Physics

The microstructural geometry (MG) of materials has a significant influence on their macroscopic response, all the more when the process is essentially microscopic as for microstructural degradation processes. However, the MG tends to be approximated by ideal spherical packings with constitutive description of the microstructural contacts. Interfaces tracking models like phase-field modeling (PFM) are promising candidates to capture the microstructures dynamics. Contact PFM (CPFM) enables to include catalyzing/inhibiting (CI) effects, accelerating/delaying equilibrium, such as temperature or the presence of certain constituents. To emphasize the influence of geometry and CI effects, we study numerically the chemo-mechanical response of digitalized geomaterials at the grain scale. An application to pressure solution creep (PSC) shows the importance of the MG and how the influence of temperature and clay can be taken into account without explicit modeling. As already inferred in previous works on PSC, the lack of MG considerations could be the reason why a unique description of PSC is missing. A simple reason could be that PSC is directly dependent on the strain concentration, which is directly dependent on the MG. This is our motivation here to investigate and suggest the influence of the MG on a degradation process like PSC.

Generalized Burridge-Knopoff (GBK) models display rich dynamics, characterized by instabilities and multiple bifurcations. GBK models consist of interconnected masses that can slide on a rough surface under friction. All masses are connected to a plate, which slowly provides energy to the system. The system displays long periods of quiescence, interrupted by fast, dynamic events (avalanches) of energy relaxation. During these events, clusters of blocks slide abruptly, simulating seismic slip and earthquake rupture. Here we propose a theory for preventing GBK avalanches, control its dynamics and incite slow-slip. We exploit the dependence of friction on pressure and use it as a backdoor for altering the dynamics of the system. We use the mathematical Theory of Control and, for the first time, we succeed in (a) stabilizing and restricting chaos in GBK models, (b) guaranteeing slow frictional dissipation and (c) tuning the GBK system toward desirable global asymptotic equilibria of lower energy. Our control approach is robust and does not require exact knowledge of the frictional behavior of the system. Finally, GBK models are known to present Self-Organized Critical (SOC) behavior. Therefore, the presented methodology shows an additional example of SOC Control (SOCC). Given that the dynamics of GBK models show many analogies with earthquakes, we expect to inspire earthquake mitigation strategies regarding anthropogenic and/or natural seismicity. In a wider perspective, our control approach could be used for improving understanding of cascade failures in complex systems in geophysics, access hidden characteristics and improve their predictability by controlling their spatio-temporal behavior in real-time.

Classic inversion methods adjust a model with a predefined number of parameters to the observed data. With transdimensional inversion algorithms such as the reversible-jump Markov Chain Monte Carlo (rjMCMC), it is possible to vary this number during the inversion and to interpret the observations in a more flexible way. Geoscience imaging applications use this behaviour to automatically adjust model resolution to the inhomogeneities of the investigated system, while keeping the model parameters on an optimal level. The rjMCMC algorithm produces an ensemble as result, a set of model realizations which together represent the posterior probability distribution of the investigated problem. The realizations are evolved via sequential updates from a randomly chosen initial solution, and converge toward the target posterior distribution of the inverse problem. Up to a point in the chain, the realizations may be strongly biased by the initial model, and have to be discarded from the final ensemble. With convergence assessment techniques, this point in the chain can be identified. Transdimensional MCMC methods produce ensembles which are not suitable for classic convergence assessment techniques because of the changes in parameter numbers. To overcome this hurdle, three solutions are introduced to convert model realizations to a common dimensionality while maintaining the statistical characteristics of the ensemble. A scalar, a vector and a matrix representation for models is presented, inferred from tomographic subsurface investigations, and three classic convergence assessment techniques are applied on them. It is shown that appropriately chosen scalar conversions of the models could retain similar statistical ensemble properties as geologic projections created by rasterization.

The Marchenko method retrieves the responses to virtual sources in the subsurface, accounting for all orders of multiples. The method is based on two integral representations for focusing and Green's functions. In discretized form these integrals are represented by finite summations over the acquisition geometry. Consequently, the method requires ideal geometries of regularly sampled and co-located sources and receivers. However, a recent study showed that this restriction can, in theory, be relaxed by deconvolving the irregularly-sampled results with certain point spread functions (PSFs).The results are then reconstructed as if they were acquired using a perfect geometry. Here, the iterative Marchenko scheme is adapted in order to include these PSFs; thus, showing how imperfect sampling can be accounted for in practical situations. Next, the new methodology is tested on a 2D numerical example. The results show clear improvement between the proposed scheme and the standard iterative scheme. By removing the requirement for perfect geometries the Marchenko method can be more widely applied to field data.

