

Adarsh Kumar, Vishal Chettry

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引用次数: 0

Spatial variation analysis of periodically correlated hydrological time series: a new insight 周期性相关水文时间序列的空间变异分析：新见解

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-11 DOI: 10.1007/s11600-026-02008-x

Nader Jandaghi, Mojtaba Ghareh Mahmoodlu, Majid Azimmohseni, Mahnaz Khalafi

{"title":"Spatial variation analysis of periodically correlated hydrological time series: a new insight","authors":"Nader Jandaghi, Mojtaba Ghareh Mahmoodlu, Majid Azimmohseni, Mahnaz Khalafi","doi":"10.1007/s11600-026-02008-x","DOIUrl":"10.1007/s11600-026-02008-x","url":null,"abstract":"<div><p>Hydrological time series often show periodic behavior influenced by seasonal and climate factors, and they are commonly modeled as periodically correlated (cyclostationary) stochastic processes. A key challenge in hydrology and climate science is quantitatively comparing such time series from different monitoring stations to study spatial variations in hydrological and climatic patterns and their driving forces. This study introduces an efficient and flexible spectral-domain method for comparing periodically correlated time series based on their discrete Fourier transform vectors. The approach uses a multivariate complete block design, where Fourier frequencies are considered as blocks to statistically assess spatial differences among the series. The effectiveness of the proposed test is extensively evaluated through simulation experiments under various realistic conditions, showing that statistical power increases with the number of Fourier frequencies. All simulations and analyses were conducted in the R programming environment, utilizing custom scripts and publicly available packages to facilitate reproducibility. To demonstrate practical use, the method is applied to monthly runoff and precipitation time series data from multiple hydrometric and meteorological stations. The results successfully identify well-known hydrological and climatic features in the study regions, demonstrating its practical applicability and its ability to uncover meaningful spatial patterns in seasonally driven hydrological systems. This framework provides a valuable tool for analyzing hydrological and climate time series data, aiding in understanding spatial variability and supporting better decision-making for regional water management and climate impact assessment.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148927479","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}

引用次数: 0

Review of Tectonic activity and the causes of the 2023 Türkiye earthquakes 2023年<s:1>基耶地震构造活动及其成因综述

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-11 DOI: 10.1007/s11600-026-02013-0

Kutubuddin Ansari

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引用次数: 0

Spatial variation analysis of periodically correlated hydrological time series: a new insight 周期性相关水文时间序列的空间变异分析：新见解

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-11 DOI: 10.1007/s11600-026-02008-x

Nader Jandaghi, Mojtaba Ghareh Mahmoodlu, Majid Azimmohseni, Mahnaz Khalafi

{"title":"Spatial variation analysis of periodically correlated hydrological time series: a new insight","authors":"Nader Jandaghi, Mojtaba Ghareh Mahmoodlu, Majid Azimmohseni, Mahnaz Khalafi","doi":"10.1007/s11600-026-02008-x","DOIUrl":"10.1007/s11600-026-02008-x","url":null,"abstract":"<div><p>Hydrological time series often show periodic behavior influenced by seasonal and climate factors, and they are commonly modeled as periodically correlated (cyclostationary) stochastic processes. A key challenge in hydrology and climate science is quantitatively comparing such time series from different monitoring stations to study spatial variations in hydrological and climatic patterns and their driving forces. This study introduces an efficient and flexible spectral-domain method for comparing periodically correlated time series based on their discrete Fourier transform vectors. The approach uses a multivariate complete block design, where Fourier frequencies are considered as blocks to statistically assess spatial differences among the series. The effectiveness of the proposed test is extensively evaluated through simulation experiments under various realistic conditions, showing that statistical power increases with the number of Fourier frequencies. All simulations and analyses were conducted in the R programming environment, utilizing custom scripts and publicly available packages to facilitate reproducibility. To demonstrate practical use, the method is applied to monthly runoff and precipitation time series data from multiple hydrometric and meteorological stations. The results successfully identify well-known hydrological and climatic features in the study regions, demonstrating its practical applicability and its ability to uncover meaningful spatial patterns in seasonally driven hydrological systems. This framework provides a valuable tool for analyzing hydrological and climate time series data, aiding in understanding spatial variability and supporting better decision-making for regional water management and climate impact assessment.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148927325","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}

引用次数: 0

Review of Tectonic activity and the causes of the 2023 Türkiye earthquakes 2023年<s:1>基耶地震构造活动及其成因综述

