Earth and Space Science最新文献

{"title":"A Microphysics Model of Multicomponent Venus' Clouds With a High-Accuracy Condensation Scheme","authors":"H. Karyu,&nbsp;T. Kuroda,&nbsp;A. Mahieux,&nbsp;S. Viscardy,&nbsp;A. Määttänen,&nbsp;N. Terada,&nbsp;S. Robert,&nbsp;A. C. Vandaele,&nbsp;M. Crucifix","doi":"10.1029/2025EA004203","DOIUrl":"https://doi.org/10.1029/2025EA004203","url":null,"abstract":"<p>Accurate modeling of the Venusian cloud structure remains challenging due to its complex microphysical properties. Condensation primarily determines the cloud particle size distribution within the various cloud layers. However, existing Venus microphysics models mainly use a full-stationary bin scheme, which may be prone to numerical diffusion during condensation. To address this, we developed a new microphysics model, the Simulator of Particle Evolution, Composition, and Kinetics (SPECK), which incorporates a moving-center bin scheme designed to minimize numerical diffusion. Furthermore, SPECK can accommodate any number of size distributions with multiple components, enabling versatile applications for more complex cloud systems. The 0-D simulations demonstrated that this microphysics framework is a reliable tool for modeling cloud microphysics under Venusian atmospheric conditions, particularly in capturing condensation and evaporation processes. We further validated SPECK against recent Venus microphysics models in 1-D simulations. The moving-center scheme is shown to exhibit less numerical diffusion compared to an existing model based on a full-stationary bin scheme, allowing for more accurate calculations of microphysical processes. Furthermore, SPECK reproduces the cloud structure observed by the Pioneer Venus Large Probe, using the same computational settings adopted in the latest microphysical model study. Thanks to the suppressed numerical diffusion, SPECK achieves high accuracy at half the typical resolution while reducing computational time sixfold, making it a promising tool for future 3-D modeling. This microphysics framework will be useful for the upcoming EnVision mission and is applicable to other planetary atmospheres, including those of Mars, Titan, gas giants, and exoplanets.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Bathymetry and Agricultural Crop Studies From ICESat-2: The Density-Dimension Algorithm","authors":"Xiaomei Lu,&nbsp;Yongxiang Hu,&nbsp;Ali Omar,&nbsp;Charles R. Trepte","doi":"10.1029/2024EA004037","DOIUrl":"https://doi.org/10.1029/2024EA004037","url":null,"abstract":"<p>The Density-Dimension Algorithm (DDA) was originally developed to retrieve land surface, vegetation canopy, and sea ice freeboard heights from the photon clouds measured by the multi-beamed micropulse lidar flying aboard NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission. The DDA employs weight functions to calculate a density field, thereby introducing an additional dimension to the photon clouds. Then, the density field is analyzed using local peak and threshold methods to facilitate the separation of feature signal photons from nearby noise photons. In this work we extend the DDA to water bathymetry (DDA-bathymetry) and agricultural crop (DDA-crop) studies. Our results demonstrate that the technique effectively identifies bathymetric and crop photons across various background conditions, including clear water, turbid water with increased suspended particles, nighttime observations with reduced background noise, and daytime observations with solar background noise.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Multi-Satellite Tracking of Surface Water Storage Change in the Era of Surface Water and Ocean Topography (SWOT) Satellite Mission","authors":"Pritam Das,&nbsp;Faisal Hossain","doi":"10.1029/2024EA004178","DOIUrl":"https://doi.org/10.1029/2024EA004178","url":null,"abstract":"<p>The Surface Water and Ocean Topography (SWOT) satellite, launched in December 2022, represents a significant advancement in the remote sensing of global water bodies, providing simultaneous measurements of Water Surface Elevation (WSE) and extent in all-weather conditions. This study evaluates SWOT's capability to estimate reservoir storage dynamics in comparison to pre-SWOT methods. SWOT demonstrates high accuracy in measuring WSE, achieving a median <i>R</i>² close to 1 and root mean square errors nearly an order of magnitude lower compared to earlier non-SWOT approaches. SWOT offers substantial improvement over single-sensor and multi-sensor methods, due to spatial averaging of distributed elevation measurements, which was further validated by similar measurements of the ICESat-2 satellite. The key limiting factor in estimating storage from elevation measuring sensors was found to be the accuracy of Area-Elevation-Volume curve. Furthermore, preliminary applications of machine learning to integrate SWOT with non-SWOT data sets show promise, although constrained by limited data availability of SWOT as of late 2024.