Full-Wavefield, Full-Domain Deterministic Modeling of Shallow Low-Magnitude Events for Improving Regional Ground-Motion Predictions

ABSTRACT At the fundamental level, seismic risk analysis relies on good modeling tools for predicting the ground motion resulting from hypothetical earthquake events, which is traditionally approximated using many variations of ground-motion prediction equations (GMPEs). The main benefit of these equations lies in their low computational cost, allowing one to run Monte Carlo simulations in which event probabilities are dictated by regional catalogs comprising historical observations. These equations, however, rely on approximations that are only accurate in a statistical sense. In this study, we consider cases in which regional high-resolution 3D earth models are available from exploration reflection seismology. These high-fidelity velocity models allow us to perform deterministic elastic ground-motion simulations at local distances, given a prescribed synthetic earthquake event, and compare the results with those predicted by GMPEs. This full-wavefield full-domain modeling approach is significantly more costly and particularly challenging due to the slow shear-wave velocity at the near surface, which requires fine spatial and temporal discretizations. With the aid of powerful computational resources, we use an adaptive mesh generator and an efficient wave solver to model the 3D elastic and anelastic wave propagation from the hypocenter all the way to the ground surface. This approach can simultaneously account for 3D subsurface structures, near-surface site effects, topographic relief, and the radiation pattern of the source. In areas where observations are sparse, the modeling results can fill the gap between stations and provide a test bed that can be used for improving the development and accuracy of GMPEs. This approach is well suited for areas where shallow low-magnitude-induced seismic events can occur. Lastly, to demonstrate our approach, we consider an observed seismic event at the Groningen gas field and compare the recorded ground motions with both—those predicted by our approach and those predicted by GMPEs.

Efficient Black-Box Planning Using Macro-Actions with Focused Effects

The difficulty of deterministic planning increases exponentially with search-tree depth. Black-box planning presents an even greater challenge, since planners must operate without an explicit model of the domain. Heuristics can make search more efficient, but goal-aware heuristics for black-box planning usually rely on goal counting, which is often quite uninformative. In this work, we show how to overcome this limitation by discovering macro-actions that make the goal-count heuristic more accurate. Our approach searches for macro-actions with focused effects (i.e. macros that modify only a small number of state variables), which align well with the assumptions made by the goal-count heuristic. Focused macros dramatically improve black-box planning efficiency across a wide range of planning domains, sometimes beating even state-of-the-art planners with access to a full domain model.

NWP verification against own analysis by using a Data Assimilation confidence mask

<p>Numerical Model Prediction (NWP) verification against station measurements from a surface network is affected by sub-tile representativeness issues. Moreover, the station network is often not representative of the whole verification domain (e.g. usually coastal stations are predominant) and large unpopulated regions (such as oceans, Polar regions, deserts) are under-sampled. Verification against gridded analyses mitigate these issues, since they partially address the sub-tile representativeness, and sample homogeneously the verification domain. Moreover, gridded analyses merge station network measurements to radar and satellite retrieval estimates, in a physical coherent fashion, over the same NWP grid. Verification against own analysis, despite quite convenient, is however hampered by its dependence on the NWP background model, which renders the verification “incestuous”, further than being affected by the uncertainties introduced by retrieval algorithms and Data Assimilation (DA) procedures.</p><p>In this study we investigate the use of a gridded NWP own analysis for verification, by applying a mask to reduce the background model contribution. The mask weights the verification scores to account for the amounts of observations assimilated and their associated uncertainty, as estimated from DA. We illustrate the approach by using the Canadian Precipitation Analysis (CaPA), which assimilates station measurements, radar and satellite-based (IMERG) observations. The CaPA confidence (weighting) mask is dynamic and changes depending on the daily available (assimilated) observations, and on their corresponding DA error statistics; it is defined as</p><p>                                             mask = 1 - var(A-O)/var(B-O)</p><p>where A=analysis, B=Background, O=observations. Where the analysis is identical to the background model, the weighting mask is zero.</p><p>We evaluate the Canadian Regional Deterministic Prediction System (RDPS), which is the NWP system used as background model for CaPA. As expected, the verification results obtained by using the weighting mask lay between the verification results obtained verifying against the analysis over the full domain, and the results obtained verifying against station measurements. The effects of sub-tile representativeness are quantified by comparing verification results against station measurements to verification results against CaPA for the grid-points co-located with the stations. Finally, the comparison of the verification results against CaPA over the full domain versus the verification results against CaPA for the grid-points co-located with stations, estimates to which extent the station network is representative of the full domain.</p><p>The approach aims to propose a simple -yet effective- better practice for verification against own analysis.</p>

Local and parallel stabilized finite element methods based on full domain decomposition for the stationary Stokes equations

Based upon full domain decomposition, local and parallel stabilized finite element methods for the stationary Stokes equations are proposed and analysed, where the quadratic equal-order finite elements are employed for the velocity and pressure approximations, and a stabilized term based on two local Gauss integrations is used to offset the discrete pressure space to circumvent the discrete inf-sup condition. In the proposed parallel method, all of the computations are performed on the locally refined global grids that are fine around the interested subdomain and coarse elsewhere, making the method easy to implement based on a sequential solver with low communication cost. Stability and optimal error estimates of the present methods are deduced. Numerical results on examples including a problem with known analytic solution, lid-driven cavity flow, backward-facing step flow and flow around a cylinder are given to verify the theoretical predictions and demonstrate the high efficiency of the method. Results show that our parallel method can provide an approximate solution with the convergence rate of the same order as the solution computed by the standard stabilized finite element method, with a substantial reduction in computational time.

Accurate Magnetic Shell Approximations with Magnetostatic Finite Element Formulations by a Subdomain Approach

This paper presents a subproblem approach with h-conformal magnetostatic finite element formulations for treating the errors of magnetic shell approximation, by replacing volume thin regions by surfaces with interface conditions. These approximations seem to neglect the curvature effects in the vicinity of corners and edges. The process from the surface-to-volume correction problem is presented as a sequence of several subdomains, which can be composed to the full domain, including inductors and thin magnetic regions. Each step of the process will be separately performed on its own subdomain and submesh instead of solving the problem in the full domain. This allows reducing the size of matrix and time computation.

Target-oriented full-waveform inversion using Marchenko redatumed wavefields

SUMMARY Full-waveform inversion (FWI) has demonstrated increasing success in estimating medium properties, but its computational cost still poses challenges in moving towards high-resolution imaging of targets at depth. Here, we propose a target-oriented FWI method that inverts for the medium parameters confined within an arbitrary region of interest. Our method is novel in terms of both local wavefield modelling and data redatuming, in order to build a target-oriented objective function which is sensitive to the target medium only without further assumptions about the medium outside. Based on the convolution-type representation theorem, our local forward modelling operator propagates wavefields within the target medium only while providing full acoustic coupling between the target medium and the surrounding geology. A key requirement of our local FWI method is that the subsurface wavefields surrounding and inside the target be as accurate as possible. As such, the subsurface wavefields are retrieved by the Marchenko method, which can redatum the single-sided surface reflection data to the target zone while preserving both primary and multiple reflections, with minimal a priori knowledge of the full-domain medium. Given a sufficiently accurate initial velocity macromodel, our numerical examples show that our local FWI method resolves the reservoir zone of a 2-D Barrett Unconventional P-wave velocity model much more efficiently than the conventional full-domain FWI without significantly sacrificing accuracy. Our method may further enable FWI approaches to high-resolution imaging of subsurface targets.