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The 1 January 2024 Noto-Hanto (Noto Peninsula) earthquake (M JMA 7.6) generated strong ground motion, large crustal deformation and tsunamis that caused significant damage in the region. Around Noto Peninsula, both offshore submarine and partially inland active faults have been identified by previous projects: Ministry of Land, Infrastructure, Transport and Tourism (MLIT) and Japan Sea Earthquake and Tsunami Research Project (JSPJ). We inverted the tsunami waveforms recorded on 6 wave gauges and 12 tide gauges around Sea of Japan and the GNSS data recorded at 53 stations in Noto Peninsula to estimate the slip amount and seismic moment on each of active faults. The results show that the 2024 coseismic slips were 3.5 m, 3.2 m, and 3.2 m on subfaults NT4, NT5 and NT6 of the JSPJ model, located on the northern coast of Noto Peninsula and dipping toward southeast. A smaller slip, 1.0 m, estimated on NT8 on the southwestern end of the 2024 rupture, may be attributed to its previous rupture during the 2007 Noto earthquake. The total length of these four faults is ~ 100 km, and the seismic moment is 1.90 × 10 20 Nm (Mw = 7.5). Almost no slip was estimated on the northeastern subfaults NT2 and NT3, which dip northwestward, opposite to NT4–NT5–NT6, and western subfault NT8. Aftershocks including the M JMA 6.1 event occurred in the NT2–NT3 region, but they are smaller than the potential magnitude (Mw 7.1) those faults can release in a tsunamigenic earthquake. Similar features are also found for the MLIT model; the 2024 slip was only on F43 along the northern coast of Noto Peninsula, and northeastern F42 did not rupture, leaving potential for future event. Graphical Abstract

Earthquake ruptures produce fault slip and kilometer‐wide diffuse deformation of the host rocks. However, the origin of the diffuse deformation and its role in the rupture process are debated. We produce a refined slip model for the 2019 Ridgecrest, California, earthquakes, and analyze the relations between down‐dip rupture process, and surface diffuse deformation. We show that the decrease in coseismic slip toward the ground surface, also known as shallow slip deficit (SSD), correlates with the occurrence of diffuse deformation at the surface, which is not accounted for by models assuming elastic host rocks. Hence, we suggest that a significant part of the SSD in earthquake source models could be interpreted as a proxy for shallow diffuse inelastic deformation around faults. Revisiting earthquake source models for 28 continental earthquakes, we discuss the controlling parameters of the SSD and diffuse deformation, and propose a conceptual model of the near‐field coseismic surface deformation. Plain Language Summary Earthquakes can rupture up to the ground surface, generating displacements along faults and fractures that are visible in the field. This surface deformation can also be accompanied by diffuse deformation that occurs at a wider, >0.5 km, scale around the faults, and is imperceptible from a field perspective. Here, we use numerical modeling combined with detailed surface observations for the 2019 Ridgecrest, California, earthquakes to show that reduction in fault slip near the surface correlates with the occurrence diffuse deformation at the surface. Therefore, the reduction in shallow slip suggested in earthquake rupture models could be interpreted as a proxy for non‐elastic diffuse deformation around the faults. Compiling data for worldwide earthquake ruptures, we show that this process occurs similarly for other events, and we analyze the controlling parameters. Key Points Refined slip model for the 2019 Ridgecrest earthquake sequence from the inversion of InSAR, optical, and GNSS data Shallow slip deficit is partially accounted for by diffuse deformation which likely involves bulk elastoplastic yielding A compilation of published data on strike‐slip earthquakes highlights possible controlling parameters of the SSD and diffuse deformation

The 2011 Tohoku earthquake was observed by dense strong motion, teleseismic, geodetic, and tsunami networks. We first inverted each of the datasets obtained by the networks separately, for the rupture process of the earthquake. We then performed checkerboard resolution tests for assessing the resolving power of these datasets. In order to overcome the limited resolutions of the separate inversions and differences in their results, we performed a quadruple joint inversion of all these data to determine a source model most suitable for explaining all the datasets. In the obtained source model, the maximum coseismic slip was approximately 35 m, and the total seismic moment was calculated to be 4.2 × 1022 Nm, which yielded Mw = 9.0. The main rupture propagated not only in the strike direction but also in the dip direction and included both the deep area called the Miyagi‐oki region and the compact shallow area near the Japan Trench. Key Points We made the source model reflecting all aspects of the 2011 Tohoku earthquake We performed joint inversion of seismic, geodetic, and tsunami datasets The main rupture included both deep Miyagi‐oki area and compact shallow area