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144300405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Predictions of Equatorial Vertical Plasma Drift Using TEC Data and a Neural Network Model","authors":"S. A. Reddy,&nbsp;X. Pi,&nbsp;C. Forsyth,&nbsp;A. Aruliah,&nbsp;A. Smith","doi":"10.1029/2024EA004167","DOIUrl":"https://doi.org/10.1029/2024EA004167","url":null,"abstract":"<p>Vertical plasma drift, <i>v</i>_<i>z</i>, plays a key role in the dynamics, morphology, and space weather effects of the equatorial and low latitude ionosphere. Modeling the drift has been an on-going effort for climatology-based prediction. To address daily prediction, the <i>Vertical drIfts</i>: <i>Predicting Equatorial ionospheRic dynamics</i> (VIPER) model has been developed. VIPER is a machine learning model that is trained on total electron content (TEC) data to predict low-latitude vertical plasma drift observed by the C/NOFS mission across the period 2009–2015. The uniqueness of VIPER is that it uses TEC data for the prediction, and the data is globally and readily available. A Gaussian fitting routine is developed to strengthen the link between TEC and <i>v</i>_<i>z</i>. VIPER is a multi-layer perceptron framework with Monte Carlo (MC) uncertainty estimation capabilities. It has a mean absolute error of 8.3 m/s, an R of 0.89/1, and a skill of 0.78/1, all of which are strong scores. The model is capped at quiet and unsettled activity levels (Kp &lt; 3). MC analysis reveals that predictions should be interpreted as distributions and the uncertainty can vary with distributions of TEC data and regions of prediction even if the predicted value is the same. VIPER offers longitudinally global coverage and uncertainty estimation capabilities. It could also be expanded to handle storm-time conditions with additional work.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144300404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Regional Uncertainty Analysis in the Air–Sea CO2 Flux","authors":"L. Gloege,&nbsp;M. D. Eisaman","doi":"10.1029/2024EA004032","DOIUrl":"https://doi.org/10.1029/2024EA004032","url":null,"abstract":"&lt;p&gt;Accurate quantification of the ocean carbon sink and its associated uncertainty is critical for guiding international policy efforts and the accurate monitoring, reporting, and verification of marine carbon dioxide removal interventions. Here we use error propagation to break down the uncertainty in air–sea &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;CO&lt;/mtext&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{CO}}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; flux into three primary sources: the gas transfer velocity &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mi&gt;w&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $left({k}_{w}right)$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, the solubility &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;K&lt;/mi&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $left({K}_{0}right)$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, and the difference in partial pressure of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;CO&lt;/mtext&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${text{CO}}_{2}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;Δ&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;pCO&lt;/mtext&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $left({Delta }{text{pCO}}_{2}right)$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; between the ocean and atmosphere. These are further decomposed into uncertainties from the underlying variables (e.g., temperature and salinity used to calculate &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;K&lt;/mi&gt;\u0000 &lt;mn&gt;0&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; ${K}_{0}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). We find gas transfer velocity is the dominant term driving uncertainty in the air–sea &lt;span&gt;&lt;/span&gt;","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144292296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"DepthFormer: Depth-Enhanced Transformer Network for Semantic Segmentation of the Martian Surface