Search arXivSearch

arXiv · 2406.01286

Back-Propagating Rupture: Nature, Excitation, and Implications

Abstract

Recent observations show that certain rupture phase can propagate backward relative to the earlier one during a single earthquake event. Such back-propagating rupture (BPR) was not well considered by the conventional earthquake source studies and remains a mystery to the seismological community. Here we present a comprehensive analysis of BPR, by combining theoretical considerations, numerical simulations, and observational evidences. First, we argue that BPR in terms of back-propagating stress wave is an intrinsic feature during dynamic ruptures; however, its signature can be easily masked by the destructive interference behind the primary rupture front. Then, we propose an idea that perturbation to an otherwise smooth rupture process may make some phases of BPR observable. We test and verify this idea by numerically simulating rupture propagation under a variety of perturbations, including a sudden change of stress, bulk or interfacial property and fault geometry along rupture propagation path. We further cross-validate the numerical results by available observations from laboratory and natural earthquakes, and confirm that rupture "reflection" at free surface, rupture coalescence and breakage of prominent asperity are very efficient for exciting observable BPR. Based on the simulated and observed results, we classify BPR into two general types: interface wave and high-order re-rupture, depending on the stress recovery and drop before and after the arrival of BPR, respectively. Our work clarifies the nature and excitation of BPR, and can help improve the understanding of earthquake physics, the inference of fault property distribution and evolution, and the assessment of earthquake hazard.

Explore related subjects

Keep this discovery

BibTeXRIS

Xiaotian Ding, Shiqing Xu, Eiichi Fukuyama, Futoshi Yamashita. 2024-06-03. Back-Propagating Rupture: Nature, Excitation, and Implications. https://doi.org/10.1029/2024jb029629

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Holistic law of aftershocks

The paper is devoted to the phenomenological theory of aftershocks occurring in the source of a tectonic earthquake following the main shock. The theory was developed by the author jointly with A.D. Zavyalov and O.D. Zotov during the course of a long-term study of aftershocks. The theory is based on the concepts of source deactivation and the source's proper time. The holistic law governing the decay of aftershock activity over proper time follows from the theory. The damping decrement is equal to the source deactivation coefficient. The main focus of this paper is the analysis of the logical structure of the theory. The paper also contains a brief description of the experimental results obtained using the theory. Keywords: earthquake source, aftershocks, Omori's law, Utsu's law, deactivation coefficient, proper time, underground clock, foreshock convergence, aftershock divergence.

physics.geo-ph

Bayesian deep learning integration of geophysical and drilling data for 3D prediction of copper mineralization and drill targeting: a case study from the Kogodai prospect, Rudny Altai

Exploration drill targeting in structurally complex terranes is hindered by sparse sampling, heterogeneous datasets, and the ambiguity of geophysical inversions. Here, we present an uncertainty-aware 3D workflow for the acceleration of time-to-discovery in brownfield explorations and apply it to the Kogodai prospect in the Rudny Altai metallogenic province. We jointly analyse existing drilling and geophysical data in a comprehensive approach, revealing hidden patterns in already available data. Drillholes and trenches were desurveyed to a common 3D reference frame, and assays were composited to a consistent spatial support to facilitate joint modelling with geophysical inputs. We develop Bayesian deep-learning models to predict 3D fields of Cu grade together with chargeability and apparent resistivity while quantifying epistemic uncertainty via Monte Carlo sampling. The original contribution of this work is to treat the problem not as pointwise regression between co-located observations, but as joint learning of spatially continuous 3D fields from sparse, heterogeneous exploration evidence. The resulting 3D predictions delineate a principal mineralized trend and several localized candidate zones that coincide with elevated induced polarization (IP) responses, while uncertainty mapping highlights where predictions are robust versus where additional drilling would be most informative. The continuous Cu-grade field can also be thresholded to produce binary prospectivity maps, allowing the sensitivity of target delineation to the chosen cutoff to be evaluated. The outputs are intended for qualitative interpretation and risk-aware drill targeting rather than resource estimation, and we discuss key limitations arising from incomplete provenance metadata for geophysical products and heterogeneity of historical sampling.

physics.geo-ph

PyelogP: Automated Energy-Based Determination of Preconsolidation Pressure in Clay Deposits

Estimating the preconsolidation pressure ($\sigma'_p$) from one-dimensional consolidation (oedometer) tests is critical in geotechnical engineering for settlement analysis. Traditional graphical methods, such as the Casagrande procedure, may introduce uncertainties, particularly when interpreting rounded $e$-log($P$) curves typical of disturbed specimens of soft clays and silt deposits. This paper introduces PyelogP, an open-source Python library designed to calculate $\sigma'_p$ using the strain-energy method proposed by Becker et al. (1987) as an automated and reproducible alternative. The algorithm combines natural cubic spline interpolation, knee-point detection via the Kneedle algorithm, and split-point linear regression within the work-pressure space. Physically informed thresholds, including overconsolidation ratio limits and second-derivative maxima (${d^2 e}/{d(\log \sigma')^2}$), are incorporated to establish pre-yield and post-yield fitting boundaries. The performance of PyelogP is evaluated against a suite of 22 experimental consolidation datasets covering various clay deposits, including Saint-Alban clay and San Francisco Old Bay Clay. The results demonstrate strong agreement with the published values ($R^2$ = 0.912, RMSE = 0.374, MBE = -0.080), while the $O(N^2)$ algorithm requires only a few milliseconds per curve for typical oedometer datasets and less than 150 milliseconds for the largest datasets.

physics.geo-ph