Earth and Space Science最新文献

{"title":"A Method to Obtain Remotely Sensed Grain Size Distributions From Granular Deposits With Complex Surfaces","authors":"H. L. Jacobson,&nbsp;G. Walton,&nbsp;K. R. Barnhart,&nbsp;F. K. Rengers","doi":"10.1029/2025EA004376","DOIUrl":"https://doi.org/10.1029/2025EA004376","url":null,"abstract":"<p>Constraining the grain size distribution of granular deposits with complex surfaces is difficult with existing approaches. Field and laboratory techniques are time consuming and limited by the maximum grain size that laboratories can accommodate. In this study, we present a new method to identify the coarse fraction of the grain size distribution at a debris-flow fan deposit surveyed with terrestrial laser scanning (TLS) in Glenwood Canyon, Colorado, USA. This method is a novel grain segmentation algorithm developed for application to point cloud data of deposits with complex surfaces and angular grains ranging in size from centimeters to a meter. This approach combines an existing random forest machine learning method with a novel iterative clustering algorithm. We compared the grain size distribution from our algorithm with a Wolman pebble count conducted in the field, and found a root mean squared error of less than 2 cm from the 5th to 95th percentile of the grain size distribution of grains ranging from cobble to boulder sized (6.3–78 cm in our application). Finally, we compared our new algorithm with an existing open-source grain segregation algorithm, and our method outperformed the selected alternative when applied to the debris-flow deposit point cloud.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EA004376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331899","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":"DeFault: DEep-Learning-Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site","authors":"Hanchen Wang,&nbsp;Yinpeng Chen,&nbsp;Tariq Alkhalifah,&nbsp;Ting Chen,&nbsp;Youzuo Lin,&nbsp;David Alumbaugh","doi":"10.1029/2023EA003422","DOIUrl":"https://doi.org/10.1029/2023EA003422","url":null,"abstract":"&lt;p&gt;The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high-quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;DeFault&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $mathit{DeFault}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain-adaptation, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;DeFault&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $mathit{DeFault}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;DeFault&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $mathit{DeFault}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;DeFault&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $mathit{DeFault}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; on a field case study involving &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; injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;DeFault&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $mathit{DeFault}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. T","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023EA003422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331897","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":"Pre-Sunrise Equatorial Plasma Bubble Over Indonesia During the 11 May 2024 Super Geomagnetic Storm","authors":"Suraina,&nbsp;A. Rakhman,&nbsp;P. Abadi,&nbsp;L. O. M. M. Kilowasid,&nbsp;A. Y. Putra,&nbsp;S. Perwitasari,&nbsp;T. M. Irnaka","doi":"10.1029/2024EA004152","DOIUrl":"https://doi.org/10.1029/2024EA004152","url":null,"abstract":"<p>Equatorial Plasma Bubbles (EPBs) generally form around sunset in equatorial to low-latitude regions. However, based on observations of the rate total electron content (TEC) change index (ROTI) map over Indonesia and the ionosonde data from the Southeast Asian equatorial station during the super geomagnetic storm on 11 May 2024, we report that EPBs did not form during the post-sunset period. Instead, EPBs were observed to form pre-sunrise in the Indonesian region, an event that occurs rarely. These EPB structures developed and strengthened as they evolved and extended poleward. We suspect that the EPBs formed during the pre-sunrise period were caused by the eastward disturbance dynamo electric field (DDEF), which begins around midnight and continues until sunrise. As a result, plasma bubbles started forming near sunrise and survived until the morning. Observations from three ground-based GPS stations in Southeast Asia on May 11th, showed a significant decrease in TEC caused by EPBs pre-sunrise. However, no GNSS scintillation was detected during this period. In contrast, strong scintillation was observed at mid-latitudes. Before the formation of the EPB pre-sunrise, the peak height of the ionospheric F layer experienced a significant increase, likely caused by the DDEF during the recovery phase. The rise in the F layer height could support the growth rate of Rayleigh-Taylor instability. Therefore, DDEF becomes a major contributor to the formation of EPBs pre-sunrise.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331898","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 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}