From Rover Images","authors":"Yuan Ma,&nbsp;Zhaojin Li,&nbsp;Bo Wu,&nbsp;Ran Duan","doi":"10.1029/2024EA003812","DOIUrl":"https://doi.org/10.1029/2024EA003812","url":null,"abstract":"<p>The Martian surface, with its diverse landforms that reflect the planet's evolution, has attracted increasing scientific interest. While extensive data is needed for interpretation, identifying landform types is crucial. This semantic information reveals underlying features and patterns, offering valuable scientific insights. Advanced deep learning techniques, particularly Transformers, can enhance semantic segmentation and image interpretation, deepening our understanding of Martian surface features. However, current publicly available neural networks are trained in the context of Earth, rendering the direct use of the Martian surface impossible. Besides, the Martian surface features poorly texture and homogenous scenarios, leading to difficulty in segmenting the images into favorable semantic classes. In this paper, an innovative depth-enhanced Transformer network—DepthFormer is developed for the semantic segmentation of Martian surface images. The stereo images acquired by the Zhurong rover along its traverse are used for training and testing the DepthFormer network. Different from regular deep-learning networks only dealing with three bands (red, green and blue) of images, the DepthFormer incorporates the depth information available from the stereo images as the fourth band in the network to enable more accurate segmentation of various surface features. Experimental evaluations and comparisons using synthesized and actual Mars image data sets reveal that the DepthFormer achieves an average accuracy of 98%, superior to that of conventional segmentation methods. The proposed method is the first deep-learning model incorporating depth information for accurate semantic segmentation of the Martian surface, which is of significance for future Mars exploration missions and scientific studies.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All-Sky Imagers","authors":"Daniel Okoh,&nbsp;Claudio Cesaroni,&nbsp;Babatunde Rabiu,&nbsp;Kazuo Shiokawa,&nbsp;Yuichi Otsuka,&nbsp;Samuel Ogunjo,&nbsp;Aderonke Akerele,&nbsp;John Bosco Habarulema,&nbsp;Bruno Nava,&nbsp;Yenca Migoya-Orué,&nbsp;Punyawi Jamjareegulgarn,&nbsp;Adeniran Seun,&nbsp;Ogechi Adama,&nbsp;George Ochieng,&nbsp;James Ameh,&nbsp;Adero Awuor,&nbsp;Paul Baki","doi":"10.1029/2024EA004117","DOIUrl":"https://doi.org/10.1029/2024EA004117","url":null,"abstract":"<p>Equatorial plasma bubbles (EPBs) disrupt satellite-based communication and navigation systems, particularly in equatorial regions. Reliable detection and classification of EPBs from all-sky imager (ASI) images are essential for accurate space weather monitoring and forecasting. This study presents a novel bootstrapping convolutional neural network (CNN) approach to optimize automated EPB detection on ASI images for operational space weather monitoring applications, and overcoming challenges related to image variability and imbalanced data sets. Data used for CNN training were obtained from the optical mesosphere thermosphere imagers ASI installed at the Space Environment Research Laboratory, National Space Research and Development Agency, Abuja during the period from 2015 to 2020. Our method involved training three sub-models, and aggregating their predictions. The CNN trainings were conducted on three sub-datasets of 3,000 images each, categorized as “EPB,” “Noisy/Cloudy” or “No EPB.” Three corresponding sub-models were developed from the CNN trainings. The three sub-model classifications independently gave prediction accuracies of 98.67%, 98.33%, and 95.83% on a reserved test data set of 600 images. Ensemble models further improved the model prediction accuracies to 99.17% and 99.33% for methods based on the mean of sub-model probabilities and the mode of sub-model classifications respectively. Our results indicate that the bootstrapping CNN technique enhanced the EPB detection accuracy, providing a powerful tool for real-time space weather monitoring applications, and implications for improving operational reliability of satellite-based navigation and communication in the equatorial region.