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-11 DOI: 10.1007/s11600-026-02013-0

Kutubuddin Ansari

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引用次数: 0

A novel generalized multi-source weighted ensemble scheme of precipitation simulations under global climate models for improving future drought characterization 全球气候模式下降水模拟的一种新的广义多源加权集合方案，用于改善未来干旱特征

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-11 DOI: 10.1007/s11600-026-02017-w

Aroob Shahzadi, Aamina Batool, Zulfiqar Ali, Humara Adnan, Azeem Ali, Nasrullah Khan, Veysi Kartal, Rizwan Niaz, Sajjad Haider Bhatti

{"title":"A novel generalized multi-source weighted ensemble scheme of precipitation simulations under global climate models for improving future drought characterization","authors":"Aroob Shahzadi, Aamina Batool, Zulfiqar Ali, Humara Adnan, Azeem Ali, Nasrullah Khan, Veysi Kartal, Rizwan Niaz, Sajjad Haider Bhatti","doi":"10.1007/s11600-026-02017-w","DOIUrl":"10.1007/s11600-026-02017-w","url":null,"abstract":"<div><p>Drought is a major climatic hazard with severe socioeconomic and environmental consequences, including water scarcity, agricultural losses, ecosystem degradation, and increased wildfire risks. Therefore, continuous and accurate drought monitoring is essential for reducing its adverse impact and supporting climate adaptation policies. In recent years, the use of simulated data of precipitation under global climate models (GCMs) is common in climate monitoring modules. However, inherent uncertainties and biases in individual GCMs necessitate the development of robust ensemble strategies to improve climate projections. Consequently, effective drought monitoring and future risk assessment rely on accurate ensemble of precipitation simulations under GCMs. This study introduces a novel generalized multi-source weighted ensemble scheme—hybrid distance-based weighting scheme (HDWS) to enhance precipitation simulations under GCMs. In HDWS, three statistical factors—1) point-to-point deviation, 2) Mahalanobis distance, and 3) regression analysis—are jointly integrated to derive weights for the ensemble of multiple GCMs in precipitation simulation. Application of HDWS is based on 18 GCMs for the Tibetan Plateau Region of China. The performance of HDWS is assessed against the conventional simple model averaging (SMA) approach using mean absolute error (MAE) and normalized relative absolute error (nRAE). The results of the comparative analysis indicate that HDWS significantly outperforms SMA. Furthermore, the study utilizes the ensemble data to estimate the standardized precipitation index (SPI) and assesses drought characteristics at 1-, 3-, 6-, and 12-month timescales.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148927627","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}

引用次数: 0

A novel generalized multi-source weighted ensemble scheme of precipitation simulations under global climate models for improving future drought characterization 全球气候模式下降水模拟的一种新的广义多源加权集合方案，用于改善未来干旱特征

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-11 DOI: 10.1007/s11600-026-02017-w

Aroob Shahzadi, Aamina Batool, Zulfiqar Ali, Humara Adnan, Azeem Ali, Nasrullah Khan, Veysi Kartal, Rizwan Niaz, Sajjad Haider Bhatti

{"title":"A novel generalized multi-source weighted ensemble scheme of precipitation simulations under global climate models for improving future drought characterization","authors":"Aroob Shahzadi, Aamina Batool, Zulfiqar Ali, Humara Adnan, Azeem Ali, Nasrullah Khan, Veysi Kartal, Rizwan Niaz, Sajjad Haider Bhatti","doi":"10.1007/s11600-026-02017-w","DOIUrl":"10.1007/s11600-026-02017-w","url":null,"abstract":"<div><p>Drought is a major climatic hazard with severe socioeconomic and environmental consequences, including water scarcity, agricultural losses, ecosystem degradation, and increased wildfire risks. Therefore, continuous and accurate drought monitoring is essential for reducing its adverse impact and supporting climate adaptation policies. In recent years, the use of simulated data of precipitation under global climate models (GCMs) is common in climate monitoring modules. However, inherent uncertainties and biases in individual GCMs necessitate the development of robust ensemble strategies to improve climate projections. Consequently, effective drought monitoring and future risk assessment rely on accurate ensemble of precipitation simulations under GCMs. This study introduces a novel generalized multi-source weighted ensemble scheme—hybrid distance-based weighting scheme (HDWS) to enhance precipitation simulations under GCMs. In HDWS, three statistical factors—1) point-to-point deviation, 2) Mahalanobis distance, and 3) regression analysis—are jointly integrated to derive weights for the ensemble of multiple GCMs in precipitation simulation. Application of HDWS is based on 18 GCMs for the Tibetan Plateau Region of China. The performance of HDWS is assessed against the conventional simple model averaging (SMA) approach using mean absolute error (MAE) and normalized relative absolute error (nRAE). The results of the comparative analysis indicate that HDWS significantly outperforms SMA. Furthermore, the study utilizes the ensemble data to estimate the standardized precipitation index (SPI) and assesses drought characteristics at 1-, 3-, 6-, and 12-month timescales.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148927324","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}

