Search for: All records

Abstract Megathrust earthquakes are the largest on Earth, capable of causing strong ground shaking and generating tsunamis. Physical models used to understand megathrust earthquake hazard are limited by existing uncertainties about material properties and governing processes in subduction zones. A key quantity in megathrust hazard assessment is the distance between the updip and downdip rupture limits. The thermal structure of a subduction zone exerts a first‐order control on the extent of rupture. We simulate temperature for profiles of the Cascadia, Nankai and Hikurangi subduction zones using a 2D coupled kinematic‐dynamic thermal model. We then build reduced‐order models (ROMs) for temperature using the interpolated Proper Orthogonal Decomposition (iPOD). The resulting ROMs are data‐driven, model agnostic, and computationally cheap to evaluate. Using the ROMs, we can efficiently investigate the sensitivity of temperature to input parameters, physical processes, and modeling choices. We find that temperature, and by extension the potential rupture extent, is most sensitive to variability in parameters that describe shear heating on the slab interface, followed by parameters controlling the thermal structure of the incoming lithosphere and coupling between the slab and the mantle. We quantify the effect of using steady‐state versus time‐dependent models, and of uncertainty in the choice of isotherm representing the downdip rupture limit. We show that variability in input parameters translates to significant differences in estimated moment magnitude. Our analysis highlights the strong effect of variability in the apparent coefficient of friction, with previously published ranges resulting in pronounced variability in estimated rupture limit depths. 

Abstract Reconstructing fault surfaces from volumetric data is a longstanding challenge in geosciences. We present a novel 3D method based on the medial axis to transform a volumetric strain‐rate invariant field from long‐term geodynamic simulations into fault surfaces. In these geodynamic models, faults correspond to regions of locally high values of the second invariant of the strain‐rate commonly referred to as shear zones. The proposed workflow begins by normalizing the strain‐rate to define fault indicator field . An iso‐surface of a chosen value is then extracted to form an envelope around the shear zones. Using the shrinking ball algorithm (Ma et al., 2012,https://doi.org/10.1007/s00371‐011‐0594‐7), we compute the medial axis of this 3D envelope to generate a point cloud representing the geometric skeleton of the shear zones. We reconstruct fault surfaces by applying Delaunay triangulation followed by Laplacian smoothing. For models involving multiple intersecting faults, we perform a local principal component analysis (PCA) of the coordinates defining the medial axis and use the resulting eigenvectors to detect first‐order orientation variations, enabling the separation and individualization of faults. We demonstrate the generality and robustness of the method by applying it several diverse 3D geodynamic scenarios: A single strike‐slip fault, a branching strike‐slip fault in a restraining bend, a dense strike‐slip fault network, a rift system, and a subduction zone with a megathrust and a conjugate thrust fault. 

Abstract Structural inversion of rifted basins is generally associated with surface uplift and denudation of the sedimentary infill, reflecting the active contractional deformation in the crust. However, worldwide examples of inverted rifts show contrasting basin-scale subsidence and widespread sedimentation patterns during basin inversion. By conducting a series of three-dimensional coupled geodynamic and surface processes models, we investigated the dynamic controls on these subsidence anomalies during the successive stages of rifting and basin inversion, and we propose a new evolutionary model for this process. Our models show that the inherited thermo-rheological properties of the lithosphere influence the initial strain localization and subsequent migration of crustal deformation during inversion. The sense of the vertical movements (i.e., uplift or subsidence), however, is not directly linked to the underlying crustal stress patterns; rather, it reflects the balance among contraction-induced tectonic uplift, postrift thermal subsidence of the inherited lithosphere, and sediment redistribution. Based on the interplay among the competing differential vertical movements with different amplitudes and wavelengths, inversion of rifted basins may lead to the growth of intraplate orogens, or the contraction-driven localized uplift may be hindered by the thermal sag effects of the inherited shallow lithosphere-asthenosphere boundary, resulting in basin-scale subsidence. In such basins, dating the first erosional surfaces and other unconformities may not provide accurate timing for the onset of inversion. 

In this paper, we consider the problem of optimizing the worst-case behavior of a partially observed system. All uncontrolled disturbances are modeled as finite-valued uncertain variables. Using the theory of cost distributions, we present a dynamic programming (DP) approach to compute a control strategy that minimizes the maximum possible total cost over a given time horizon. To improve the computational efficiency of the optimal DP, we introduce a general definition for information states and show that many information states constructed in previous research efforts are special cases of ours. Additionally, we define approximate information states and an approximate DP that can further improve computational tractability by conceding a bounded performance loss. We illustrate the utility of these results using a numerical example. 

