Following an earthquake, various postseismic mechanisms act to relax and redistribute stress concentrations in the crust and upper mantle (Freed, 2005). In addition to seismic aftershocks, postseismic mechanisms include aseismic afterslip (

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

Aseismic afterslip has been proposed to drive aftershock sequences. Both afterslip moment and aftershock number broadly increase with mainshock size, but can vary beyond this scaling. We examine whether relative afterslip moment (afterslip moment/mainshock moment) correlates with several key aftershock sequence characteristics, including aftershock number and cumulative moment (both absolute and relative to mainshock size), seismicity rate change, b‐value, and Omori decay exponent. We select Mw ≥ 4.5 aftershocks for 41 tectonically varied mainshocks with available afterslip models. Against expectation, relative afterslip moment does not correlate with tested aftershock characteristics or background seismicity rate. Furthermore, adding afterslip moment to mainshock moment does not improve predictions of aftershock number. Our findings place useful empirical constraints on the link between afterslip and potentially damaging Mw ≥ 4.5 aftershocks and raise questions regarding the role afterslip plays in aftershock generation.

<p>Aftershock sequences following large tectonic earthquakes exhibit considerable spatio-temporal complexity and suggest causative mechanisms beyond co-seismic, elasto-static Coulomb stress changes in the crust. Candidate mechanisms include dynamic triggering and postseismic processes such as viscoelastic relaxation, poroelastic rebound and aseismic afterslip, which has garnered particular interest recently. Aseismic afterslip – whereby localized frictional sliding within velocity-strengthening rheologies acts to redistribute lithospheric stresses in the postseismic phase – has been suggested by numerous studies to exert dominant control on aftershock sequence evolution, including productivity, spatial distribution and temporal decay.</p> <p>As evidence is based overwhelmingly on individual case study analysis, we wish to systematically compare key metrics of aseismic afterslip and corresponding aftershock sequences to investigate this relationship. We specifically look for any empirical relationship between the seismic-equivalent moment of aseismic afterslip episodes and the corresponding aftershock sequence productivity. We first compile published afterslip models into a database containing moment estimates over varying time periods, as well as spatial distributions, temporal decays and modelling methodology as a supplementary resource. We then identify the corresponding aftershock sequence from the globally comparable USGS PDE catalog. As expected, coseismic moment exerts an obvious control on both afterslip moment and aftershock productivity – an effect we control for by normalising by mainshock moment and expected productivity (the Utsu-Seki law) respectively. Preliminary results suggest broad variability of both afterslip moment and aftershock productivity with no obvious control of afterslip on aftershocks beyond the scaling with mainshock size, including when separated by mainshock mechanism or region. As this study is insensitive to spatial and temporal distributions, we cannot rule out the potential influence afterslip exerts in these but find no evidence that afterslip drives overall productivity of aftershock sequences.</p>

for toroidal plasmas is presented for the first time in the XGC edge gyrokinetic particle-in-cell code, starting from the δf implementation by M. Cole et al. [Phys. Plasmas 28, 034501 (2021)]. An example simulation is also presented.