There are different kinds of intensity measures to characterize the main properties of the earthquake records. This paper proposes a simulation-based approach to compute correlation coefficients of motion duration and intensity measures of the earthquake ground motions. This method is used to investigate the influence of the ground motion data set selection in resulting duration-intensity correlation coefficients. The simulation procedure is used to tackle the problem of inadequate available ground motions with specific parameters. Correlation coefficients are investigated in three different cases. In case one, simulated ground motions differ in terms of earthquake source parameters, site characteristics, and site-to-source distances. In case two, ground motions are simulated in a specific site from probable earthquake events. In case 3, ground motions are simulated from a specific event in different sites. The first case doesnt show a significant correlation, while the second and the third case demonstrate significant positive and negative correlations, respectively.

We introduce the first fully self-consistent model combining the seismic micro-ruptures occurring within a generalized Burridge-Knopoff spring-block model with the nucleation and propagation of electric charge pulses within a coupled mechano-electrokinetic system. This model provides a general theoretical framework for modeling and analyzing geoelectric precursors to earthquakes. In particular, it can reproduce the unipolar pulses that have often been reported before large seismic events, as well as various observed anomalies in the statistical moments of the ambient electric fields and the power-law exponent transition of the power spectra of electric fields.

Understanding the principles of geophysical phenomena is an essential and challenging task. "Model-driven" approaches have supported the development of geophysics for a long time; however, such methods suffer from the curse of dimensionality and may inaccurately model the subsurface. "Data-driven" techniques may overcome these issues with increasingly available geophysical data. In this article, we review the basic concepts of and recent advances in data-driven approaches from dictionary learning to deep learning in a variety of geophysical scenarios. Explorational geophysics including data processing, inversion and interpretation will be mainly focused. Artificial intelligence applications on geoscience involving deep Earth, earthquake, water resource, atmospheric science, satellite remoe sensing and space sciences are also reviewed. We present a coding tutorial and a summary of tips for beginners and interested geophysical readers to rapidly explore deep learning. Some promising directions are provided for future research involving deep learning in geophysics, such as unsupervised learning, transfer learning, multimodal deep learning, federated learning, uncertainty estimation, and activate learning.

We consider reflection data that have been subsampled by 70% and use Point-Spread-Functions to reconstruct the original data. The subsampled, original and reconstructed reflection data are used to image the medium of interest with the Marchenko method. The image obtained using the subsampled data shows artifacts caused by internal multiples, which are eliminated when the original and reconstructed data are used.

Seismic images provided by reverse time migration can be contaminated by artefacts associated with the migration of multiples. Multiples can corrupt seismic images, producing both false positives, i.e. by focusing energy at unphysical interfaces, and false negatives, i.e. by destructively interfering with primaries. Multiple prediction / primary synthesis methods are usually designed to operate on point source gathers, and can therefore be computationally demanding when large problems are considered. A computationally attractive scheme that operates on plane-wave datasets is derived by adapting a data-driven point source gathers method, based on convolutions and cross-correlations of the reflection response with itself, to include plane-wave concepts. As a result, the presented algorithm allows fully data-driven synthesis of primary reflections associated with plane-wave source responses. Once primary plane-wave responses are estimated, they are used for multiple-free imaging via plane-wave reverse time migration. Numerical tests of increasing complexity demonstrate the potential of the proposed algorithm to produce multiple-free images from only a small number of plane-wave datasets.

Increasing aridification of continental areas due to global climate change has impacted freshwater availability, particularly in extremely dry landmasses, such as Australia. Multiple demands on water resources require integrated basin management approaches, necessitating knowledge of total water storage, and changes in water mass. Such monitoring is not practical at continental scales using traditional methods. Satellite gravity has proven successful at documenting changes in total water mass at regional scales, and here we use data from the Grace and Grace-FO missions, spanning 2002 - 2020, to track regional water budget trends in Australia most heavily utilised basin systems, including the Murray-Darling Basin. The period of analysis covers the Millennium drought (2002-2009) and 2010-11 heavy flooding events, which contribute significant signal variability. However our extended datasets demonstrate a negative trend in the geoid anomaly over the Murray-Darling Basin of -1.5mm, equivalent to a water loss rate of -0.91 Gt yr-1. With the exception of northern Australia, similar scale geoid declines are observed in most Australian basin systems analysed - implying declining total water storage. Long-term declines in water availability require concerted management plans, balancing the requirements of agriculture and industry, with domestic use, traditional owners, and healthy freshwater ecosystems.