Surveys in Geophysics最新文献

A 6-year cycle has long been recognized to influence the Earth’s rotation, the internal magnetic field and motions in the fluid Earth’s core. Recent observations have revealed that a 6-year cycle also affects the angular momentum of the atmosphere and several climatic parameters, including global mean sea level rise, precipitation, land hydrology, Arctic surface temperature, ocean heat content and natural climate modes. In this review, we first present observational evidences supporting the existence of a 6-year cycle in the Earth system, from its deep interior to the climate system. We then explore potential links between the Earth’s core, mantle and atmosphere that might explain the observations, and investigate various mechanisms that could drive the observed 6-year oscillation throughout the whole Earth system.

The risk of earthquakes and their effects on both nature and infrastructure in seismically active regions of India require adaptable and scalable earthquake early warning (EEW) systems. Developing a robust EEW system is crucial to mitigate earthquake risks in the region, but it is a challenging task. Various institutes have attempted to develop EEW systems using different methods. Still, there is no common consensus, and issues remain with response time and reliability of disseminated information to the public. Efforts by institutions like the Indian Institute of Technology, Roorkee, have advanced EEW technologies, focusing on dense seismic sensor networks, real-time data processing algorithms, and effective dissemination mechanisms. Recent initiatives aim to improve sensor sensitivity and accuracy through fast communication systems for quicker earthquake detection. However, challenges persist in making EEW accessible and affordable, particularly in remote areas, due to the lack of a nationwide system. The National Centre for Seismology (NCS), under the Ministry of Earth Sciences (MoES), is piloting an EEW system in the NW Himalayas, which could lead to a nationwide implementation. Developing region-specific algorithms for rapid data analysis and nurturing collaboration between academic institutions, government agencies, and international partners are crucial steps. Public awareness campaigns and educational programs are essential for community resilience and timely response to earthquake alerts. Establishing a robust EEW system in India could significantly enhance earthquake risk mitigation efforts in earthquake-prone zones of the country and should be viewed within the context of a holistic risk reduction framework. EEW systems can enhance mitigation efforts, but they must be complemented by other essential measures, such as improving building resilience and promoting public awareness.

Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised features. However, their accuracy and training cost restrict their applicability. To address these issues, we propose a novel initialization for PINNs based on meta-learning to enhance their performance. In our framework, we first utilize meta-learning to train a common network initialization for a distribution of medium parameters (i.e., velocity models). This phase employs a unique training data container, comprising a support set and a query set. We use a dual-loop approach, optimizing network parameters through a bidirectional gradient update from the support set to the query set. Following this, we use the meta-trained PINN model as the initial model for a regular PINN training for a new velocity model, where the optimization of the network is jointly constrained by the physical and regularization losses. Numerical results demonstrate that, compared to the vanilla PINN with random initialization, our method achieves a much faster convergence speed, and also obtains a significant improvement in the results accuracy. Meanwhile, we showcase that our method can be integrated with existing optimal techniques to further enhance its performance.

In this paper, we review the theoretical studies of the electromagnetic and other non-seismic phenomena accompanying earthquakes. This field of geophysical research is at the interception of several sciences: electrodynamics, solid-state physics, fracture mechanics, seismology, acoustic-gravity waves, magnetohydrodynamics, ionospheric plasma, etc. In order to make physics of these phenomena as transparent as possible, we use a simplified way of deriving some theoretical results and restrict our analysis to order-of-magnitude estimates. The main emphasis is on those theoretical models which give not only a qualitative, but also a quantitative, description of the observed phenomena. After some introductory material, the review is begun with an analysis of the causes of local changes in the rock conductivity occasionally observed before earthquake occurrence. The mechanisms of electrical conductivity in dry and wet rocks, including the electrokinetic effect, are discussed here. In the next section, the theories explaining the generation of low-frequency electromagnetic perturbations resulting from the rock fracture are covered. Two possible mechanisms of the coseismic electromagnetic response to the propagation of seismic waves are studied theoretically. Hereafter, we deal with atmospheric phenomena, which can be related to seismic events. Here we discuss models describing the effect of pre-seismic changes in radon activity on atmospheric conductivity and examine hypotheses explaining abnormal changes in the atmospheric electric field and in infrared radiation from the Earth, which are occasionally observed on Earth and from space over seismically active regions. In the next section, we review several physical mechanisms of ionospheric perturbations associated with seismic activity. Among them are acoustic-gravity waves resulting from the propagation of seismic waves and tsunamis and ionospheric perturbations caused by vertical acoustic resonance in the atmosphere. In the remainder of this paper, we discuss whether variations in radon activity and vertical seismogenic currents in the atmosphere can affect the ionosphere.

