English
Related papers

Related papers: The Seismic Wavefield Common Task Framework

200 papers

Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evaluation has been limited to computational benchmarks that may…

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the Multiscale…

Seismic processing plays a crucial role in transforming raw data into high-quality subsurface images, pivotal for various geoscience applications. Despite its importance, traditional seismic processing techniques face challenges such as…

Computer Vision and Pattern Recognition · Computer Science 2025-05-12 Fabian Fuchs , Mario Ruben Fernandez , Norman Ettrich , Janis Keuper

Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely used hard-parameter-sharing setting, a shared backbone is combined with task-specific…

Machine Learning · Computer Science 2026-04-16 Hevish Cowlessur , Chandra Thapa , Tansu Alpcan , Seyit Camtepe

Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would have immense societal value, in areas such as agricultural…

Machine Learning · Computer Science 2020-06-25 Sijie He , Xinyan Li , Timothy DelSole , Pradeep Ravikumar , Arindam Banerjee

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the…

Systems and Control · Electrical Eng. & Systems 2023-09-13 Lei Guo , Wenshuo Li , Yukai Zhu , Xiang Yu , Zidong Wang

Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software quality and reduce maintenance costs. This research investigates the combined effects of…

Software Engineering · Computer Science 2026-05-19 Ahmad Nauman Ghazi , Nagajyothi Devarapalli , Ashir Javeed , Sadi Alawadi , Fahed Alkhabbas , Khalid AlKharabsheh

The interpretation of seismic images faces challenges due to the presence of several uncertainty sources. Uncertainties exist in data measurements, source positioning, and subsurface geophysical properties. Understanding uncertainties' role…

Forecasting chaotic systems is a cornerstone challenge in many scientific fields, complicated by the exponential amplification of even infinitesimal prediction errors. Modern machine learning approaches often falter due to two opposing…

Machine Learning · Computer Science 2025-10-07 Harshil Vejendla

The application of models to assess the risk of the physical impacts of weather and climate and their subsequent consequences for society and business is of the utmost importance in our changing climate. The operation of such models is…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-09-28 Blair Edwards , Paolo Fraccaro , Nikola Stoyanov , Nelson Bore , Julian Kuehnert , Kommy Weldemariam , Anne Jones

With the rise of data volume and computing power, seismological research requires more advanced skills in data processing, numerical methods, and parallel computing. We present the experience of conducting training workshops over various…

Metal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and computational…

Biomolecules · Quantitative Biology 2024-12-17 Dong Chen , Chun-Long Chen , Guo-Wei Wei

Earthquake monitoring workflows are designed to detect earthquake signals and to determine source characteristics from continuous waveform data. Recent developments in deep learning seismology have been used to improve tasks within…

The prevailing methodology in data-driven fault detection leverages synthetic data for training neural networks. However, it grapples with challenges when it comes to generalization in surveys exhibiting complex structures. To enhance the…

Geophysics · Physics 2024-10-28 Yimin Dou , Minghui Dong , Kewen Li , Y uan Xiao

The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dense geophone deployments, distributed acoustic sensing (DAS) arrays, and large-scale 2D and…

Geophysics · Physics 2026-05-13 Jiahua Zhao , Umair bin Waheed , Jing Sun , Yang Cui , Nikos Savva , Eric Verschuur

We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures. Applying machine learning methods to solid-state materials…

Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things (IoT), replacing mechanistic and basic statistical models…

Recent advances in deep learning have revolutionized seismic monitoring, yet developing a foundation model that performs well across multiple complex tasks remains challenging, particularly when dealing with degraded signals or data…

Machine Learning · Computer Science 2025-07-10 Xinghao Wang , Feng Liu , Rui Su , Zhihui Wang , Lihua Fang , Lianqing Zhou , Lei Bai , Wanli Ouyang

Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for induced seismicity and successful prediction of laboratory…

Geophysics · Physics 2022-10-13 Laura Laurenti , Elisa Tinti , Fabio Galasso , Luca Franco , Chris Marone

While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse downstream tasks, the escalating computational requirements associated with their expanding…