Several bodies in the outer solar system are believed to host liquid water oceans underneath their icy surfaces. Knowledge of the hydrosphere properties is essential for understanding and assessing their habitability. We introduce a methodology based on Bayesian inference that enables a robust characterization of the hydrosphere through the combination of gravity and magnetic induction data. The interior models retrieved are consistent with the geophysical observations, leading to probability distributions for the relevant interior properties. We apply this joint inversion approach to constrain Europa's hydrosphere with gravity and magnetic field measurements acquired by the Galileo mission. Our results indicate that the combination of these datasets allows simultaneous constraints on the ice shell and ocean thickness, enhancing our knowledge of the hydrosphere structure. This methodology is valuable for synergistic interior science investigations of several missions in development or in planning, including Europa Clipper, JUICE and the Uranus Orbiter and Probe. Plain Language Summary The outer solar system has several moons with icy surfaces that may hide oceans of liquid water beneath them. Studying these oceans is important for assessing whether these worlds can harbor life. We have developed a novel technique to study these potentially habitable oceans by combining measurements of the gravity and magnetic fields of these icy moons. We use a statistical method to generate models of the moon's interior that are consistent with the available gravity and magnetic field measurements. By applying this method to measurements of Jupiter's moon, Europa, we show that it can provide robust estimates of the thicknesses of the moon's ice shell and the subsurface ocean. This new technique will be useful for studying the interior of Europa and other icy moons with the data acquired by future missions, such as Europa Clipper, JUICE, and the Uranus Orbiter and Probe. Key Points We developed a technique to combine measurements of gravity and magnetic field that improves the characterization of icy moon hydrospheres We applied this joint inversion to constrain Europa's ice and ocean thicknesses with gravity and magnetic induction data from Galileo Our results demonstrate that the joint inversion of these observations allows us to better understand the hydrosphere's structure

We introduce a new approach to the joint inversion of multimodal geophysical data using Gramian spaces of model parameters and Gramian constraints, computed as determinants of the corresponding Gram matrices of the multimodal model parameters and/or their attributes. We demonstrate that this new approach is a generalized technique that can be applied to the simultaneous joint inversion of any number and combination of geophysical datasets. Our approach includes as special cases those extant methods based on correlations and/or structural constraints of the multimodal model parameters. As an illustration of this new approach, we present a model study relevant to exploration under cover for iron oxide copper‐gold (IOCG) deposits, and demonstrate how joint inversion of gravity and magnetic data is able to recover alteration associated with IOCG mineralization. Key Points Introduction of gramian constraints as regularization Simultaneous joint inversion of multi‐modal geophysical data Ability to discriminate mineralogy from joint inversion of potential field data

Pre-stack seismic inversion of the P- and S-wave velocities and bulk density is important in seismic exploration for evaluating lithological units and fluid properties. Generally, this inversion is based on ray-tracing modeling, which introduces errors and requires substantial pre-processing for stratified models due to its oversimplified single-interface assumption. To overcome those problems, we propose a pre-stack inversion method, using wave-equation-based modeling as a forward engine. Most wave-equation-based pre-stack inversions are based on the reflectivity method and adopt nonlinear optimization algorithms, although accurate, but computationally expensive. Hence, we use a fast propagator matrix (PM) method valid for layered media. To improve the stability and accuracy, the PP data inversion is extended to joint PP and PS PM-based inversion (JPMI). A linear inversion scheme is adopted to reduce the computational cost, and the Fréchet derivatives are computed accordingly. Moreover, to obtain an optimal model solution, the L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) optimization algorithm and L-curve criterion, an adaptive regularization parameter acquisition method, are implemented. A posterior probability analysis shows that the method has a higher parameter sensitivity than the joint exact Zoeppritz-based inversion and gives better estimations than the single-data inversion. We discuss the effects of dataset weight, internal multi-reflections, time window setting, noise level and initial model by using model tests. Synthetic and real-data examples demonstrate that the algorithm is better than the single PP inversion in terms of stability and accuracy, especially for S-wave velocity and density estimations.

