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Abstract 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 in various forms of delivery to support the adoption of large-scale high-performance computing (HPC) and cloud computing, advancing seismological research. The seismological foci were on earthquake source parameter estimation in catalogs, forward and adjoint wavefield simulations in 2D and 3D at local, regional, and global scales, earthquake dynamics, ambient noise seismology, and machine learning. This contribution describes the series of workshops delivered as part of research projects, the learning outcomes for participants, and lessons learned by the instructors. Our curriculum was grounded on open and reproducible science, large-scale scientific computing and data mining, and computing infrastructure (access and usage) for HPC and the cloud. We also describe the types of teaching materials that have proven beneficial to the instruction and the sustainability of the program. We propose guidelines to deliver future workshops on these topics. 

SUMMARY Improving the resolution of seismic anelastic models is critical for a better understanding of the Earth’s subsurface structure and dynamics. Seismic attenuation plays a crucial role in estimating water content, partial melting and temperature variations in the Earth’s crust and mantle. However, compared to seismic wave-speed models, seismic attenuation tomography models tend to be less resolved. This is due to the complexity of amplitude measurements and the challenge of isolating the effect of attenuation in the data from other parameters. Physical dispersion caused by attenuation also affects seismic wave speeds, and neglecting scattering/defocusing effects in classical anelastic models can lead to biased results. To overcome these challenges, it is essential to account for the full 3-D complexity of seismic wave propagation. Although various synthetic tests have been conducted to validate anelastic full-waveform inversion (FWI), there is still a lack of understanding regarding the trade-off between elastic and anelastic parameters, as well as the variable influence of different parameter classes on the data. In this context, we present a synthetic study to explore different strategies for global anelastic inversions. To assess the resolution and sensitivity for different misfit functions, we first perform mono-parameter inversions by inverting only for attenuation. Then, to study trade-offs between parameters and resolution, we test two different inversion strategies (simultaneous and sequential) to jointly constrain the elastic and anelastic parameters. We found that a sequential inversion strategy performs better for imaging attenuation than a simultaneous inversion. We also demonstrate the dominance of seismic wave speeds over attenuation, underscoring the importance of determining a good approximation of the Hessian matrix and suitable damping factors for each parameter class. 

SUMMARY The uneven distribution of earthquakes and stations in seismic tomography leads to slower convergence of nonlinear inversions and spatial bias in inversion results. Including dense regional arrays, such as USArray or Hi-Net, in global tomography causes severe convergence and spatial bias problems, against which conventional pre-conditioning schemes are ineffective. To save computational cost and reduce model bias, we propose a new strategy based on a geographical weighting of sources and receivers. Unlike approaches based on ray density or the Voronoi tessellation, this method scales to large full-waveform inversion problems and avoids instabilities at the edges of dense receiver or source clusters. We validate our strategy using a 2-D global waveform inversion test and show that the new weighting scheme leads to a nearly twofold reduction in model error and much faster convergence relative to a conventionally pre-conditioned inversion. We implement this geographical weighting strategy for global adjoint tomography.