引用次数: 0

Assessing the land surface temperature dynamics and urban heat island effect in Varanasi, India 评估印度瓦拉纳西的地表温度动态和城市热岛效应

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-11 DOI: 10.1007/s11600-026-02018-9

Adarsh Kumar, Vishal Chettry

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引用次数: 0

Conditional diffusion-guided least-squares reverse time migration 条件扩散引导最小二乘逆时偏移

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-10 DOI: 10.1007/s11600-026-01991-5

Liwei Song, Weihan Tan, Ying Shi, Ying Zhang

{"title":"Conditional diffusion-guided least-squares reverse time migration","authors":"Liwei Song, Weihan Tan, Ying Shi, Ying Zhang","doi":"10.1007/s11600-026-01991-5","DOIUrl":"10.1007/s11600-026-01991-5","url":null,"abstract":"<div><p>To address the resolution limitations of least-squares reverse time migration (LSRTM) under band-limited illumination conditions, this paper proposes a diffusion-regularized LSRTM framework (DLSRTM). The method uses RTM images as physical conditions and trains a conditional denoising diffusion probabilistic model (C-DDPM) to learn the mapping from noise to high-resolution reflectivity, thereby obtaining a generative prior suitable for inversion. During inversion, an alternating strategy combining a data consistency step and a deterministic denoising projection is employed. First, the image generated by C-DDPM serves as the input for LSRTM model updates, followed by a prior projection through a conditional denoising operator. This implicitly replaces explicit regularization and effectively shrinks the solution space. To mitigate the computational cost of diffusion sampling, a denoising diffusion implicit model (DDIM)-based skip-step deterministic inference scheme is introduced to enhance efficiency. Numerical experiments on both synthetic and field data demonstrate that, when the training and target data distributions align, DLSRTM significantly outperforms conventional LSRTM in terms of deep interface continuity, amplitude fidelity, and artifact suppression. Moreover, it achieves comparable residual thresholds with fewer iterations, resulting in a synergistic improvement in both imaging quality and computational efficiency.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148927141","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}

引用次数: 0

Conditional diffusion-guided least-squares reverse time migration 条件扩散引导最小二乘逆时偏移

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-09-10 DOI: 10.1007/s11600-026-01991-5

Liwei Song, Weihan Tan, Ying Shi, Ying Zhang

{"title":"Conditional diffusion-guided least-squares reverse time migration","authors":"Liwei Song, Weihan Tan, Ying Shi, Ying Zhang","doi":"10.1007/s11600-026-01991-5","DOIUrl":"10.1007/s11600-026-01991-5","url":null,"abstract":"<div><p>To address the resolution limitations of least-squares reverse time migration (LSRTM) under band-limited illumination conditions, this paper proposes a diffusion-regularized LSRTM framework (DLSRTM). The method uses RTM images as physical conditions and trains a conditional denoising diffusion probabilistic model (C-DDPM) to learn the mapping from noise to high-resolution reflectivity, thereby obtaining a generative prior suitable for inversion. During inversion, an alternating strategy combining a data consistency step and a deterministic denoising projection is employed. First, the image generated by C-DDPM serves as the input for LSRTM model updates, followed by a prior projection through a conditional denoising operator. This implicitly replaces explicit regularization and effectively shrinks the solution space. To mitigate the computational cost of diffusion sampling, a denoising diffusion implicit model (DDIM)-based skip-step deterministic inference scheme is introduced to enhance efficiency. Numerical experiments on both synthetic and field data demonstrate that, when the training and target data distributions align, DLSRTM significantly outperforms conventional LSRTM in terms of deep interface continuity, amplitude fidelity, and artifact suppression. Moreover, it achieves comparable residual thresholds with fewer iterations, resulting in a synergistic improvement in both imaging quality and computational efficiency.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 5","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926947","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}

引用次数: 0