SUMMARY To reach Earth’s surface, magma must ascend from the hot, ductile asthenosphere through cold and brittle rock in the lithosphere. It does so via fluid-filled fractures called dykes. While the continuum mechanics of ductile asthenosphere is well established, there has been little theoretical work on the cold and brittle regime where dyking and faulting occurs. Geodynamic models use plasticity to model fault-like behaviour; plasticity also shows promise for modelling dykes. Here we build on an existing model to develop a poro-viscoelastic–viscoplastic theory for two-phase flow across the lithosphere. Our theory addresses the deficiencies of previous work by incorporating (i) a hyperbolic yield surface, (ii) a plastic potential with control of dilatancy and (iii) a viscous regularization of plastic failure. We use analytical and numerical solutions to investigate the behaviour of this theory. Through idealized models and a comparison to linear elastic fracture mechanics, we demonstrate that this behaviour includes a continuum representation of dyking. Finally, we consider a model scenario reminiscent of continental rifting and demonstrate the consequences of dyke injection into the cold, upper lithosphere: a sharp reduction in the force required to rift. 

Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors, particularly on large-scale lidar data. Surprisingly, although semantic class labels naturally follow a long-tailed distribution, contemporary benchmarks focus on only a few common classes (e.g., pedestrian and car) and neglect many rare classes in-the-tail (e.g., debris and stroller). However, AVs must still detect rare classes to ensure safe operation. Moreover, semantic classes are often organized within a hierarchy, e.g., tail classes such as child and construction-worker are arguably subclasses of pedestrian. However, such hierarchical relationships are often ignored, which may lead to misleading estimates of performance and missed opportunities for algorithmic innovation. We address these challenges by formally studying the problem of Long-Tailed 3D Detection (LT3D), which evaluates on all classes, including those in-the-tail. We evaluate and innovate upon popular 3D detection codebases, such as CenterPoint and PointPillars, adapting them for LT3D. We develop hierarchical losses that promote feature sharing across common-vs-rare classes, as well as improved detection metrics that award partial credit to "reasonable" mistakes respecting the hierarchy (e.g., mistaking a child for an adult). Finally, we point out that fine-grained tail class accuracy is particularly improved via multimodal fusion of RGB images with LiDAR; simply put, small fine-grained classes are challenging to identify from sparse (lidar) geometry alone, suggesting that multimodal cues are crucial to long-tailed 3D detection. Our modifications improve accuracy by 5% AP on average for all classes, and dramatically improve AP for rare classes (e.g., stroller AP improves from 3.6 to 31.6)! Our code is available at this https URL. 

SUMMARY Physics-based simulations provide a path to overcome the lack of observational data hampering a holistic understanding of earthquake faulting and crustal deformation across the vastly varying space–time scales governing the seismic cycle. However, simulations of sequences of earthquakes and aseismic slip (SEAS) including the complex geometries and heterogeneities of the subsurface are challenging. We present a symmetric interior penalty discontinuous Galerkin (SIPG) method to perform SEAS simulations accounting for the aforementioned challenges. Due to the discontinuous nature of the approximation, the spatial discretization natively provides a means to impose boundary and interface conditions. The method accommodates 2-D and 3-D domains, is of arbitrary order, handles subelement variations in material properties and supports isoparametric elements, that is, high-order representations of the exterior boundaries, interior material interfaces and embedded faults. We provide an open-source reference implementation, Tandem, that utilizes highly efficient kernels for evaluating the SIPG linear and bilinear forms, is inherently parallel and well suited to perform high-resolution simulations on large-scale distributed memory architectures. Additional flexibility and efficiency is provided by optionally defining the displacement evaluation via a discrete Green’s function approach, exploiting advantages of both the boundary integral and volumetric methods. The optional discrete Green’s functions are evaluated once in a pre-computation stage using algorithmically optimal and scalable sparse parallel solvers and pre-conditioners. We illustrate the characteristics of the SIPG formulation via an extensive suite of verification problems (analytic, manufactured and code comparison) for elastostatic and quasi-dynamic problems. Our verification suite demonstrates that high-order convergence of the discrete solution can be achieved in space and time and highlights the benefits of using a high-order representation of the displacement, material properties and geometries. We apply Tandem to realistic demonstration models consisting of a 2-D SEAS multifault scenario on a shallowly dipping normal fault with four curved splay faults, and a 3-D intersecting multifault scenario of elastostatic instantaneous displacement of the 2019 Ridgecrest, CA, earthquake sequence. We exploit the curvilinear geometry representation in both application examples and elucidate the importance of accurate stress (or displacement gradient) representation on-fault. This study entails several methodological novelties. We derive a sharp bound on the smallest value of the SIPG penalty ensuring stability for isotropic, elastic materials; define a new flux to incorporate embedded faults in a standard SIPG scheme; employ a hybrid multilevel pre-conditioner for the discrete elasticity problem; and demonstrate that curvilinear elements are specifically beneficial for volumetric SEAS simulations. We show that our method can be applied for solving interesting geophysical problems using massively parallel computing. Finally, this is the first time a discontinuous Galerkin method is published for the numerical simulations of SEAS, opening new avenues to pursue extreme scale 3-D SEAS simulations in the future.