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"A Simple Snowfall Retrieval Algorithm for the GPM Dual-Frequency Precipitation Radar: Development and Validation With OLYMPEX Campaign Observation","authors":"S. Akiyama,&nbsp;S. Shige,&nbsp;K. Aonashi,&nbsp;T. Iguchi","doi":"10.1029/2024EA003962","DOIUrl":"https://doi.org/10.1029/2024EA003962","url":null,"abstract":"<p>The current operational algorithm for the Ku- and Ka-band Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite, which does not effectively utilize Ka-band radar, underestimates snowfall amount. We developed a dual-frequency method (DF-method) that can be incorporated into the framework of the DPR operational algorithm. Estimates from the DF-method, as well as those from the operational algorithm, were validated against data nearly simultaneously measured by in situ airborne instruments and those from a ground-based radar during the Olympic Mountains Experiment (OLYMPEX). The results showed the DF-method produced high correlation, but some bias dependent on an assumed particle model. Both the operational algorithm and the DF-method using the scattering properties of the spheroid model equivalent to the best aggregate model yielded unsatisfactory results, indicating that it is important to use realistic snow scattering properties in the DF-method, rather than relying on the Mie or T-matrix scattering.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003962","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery","authors":"Robert Popescu,&nbsp;Nantheera Anantrasirichai,&nbsp;Juliet Biggs","doi":"10.1029/2024EA003892","DOIUrl":"https://doi.org/10.1029/2024EA003892","url":null,"abstract":"<p>Satellite-based Interferometric Synthetic Aperture Radar (InSAR) images have the potential to detect volcanic deformation prior to eruptions, but while a vast number of images are routinely acquired, only a small percentage contain volcanic deformation events. Manual inspection could miss these anomalies, and an automatic system modeled with supervised learning requires suitably labeled data sets. To tackle these issues, this paper explores the use of unsupervised deep learning on InSAR images for the purpose of identifying volcanic deformation as anomalies. We test three different state-of-the-art architectures, one convolutional neural network Patch Distribution Modeling (PaDiM) and two generative models (GANomaly and Denoising diffusion probabilistic models (DDPM)). We propose a preprocessing approach to deal with noisy and incomplete data points. We further improve the performance of PaDiM by using a weighted distance, assigning greater importance to features from deeper layers. The final framework was tested with five different volcanoes, which have different characteristics and its performance was compared against an existing supervised learning method for volcanic deformation detection. The experiments show that our final anomaly detection outperforms the supervised learning method, particularly where the characteristics of deformation are unknown. Our framework can thus be used to identify deformation at volcanoes without needing prior knowledge about the deformation patterns present there.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 \u0000 \u0000 \u0000 b\u0000 \u0000 $b$\u0000 -Value Tomography of the Calabrian Arc","authors":"C. Godano,&nbsp;G. Petrillo,&nbsp;A. Tramelli,&nbsp;V. Convertito","doi":"10.1029/2024EA004065","DOIUrl":"https://doi.org/10.1029/2024EA004065","url":null,"abstract":"<p>In the Calabrian Arc subduction zone, the notable lack of seismicity at depths near 100 km strongly suggests the presence of slab detachment. Contrary to typical patterns, where <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 </mrow>\u0000 <annotation> $b$</annotation>\u0000 </semantics></math>-values decrease with depth, our b-value mapping reveals unexpectedly high <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 </mrow>\u0000 <annotation> $b$</annotation>\u0000 </semantics></math>-values at these depths. Within the 100–150 km depth interval, the gradient of the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>b</mi>\u0000 </mrow>\u0000 <annotation> $b$</annotation>\u0000 </semantics></math>-value reaches its peak, indicating a significant reduction in stress. We propose four potential interpretations for these observations: (a) fluid-induced weakening due to dehydration processes, (b) heterogeneity at the slab tip reducing rupture propagation, (c) creeping zone behavior at the detachment tip, and (d) post-detachment damage to the rocks, leaving them unable to support stress. These hypotheses remain beyond experimental verification at present. This study underscores the complex interplay of geological processes at depth and their implications for seismic hazard assessment in subduction zones.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144244183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}