arXiv subjects

Masayuki Kano

Publications and source records attributed to Masayuki Kano.

8 recordsLinked to original sources

arXiv2026

PINN-based short-term forecasting of fault slip evolution during the 2010 slow slip event in the Bungo Channel, Japan

Monitoring and forecasting fault slip evolution are fundamental for understanding earthquake cycles and assessing future seismic hazards. This study proposes a physics-based data assimilation framework that integrates geodetic observations with fault mechanics introducing spatial heterogeneity in frictional properties, with a particular focus on short-term fault slip forecasting. The proposed method employs physics-informed neural networks (PINNs) to calculate fault slip evolutions and to optimize the spatial distribution of frictional properties and is applied to the 2010 slow slip event beneath the Bungo Channel, southwest Japan, by changing the data period to be assimilated. When only the initial phase of slip acceleration is assimilated, a velocity-weakening frictional region is inferred beneath southwest Shikoku, corresponding to the initial nucleation are of the slow slip event. Out results demonstrate that the PINN-based data assimilation framework successfully forecasts slow transient slip even when only slip acceleration data are assimilated, whereas forecasts based on frictionally homogeneous models result in unstable fast slip. This difference can be interpreted as a consequence of introducing frictional heterogeneity, which allows both the characteristic size of the slipping region and the critical nucleation size to be variable, leading to stable slip evolution consistent with observations. When longer observation periods are assimilated, a velocity-strengthening region emerges around the slip-weakening patch, progressively restricting the direction of slip propagation. This velocity-strengthening region is interpreted as a mechanical constraint imposed by fault physics, linking the slip regions required to reproduce the observed geodetic time series. The results highlight the capability of PINN-based data assimilation incorporating geodetic observations and fault mechanics.

physics.geo-ph↗

arXiv2026

Physics-informed deep learning links geodetic data and fault friction

Fault slip modeling, based on laboratory-derived friction laws, has significantly enhanced our understanding of fault mechanics. Agreement between model predictions and observations supports the hypothesis that observed slip diversity, including fast earthquakes and slow transient slips (Slow Slip Events; SSEs), originates from frictional heterogeneity. However, quantitative assessments of frictional heterogeneity from geodetic observations while fully incorporating fault mechanics are lacking due to the difficulties of high-dimensional optimization. In this study, we aim to address this gap using Physics-Informed Neural Networks (PINNs) to link frictional heterogeneity with geodetic observations. PINNs employ a neural network to represent the spatially variable frictional properties, making their estimation feasible. Targeting the 2010 Bungo SSE in southwest Japan, our estimation reveals heterogeneous friction coinciding with localized SSE nucleation in southwest Shikoku, and subsequent westward propagation. The calculated fault slip of SSE successfully reproduces the spatio-temporal pattern of observed surface displacements. This PINN-based inversion provides a mechanically consistent fault slip model validated through quantitative comparison with observations. Furthermore, we forecast the future fault slip evolution, demonstrating the importance of assimilating observations spanning multiple SSE cycles. Our results demonstrate the potential of PINN for advancing understanding of fault mechanics and enabling physics-based fault slip forecasting.

physics.geo-ph↗

arXiv2025

Postseismicity of slow-slip doublets discerned on the outermost of the Nankai Trough subduction megathrust

Despite dissimilar slip rates, slow earthquakes are faulting as ordinary earthquakes are. It is therefore physically natural that slow earthquakes also cause postseismic motions similarly to ordinary earthquakes, even though coseismic and postseismic slips remain undifferentiated for slow earthquakes. We pursue the slow-earthquake postseismicity based on the analysis of a fault slip beneath the Bungo Channel, the westernmost region of the Nankai Trough subduction zone in southwestern Japan. Its 2010 long-term slow slip event (SSE) was mispredicted by physics-based models, which concludes that the initial acceleration of this SSE was too abrupt for a slow variant of a fault rupture. We identify that a mispredicted GNSS signal evolves logarithmically in time, preceded by minor signals that evolve exponentially, lasting about two years west and about half a year east. By performing sparse inverse modeling on the GNSS, we have estimated that exponential slips occur at the same depth, bracketing a logarithmic slip that occurs beneath the channel. The regions of exponential slips match repeating slow-slip regions, and deep tremors synchronize exclusively with the logarithmic slip. This source complexity can be explained as a neighboring rupture doublet and its afterslip and aftershocks by the known mechanics of ordinary earthquakes. If slow earthquakes have a dual origin in exponentially nucleating slow rupture and logarithmically decelerating postseismic creep, it is possible to pick the slow earthquake nuclei that could accelerate into megathrust catastrophes.

physics.geo-ph↗

arXiv2025

Three-dimensional crustal deformation analysis using physics-informed deep learning

Earthquake-related phenomena such as seismic waves and crustal deformation impact broad regions, requiring large-scale modeling with careful treatment of artificial outer boundaries. Physics-informed neural networks (PINNs) have been applied to analyze wavefront propagation, acoustic and elastic waveform propagations, and crustal deformation in semi-infinite domains. In this study, we investigated the capability of PINNs for modeling earthquake crustal deformation in 3-D structures. To improve modeling accuracy, four neural networks were constructed to represent the displacement and stress fields in two subdomains divided by a fault surface and its extension. Forward simulations exhibited high accuracy for internal deformation but yielded errors for rigid motions, underscoring the inherent difficulty in constraining static deformation at an infinite distance. In the inversion analysis, fault slip distributions were estimated using surface observational data. Application to real data from the 2008 Iwate-Miyagi inland earthquake showed a fault slip consistent with previous studies, despite underestimation of the magnitude. This study demonstrates the capability of PINNs to analyze 3-D crustal deformation, thereby offering a flexible approach for large-scale earthquake modeling using real-world observations and crustal structures.

