Moving from 1-D to 3-D velocity model: automated waveform-based earthquake moment tensor inversion in the Los Angeles region

Earthquake focal mechanisms put primary control on the distribution of ground motion, and also bear on the stress state of the crust. Most routine focal mechanism catalogues still use 1-D velocity models in inversions, which may introduce large uncertainties in regions with strong lateral velocity heterogeneities. In this study, we develop an automated waveform-based inversion approach to determine the moment tensors of small-to-medium-sized earthquakes using 3-D velocity models. We apply our approach in the Los Angeles region to produce a new moment tensor catalogue with a completeness of M_L ≥ 3.5. The inversions using the Southern California Earthquake Center Community Velocity Model (3D CVM-S4.26) significantly reduces the moment tensor uncertainties, mainly owing to the accuracy of the 3-D velocity model in predicting both the phases and the amplitudes of the observed seismograms. By comparing the full moment tensor solutions obtained using 1-D and 3-D velocity models, we show that the percentages of non-double-couple components decrease dramatically with the usage of 3-D velocity model, suggesting that large fractions of non-double-couple components from 1-D inversions are artifacts caused by unmodelled 3-D velocity structures. The new catalogue also features more accurate focal depths and moment magnitudes. Our highly accurate, efficient and automatic inversion approach can be expanded in other regions, and can be easily implemented in near real-time system

Influence of MgO and Hybrid Fiber on the Bonding Strength between Reactive Powder Concrete and Old Concrete

The reactive powder concrete (RPC) was used as concrete repair material in this paper. The influence of steel fiber, steel fiber + MgO, and steel fiber + MgO + polypropylene fiber (PPF) on the mechanical properties of RPC repair materials and the splitting tensile strength between RPC and old concrete was studied. Influences of steel fiber, MgO, and PPF on the splitting tensile strength were further examined by using scanning electronic microscopy (SEM) and drying shrinkage test. Results indicated that the compressive and flexural strength was improved with the increasing of steel fiber volume fraction. However, the bonding strength showed a trend from rise to decline with the increasing of steel fiber volume fraction. Although MgO caused mechanical performance degradation of RPC, it improved bonding strength between RPC and existing concrete. The influence of PPF on the mechanical properties of RPC was not obvious, whereas it further improved bonding strength by significantly reducing the early age shrinkage of RPC. Finally, the relationship of drying shrinkage and splitting tensile strength was studied, and the equation between the splitting tensile strength relative index and logarithm of drying shrinkage was obtained by function fitting

Multifault Models of the 2019 Ridgecrest Sequence Highlight Complementary Slip and Fault Junction Instability

The 2019 Ridgecrest M_w 6.4 and M_w 7.1 earthquakes ruptured a complex fault system, posing challenges in understanding their physical processes. Modeling of the ruptures relies on fault geometries at depth, which are usually assumed based on surface traces and aftershocks. Here we use seismic and geodetic data to jointly constrain the fault geometries and slip distributions. We first represent the first‐order rupture processes with a series of subevents, then conduct slip inversions with subevent‐guided fault geometries. We find that the foreshock sequentially ruptured the NW and SW striking faults starting from their junction. The mainshock initiated at a complex three‐fault junction along the extension of the foreshock NW rupture, with major slip first occurring bilaterally near the hypocenter and then minor unilateral slip later to the southeast end. The slip distributions of the foreshock and mainshock are complementary to each other on the overlapping fault section

Multifault Models of the 2019 Ridgecrest Sequence Highlight Complementary Slip and Fault Junction Instability

The 2019 Ridgecrest M_w 6.4 and M_w 7.1 earthquakes ruptured a complex fault system, posing challenges in understanding their physical processes. Modeling of the ruptures relies on fault geometries at depth, which are usually assumed based on surface traces and aftershocks. Here we use seismic and geodetic data to jointly constrain the fault geometries and slip distributions. We first represent the first‐order rupture processes with a series of subevents, then conduct slip inversions with subevent‐guided fault geometries. We find that the foreshock sequentially ruptured the NW and SW striking faults starting from their junction. The mainshock initiated at a complex three‐fault junction along the extension of the foreshock NW rupture, with major slip first occurring bilaterally near the hypocenter and then minor unilateral slip later to the southeast end. The slip distributions of the foreshock and mainshock are complementary to each other on the overlapping fault section

Mantle Transition Zone Structure Beneath Northeast Asia From 2‐D Triplicated Waveform Modeling: Implication for a Segmented Stagnant Slab

The structure of the mantle transition zone (MTZ) in subduction zones is essential for understanding subduction dynamics in the deep mantle and its surface responses. We constructed the P (V_p) and SH velocity (V_s) structure images of the MTZ beneath Northeast Asia based on two‐dimensional (2‐D) triplicated waveform modeling. In the upper MTZ, a normal V_p but 2.5% low V_s layer compared with IASP91 are required by the triplication data. In the lower MTZ, our results show a relatively higher‐velocity layer (+2% V_p and −0.5% V_s compared to IASP91) with a thickness of ~140 km and length of ~1,200 km atop the 660‐km discontinuity. Taking this anomaly as the stagnant slab and considering the plate convergence rate of 7–10 cm/year in the western Pacific region during the late Cenozoic, we deduced that the stagnant slab has a subduction age of less than 30 Ma. This suggests that the observed stagnancy of the slab in the MTZ beneath Northeast Asia may have occurred no earlier than the Early Oligocene. From the constraints derived individually on V_p and V_s structures, high V_p/V_s ratios are obtained for the entire MTZ beneath Northeast Asia, which may imply a water‐rich and/or carbonated environment. Within the overall higher‐velocity stagnant slab, a low‐velocity anomaly was further detected, with a width of ~150 km, V_p and V_s reductions of 1% and 3% relative to IASP91. Such a gap may have provided a passage for hot deep mantle materials to penetrate through the thick slab and feed the Changbaishan volcano

Ti-MAE: Self-Supervised Masked Time Series Autoencoders

Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios. Recently, contrastive learning and Transformer-based models have achieved good performance in many long-term series forecasting tasks. However, there are still several issues in existing methods. First, the training paradigm of contrastive learning and downstream prediction tasks are inconsistent, leading to inaccurate prediction results. Second, existing Transformer-based models which resort to similar patterns in historical time series data for predicting future values generally induce severe distribution shift problems, and do not fully leverage the sequence information compared to self-supervised methods. To address these issues, we propose a novel framework named Ti-MAE, in which the input time series are assumed to follow an integrate distribution. In detail, Ti-MAE randomly masks out embedded time series data and learns an autoencoder to reconstruct them at the point-level. Ti-MAE adopts mask modeling (rather than contrastive learning) as the auxiliary task and bridges the connection between existing representation learning and generative Transformer-based methods, reducing the difference between upstream and downstream forecasting tasks while maintaining the utilization of original time series data. Experiments on several public real-world datasets demonstrate that our framework of masked autoencoding could learn strong representations directly from the raw data, yielding better performance in time series forecasting and classification tasks.Comment: 20 pages, 7 figure