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Full waveform inversion (FWI) is one of the most challenging procedures to obtain quantitative information of the subsurface. For elastic inversions, when both compressional and shear velocities have to be inverted, the algorithmic issue becomes also a computational challenge due to the high cost related to modelling elastic rather than acoustic waves. This shortcoming has been moderately mitigated by using high-performance computing to accelerate 3D elastic FWI kernels. Nevertheless, there is room in the FWI workflows for obtaining large speedups at the cost of proper grid pre-processing and data decimation techniques. In the present work, we show how by making full use of frequency-adapted grids, composite shot lists and a novel dynamic offset control strategy, we can reduce by several orders of magnitude the compute time while improving the convergence of the method in the studied cases, regardless of the forward and adjoint compute kernels used.

<p>After large magnitude earthquakes have been recorded, a crucial task for hazard assessment is to quickly estimate Ground Shaking (GS) intensities at the affected region. Urgent physics-based earthquake simulations using High-Performance Computing (HPC) facilities may allow fast GS intensity analyses but are very sensitive to source parameter values. When using fast estimates of source parameters such as magnitude, location, fault dimensions, and/or Centroid Moment Tensor (CMT), simulations are prone to errors in their computed GS. Although the approaches to estimate earthquake location and magnitude are consolidated, depth location estimates are largely uncertain. Moreover, automatic CMT solutions are not always provided by seismological agencies, or such solutions are available at later times after waveform inversions allow the determination of moment tensor components. The uncertainty on these parameters, especially a few minutes after the earthquake has been registered, strongly affects GS maps resulting from simulations.</p><p>In this work, we present a workflow prototype to produce an uncertainty quantification method as a function of the source parameters. The core of this workflow is based on Machine Learning (ML) techniques. As a study case, we consider a domain of 110x80 km centered in 63.9ºN-20.6ºW in Southern Iceland, where the 17 best-mapped faults have hosted the historical events of the largest magnitude. We generate synthetic GS intensity maps using the AWP-ODC finite-difference code for earthquake simulation and a one-dimensional velocity model, with 40 recording surface stations. By varying a few source parameters (e.g. event magnitude, CMT, and hypocenter location), we finally model tens of thousands of hypothetical earthquakes. Our ML analog will then be able to relate GS intensity maps to source parameters, thus simplifying sensitivity studies.</p><p>Additionally, the results of this workflow prototype will allow us to obtain ML-based intensity maps a few seconds after an earthquake occurs exploiting the predictive power of ML techniques. We will evaluate the accuracy of these maps as standalone complements to GMPEs and simulations.</p>

Deadly earthquakes are events that are unpredictable, relatively rare and have a huge impact upon the lives of those who suffer their consequences. Furthermore, each earthquake has specific characteristics (location, magnitude, directivity) which, combined to local amplification and de-amplification effects, makes their outcome very singular. Empirical relations are the main methodology used to make early assessment of an earthquake's impact. Nevertheless, the lack of sufficient data registers for large events makes such approaches very imprecise. Physics-based simulators, on the other hand, are powerful tools that provide highly accurate shaking information. However, physical simulations require considerable computational resources, a detailed geological model, and accurate earthquake source information.A better early assessment of the impact of earthquakes implies both technical and scientific challenges. We propose a novel HPCbased urgent seismic simulation workflow, hereafter referred to as Urgent Computing Integrated Services for EarthQuakes (UCIS4EQ), which can deliver, potentially, much more accurate short-time reports of the consequences of moderate to large earthquakes. UCIS4EQ is composed of four subsystems that are deployed as services and connected by means of a workflow manager. This paper describes those components and their functionality. The main objective of UCIS4EQ is to produce ground-shaking maps and other potentially useful information to civil protection agencies. The first demonstrator will be deployed in the framework of the Center of Excellence for Exascale in Solid Earth (ChEESE, https://cheese.coe.eu/, last access: 12 Feb. 2020).

Geophysics 2 ABSTRACTFull Waveform Inversion (FWI) in seismic scenarios continues to be a complex procedure for subsurface imaging that might require extensive human interaction, in terms of model setup, constraints and data preconditioning. The underlying reason is the strong non-linearity of the problem that forces the addition of a priori knowledge (or bias) in order to obtain geologically sound results. In particular, when the use of long offset receiver is not possible or may not favor the reconstruction of the fine structure of the model, one needs to rely on reflection data. As a consequence, the inversion process is more prone to get stuck into local minima. It is then possible to take advantage of the cross-correlation error functional, less subject to starting models error, in order to output a suitable background model for inversion of reflection data. By combining these functionals, high-frequency data content with poor initial models can be successfully inverted. If we can find simple parameterizations for such functionals we can can reduce the amount of uncertainty and manual work related to tuning FWI. Thus FWI might become a semi-automatized imaging tool. I NTRODUCTIONFull Waveform Inversion (FWI) represents a seismic imaging method able to improve Earth structural models up to spatial resolutions beyond the limits of standard Travel Time Tomography (TTT), and more adequate for seismic imaging. TTT only inverts the time residuals of (mostly) P-wave phases picked on the recorded field traces, requiring human interaction.On the other hand, FWI processes the whole waveforms achieving a finer resolution. Nevertheless, given our surface to surface acquisition limitations, noise effects and initial models with poor Geophysics 3 low frequency content, convergence to the true model cannot be guaranteed. Among the strongest concerns when using FWI is the matching of synthetic and data phases when they are apart more than half a cycle in time, an effect known as cycle skipping (Luo and Schuster, 1991;Warner and Guasch, 2014; Metivier et al., 2016). Some functional have been developed over the last decades to cope with this issue: e.g the cross-correlation (CC) traveltime functional (Luo and Schuster, 1991), the adaptive FWI from Warner and Guasch (2014), or the optimal transport distance (Métivier et al., 2016). Although less sophisticated, the CC is able to provide good background models as reported by, e.g., Jimenez-Tejero et al. (2015), but lack in resolution. On