physics.geo-ph↗

arXiv2023

Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction

The ``big'' seismic data not only acquired by seismometers but also acquired by vibrometers installed in buildings and infrastructure and accelerometers installed in smartphones will be certainly utilized for seismic research in the near future. Since it is impractical to utilize all the seismic big data in terms of the computational cost, methods which can select observation sites depending on the purpose are indispensable. We propose an observation site selection method for the accurate reconstruction of the seismic wavefield by process-driven approaches. The proposed method selects observation sites suitable for accurately estimating physical model parameters such as subsurface structures and source information to be input into a numerical simulation of the seismic wavefield. The seismic wavefield is reconstructed by the numerical simulation using the parameters estimated based on the observed signals at only observation sites selected by the proposed method. The observation site selection in the proposed method is based on the sensitivity of each observation site candidate to the physical model parameters; the matrix corresponding to the sensitivity is constructed by approximately calculating the derivatives based on the simulations, and then, observation sites are selected by evaluating the quantity of the sensitivity matrix based on the D-optimality criterion proposed in the optimal design of experiments. In the present study, physical knowledge on the sensitivity to the parameters such as seismic velocity, layer thickness, and hypocenter location was obtained by investigating the characteristics of the sensitivity matrix. Furthermore, the effectiveness of the proposed method was shown by verifying the accuracy of seismic wavefield reconstruction using the observation sites selected by the proposed method.

eess.SP↗

arXiv2022

Seismic Wavefield Reconstruction based on Compressed Sensing using Data-Driven Reduced-Order Model

A seismic wavefield reconstruction framework based on compressed sensing using the data-driven reduced-order model (ROM) is proposed and its characteristics are investigated through numerical experiments. The data-driven ROM is generated from the dataset of the wavefield using the singular value decomposition. The spatially continuous seismic wavefield is reconstructed from the sparse and discrete observation and the data-driven ROM. The observation sites used for reconstruction are effectively selected by the sensor optimization method for linear inverse problems based on a greedy algorithm. The proposed framework was applied to simulation data of theoretical waveform with the subsurface structure of the horizontally-stratified three layers. The validity of the proposed method was confirmed by the reconstruction based on the noise-free observation. Since the ROM of the wavefield is used as prior information, the reconstruction error is reduced to an approximately lower error bound of the present framework, even though the number of sensors used for reconstruction is limited and randomly selected. In addition, the reconstruction error obtained by the proposed framework is much smaller than that obtained by the Gaussian process regression. For the numerical experiment with noise-contaminated observation, the reconstructed wavefield is degraded due to the observation noise, but the reconstruction error obtained by the present framework with all available observation sites is close to a lower error bound, even though the reconstructed wavefield using the Gaussian process regression is fully collapsed. Although the reconstruction error is larger than that obtained using all observation sites, the number of observation sites used for reconstruction can be reduced while minimizing the deterioration and scatter of the reconstructed data by combining it with the sensor optimization method.

physics.geo-ph↗

arXiv2022

Adjoint-based uncertainty quantification for inhomogeneous friction on a slow-slipping fault

Long-term slow-slip events (LSSEs) usually occur on the deep, shallow parts of subducting plates and have substantial relation with adjacent megathrust fault motion. Conventional techniques of quantifying slow earthquake frictional features show that these features may be indicative of predictive seismic motion; however, quantifying high-accuracy uncertainty of the frictional fields has not yet been achieved. We therefore propose a method of uncertainty quantification for spatially inhomogeneous frictional features from slip motion on an LSSE fault--megathrust fault complex in southwestern Japan. By combining a fault motion model that mimics slow-slip motion and a variational data assimilation (DA) technique using a second-order adjoint method, we have succeeded in quantifying the spatial distribution of the uncertainty of the frictional features. Further, evaluation of the spatial distribution in high-resolution reveals the correlation between the dynamics of the slow-slip motion and the important components of the frictional features, which is valuable information for observational DA design. Findings from this study are expected to advance the theoretical foundation of applied seismic motion prediction techniques using slow-slip frictional features as stress meters for megaquakes, as well as improve understanding of the relationship between the slow-slip motion and frictional parameters of a fault.

physics.geo-ph↗

arXiv2016

Data assimilation for massive autonomous systems based on second-order adjoint method

Data assimilation (DA) is a fundamental computational technique that integrates numerical simulation models and observation data on the basis of Bayesian statistics. Originally developed for meteorology, especially weather forecasting, DA is now an accepted technique in various scientific fields. One key issue that remains controversial is the implementation of DA in massive simulation models under limited computation time and resources. In this paper, we propose an adjoint-based DA method for massive autonomous models that produces optimum estimates and their uncertainties within practical computation time and resource constraints. The uncertainties are given as several diagonal components of an inverse Hessian matrix, which is the covariance matrix of a normal distribution that approximates the target posterior probability density function in the neighborhood of the optimum. Conventional algorithms for deriving the inverse Hessian matrix require $O(CN^2+N^3)$ computations and $O(N^2)$ memory, where $N$ is the number of degrees of freedom of a given autonomous system and $C$ is the number of computations needed to simulate time series of suitable length. The proposed method using a second-order adjoint method allows us to directly evaluate the diagonal components of the inverse Hessian matrix without computing all of its components. This drastically reduces the number of computations to $O(C)$ and the amount of memory to $O(N)$ for each diagonal component. The proposed method is validated through numerical tests using a massive two-dimensional Kobayashi's phase-field model. We confirm that the proposed method correctly reproduces the parameter and initial state assumed in advance, and successfully evaluates the uncertainty of the parameter. Such information regarding uncertainty is valuable, as it can be used to optimize the design of experiments.

stat.ME↗