Water resources underpin human society and economic growth, yet freshwater is unevenly distributed, leaving arid regions severely water-stressed. The Beishan mining district in Inner Mongolia exemplifies this challenge: despite abundant minerals, it lacks surface water and depends almost entirely on groundwater. To improve exploration in such complex settings, we propose a Bayesian joint inversion that leverages the complementary sensitivities of Surface Nuclear Magnetic Resonance (SNMR) and Transient Electromagnetic (TEM) data within a probabilistic framework. Using a transdimensional Markov Chain Monte Carlo (MCMC) algorithm, the method adaptively balances data weighting and model complexity. Tests on synthetic and field datasets show that combining SNMR’s direct sensitivity to water content with TEM’s high-resolution resistivity imaging enhances aquifer detection across depths and enables quantitative uncertainty assessment. Applied in Beishan, the approach delineates promising aquifers, with results confirmed by drilling, offering a robust basis for groundwater exploration and sustainable management in arid regions.

Ambient noise cross‐correlations enable the extraction of multimodal surface waves, yet resolving their complex dispersion characteristics is essential for robust subsurface imaging. Using dense array data from the North China Plain, we resolve multicomponent multimodal Rayleigh and Love wave dispersion with unprecedented detail. The results reveal several distinct dispersion characteristics, including stronger excitation of higher modes on horizontal components, opposite polarity of the first higher mode on the ZR‐RZ component, and a switching of the dominant Rayleigh mode between vertical and horizontal components at low frequencies. We also identify and confirm mode kissing between fundamental and first higher Rayleigh modes and guided P modes arising from normal mode and leaky mode coupling. These dispersion characteristics are primarily controlled by the shallow low‐velocity sediments, which govern frequency‐dependent mode excitation, polarity, and energy partitioning. Integrating these multicomponent multimodal observations improves the physical interpretability and reliability of subsurface imaging.

Resolving rupture processes of great earthquakes has fundamental importance to the study of earthquake physics, rupture dynamics, fault zone structure, and evolving processes. It also plays an essential role in earthquake hazard estimation, emergency response and seismic hazard mitigation. This paper reviews the major progress of the earthquake rupture process studies in the last decades, with an emphasize on the research directions of the department geophysics of Peking University including real-time response, back-projection techniques, geodetic data analysis, joint inversion and inversion in complex earth medium. We discussed the advantages and limitations of tradition methods; proposed a systematic and integrated approach from fast-response to detailed study. We also raised perspectives of using source models for ground motion prediction and the possibility of full-dynamic inversion.

This study investigates the ground characteristics of the high altitude (3410 m a.s.l.) permafrost site Stockhorn in the Swiss Alps using a combination of surface and subsurface temperature, soil moisture, electrical resistivity and P-wave velocity time series data including a novel approach to explicitly quantify changes in ground ice content. This study was motivated by the clear signal of permafrost degradation visible in the full dataset at this long-term monitoring site within the PERMOS (Permafrost Monitoring Switzerland) network. Firstly, we assess the spatio-temporal evolution of the ground ice and water content by a combined analysis of all available in situ thermal (borehole and ground surface temperature), hydrological (soil moisture) and geophysical (geoelectric and seismic refraction) data over two decades (2002–2022) regarding the driving factors for the spatially different warming. Secondly, we explicitly quantify the volumetric water and ice content and their changes in the subsurface from 2015 to 2022 using a time-consistent petrophysical joint inversion scheme within the open-source library pyGIMLi. The petrophysical joint inversion scheme has been improved by constraining the rock content to be constant in time for six subsequent inversions to obtain consistent changes in ice and water content over the monitoring period based on jointly inverted resistivity and traveltime data. All different data show a warming trend of the permafrost. The ice content modeled from the petrophysical joint inversion has decreased by about 15 vol.% between 2015 and 2022. Changes in ice content are first observed in the lower, south-facing part of the profile. As a result, resistivity and P-wave velocity have been decreasing significantly. Permafrost temperatures measured in the boreholes have increased between 0.5 °C and 1 °C in 20 years. Our study shows the high value of joint and quantitative analysis of datasets comprising complementary subsurface variables for long-term permafrost monitoring.