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Journal of Geophysical Research: Machine Learning and Computation
Scopus
ISSN 2993-5210 · Wiley · 315 artikel di indeks
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Artikel Jurnal
A Causal‐Spatial Explainability Framework for Geological Hazard Susceptibility Modeling
Abstract Data‐driven susceptibility models often act as black boxes, predicting where hazards may occur without explaining why. To address this gap, we propose a Causal‐Prior (CP) framework that integrates validated causal knowledge into training while yielding spatially grounded explanations…
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Tackling Small Lunar Impact Crater Classification: A Novel Augmented Data Set and Enhanced Deep Learning Framework
Abstract Small lunar impact craters, exhibiting diverse morphological characteristics, represent the most abundant geological features on the Moon's surface. These structures serve as crucial indicators for understanding regolith thickness distribution and surface modification processes. While…
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Forecasting the Rate of Induced Seismicity as a Neural Temporal Point Process
Abstract Forecasting is an essential part of risk mitigation, where the mitigation efficacy depends strongly on the quality of forecasts. We explore the neural temporal point process as a deep learning framework to forecast induced earthquakes. We train our deep learning model using numerous…
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Development of a Short‐Term Forecasting Model for Tropical Cyclone Tracks Based on Ventilation Flow and Artificial Intelligence Algorithms
Abstract While research on forecasting tropical cyclone (TC) intensity via statistical methods is relatively abundant, studies on TC track forecasting using such methods remain scarce. Existing statistical TC track forecasts primarily rely on time‐series factors of TC motion; however, their…
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SynQPF‐Net: Short‐Term Precipitation Forecasts by Integrating GraphCast Predictions and High‐Resolution Observational Analyses
Abstract While data‐driven global weather models (e.g., GraphCast) excel at large‐scale circulation forecasts, they exhibit systematic biases in heavy precipitation prediction due to European Centre for Medium‐Range Weather Forecasts Reanalysis (ERA5) uncertainties and neural networks' tendency to…
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Impact‐Based Drought Detection via Interpretable Machine Learning and Causal Discovery
Abstract The intensification of drought events due to climate change presents unprecedented risks and widespread socioenvironmental impacts. Traditional drought indices, which rely on hydrometeorological anomalies, often fall short in capturing the full extent of these impacts, as they primarily…
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Reconstructing Magnetotail Reconnection Events Using Data Mining is Feasible and Repeatable
Abstract Recently, Stephens et al. (2023), https://doi.org/10.1029/2022ja031066 utilized a data mining (DM) algorithm, applied to 26 years of magnetospheric magnetometer observations coupled with a flexible formulation of the magnetospheric magnetic field, to reconstruct the global configuration of…
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Detection of Eye Occurrence in Sequential Satellite Infrared Imagery and Its Application to Improve Deep Learning‐Based Tropical Cyclone Intensity Estimation
Abstract Tropical cyclone (TC) intensity estimation remains a critical yet challenging task, especially for intense storms that are often underestimated by both conventional and deep learning satellite‐based methods. Among various structural features, eye formation is the most distinct…
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PrecipFusionNet: A Unified Deep Learning Model for Improving Numerical Precipitation Prediction
Abstract Numerical weather prediction (NWP) often suffers from substantial biases when forecasting extreme rainfall. Traditional corrections tend to underuse spatial information, while deep learning approaches typically struggle with data imbalance for rare events. In this study, we propose…
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Skillful Short‐Term Forecasting of Clouds With a Cascade Diffusion Model
Abstract Accurate short‐term forecasting of clouds from satellite imagery is a foundational technology for downstream meteorological disaster mitigation and aviation safety enhancement systems, particularly in developing countries and remote areas lacking ground‐based observation infrastructure…
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Identifying MESSENGER Magnetospheric Boundary Crossings Using a Random Forest Region Classifier
Abstract We present a new list of bow shock and magnetopause crossings based on automated region classification for the MESSENGER (Mercury Surface, Space Environment, Geochemistry and Ranging) mission. We fit a random forest model to magnetometer and ephemeris data to classify the solar wind…
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Machine Learning Approximations for Fast and Accurate Prediction of Nonlinear Four‐Wave Interactions in Spectral Wave Models
Abstract Operational wave forecasting requires a delicate balance between the accuracy and computational speed of the spectral wave model used, in which the nonlinear wave–wave interaction “source” term plays an important role. The exact formulation of these nonlinear four‐wave interactions…
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Geolipidomics: Non‐Targeted Analysis of Rapidly Heated Geologic Samples With Gas‐Chromatography‐Mass Spectrometry
Abstract Lipids are powerful tools for reconstructing paleoenvironments and geologic processes because of their ubiquity and preservation in the rock record. Traditionally, organic geochemistry studies are targeted analyses of known biomarker compounds with known responses to past environmental…
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Quantitative Characterization of Gravel Bed Structures Using 2DSSFs and Machine Learning‐Based Surrogate Surface Generation
Abstract Accurate characterization of gravel structures is essential for understanding riverbed roughness, flow resistance, and sediment transport. However, quantifying the definitive characteristics of these gravel structures remains challenging due to the inherent complexity of particle spatial…
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A Novel Deep Learning Approach for TEC Map Completion Using Image Equation‐Guided Loss
Abstract This study introduces the Image Equation Mechanism‐Completion Generative Adversarial Network (IEM‐CGAN), a groundbreaking deep learning framework that revolutionizes the reconstruction of large‐scale global ionospheric total electron content (TEC) maps. The model achieves unprecedented…
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FMLD: A Vertical Federated Learning Framework for Privacy‐Preserving Multimodal Landslide Detection
Abstract Landslides are among the most severe global geohazards posing a significant threat to human life and infrastructure. To support landslide detection and prediction, various geohazard monitoring approaches have been developed, such as optical remote sensing imagery, light detection and…
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A Generalized Data‐Driven Shoreline Model at the Regional Scale
Abstract Shoreline models underpin long‐term coastal planning and hazard mitigation but are often limited by site‐specific calibration and simplified process representations. While machine learning (ML) approaches have recently matched or exceeded the skill of traditional models in deterministic…
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Learning Wave Scattering Properties From Seismograms
Abstract Heterogeneities in the Earth's crust scatter seismic waves at many scales, trapping seismic energy and producing coda waves that encode valuable information on geological structures. In regions such as volcanoes and fault systems, analyzing coda waves is essential for characterizing…
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NLML: A Deep Neural Network Emulator for the Exact Nonlinear Interactions in a Wind Wave Model
Abstract Nonlinear wave interactions describe the resonant energy transfer between wave components, playing a fundamental role in the evolution of ocean wave spectra. Nonlinear wave interactions significantly influence wave growth and development, making them essential for accurate wave modeling…
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Should all Noises Be Treated Equally: Impact of Input Noise Variability on Neural Network Robustness
Abstract Geophysical data collected from active field sites are often contaminated by complex and heterogeneous noise, obscuring weak seismic events, and complicating automated interpretation. Although deep learning offers promising solutions for seismic processing, its performance is highly…