Previous surveys using the remote sensing (RS) method revealed significant structures in the area of the Western Carpathians. It has not yet been possible to verify and explain the results of these surveys, even though all the phenomena are regional in nature and show many morphological features that clearly indicate recent activity and deformations, including current earthquake foci. The aim of the article was to verify these phenomena and compare them with new findings. A method of combining geomorphological data with satellite image analysis and verification using Global Navigation Satellite Systems (GNSS) and geophysics data was used. In this work, results are presented confirming the existence of a previously identified nonlinear structure—the "gravity nappe" in the western part of the Low Tatras, and the largest tectonic system Muráň—Malcov is analyzed in detail. Similar structures and tectonic zones, on a smaller scale, can also be found in other areas of the Carpathians. For example, the gravity structure in the Lesser Carpathians and the Ukrainian flysch Carpathians or the linear boundaries interpreted as tectonic systems—the Myjava-Subtatrans, Hron and Transgemerian tectonic zones. Recent movement trends have been confirmed by newly unified data from EUREF Permanent Network (EPN) stations and GNSS campaigns carried out in the last two decades in the given area. Both types of analyzed structures are directly related to the occurring foci of earthquakes.

Gravity forward modeling provides important high-resolution information for the development of global gravity models, and can also be applied in many studies, e.g., topographic/isostatic effects computation and Bouguer anomaly maps compilation. In this paper, we present efficient spectral forward modeling approaches in the spheroidal harmonic domain, based on a single layer with constant density or volumetric layers with laterally varying density. With the binomial series expansion applied in spheroidal harmonic gravity forward modeling, the computational cost of these approaches is much lower than similar approaches. In both layering cases, we derive topographic potential models up to degree and order (d/o) 2190 by applying the approaches proposed here. Our methodology is evaluated by comparing these outcome models with other similar topographic potential models derived from spherical harmonic solutions. We find that topographic potentials from spheroidal and spherical harmonic approaches are in great agreement. Finally, the model named EHFM_Earth_7200 with a maximum degree of 7200 was derived by a layer-based approach. The evaluations by ground-truth data show that EHFM_Earth_7200 improves GO_CONS_GCF_2_DIR_R6 by 4% over Antarctica, and improves EGM2008 by ~ 34% over northern Canada. A global map of Bouguer gravity anomaly was also compiled with EHFM_Earth_7200 and EGM2008. As the main conclusion of this work, the new model EHFM_Earth_7200 is beneficial for investigating and modeling the Earth’s external gravity field, the new approaches have comparable accuracy to spherical harmonic approaches and are more suitable for practical use with guaranteed convergence regions because they are performed in the spheroidal harmonic domain.

Seismic and electromagnetic (EM) imaging are essential tools for characterizing velocity and conductivity. However, the separate inversion of seismic and EM data is challenging due to the noisy measurements, inadequate data collection, and reliance on prior information, consequently resulting in uncertainty and ambiguity of the solutions. Moreover, the two methods are different in sensitivity and spatial resolution, making it difficult to discover consistencies in the inverted models. Joint inversion of seismic and EM data takes advantage of both methods and significantly improves the imaging capability of subsurface structures. In this paper, we review various coupling strategies for the joint inversion of seismic and EM data and highlight the application advances from 1-D to 3-D inversion. Specifically, we investigate the integration of machine learning techniques to tackle ill-posed inverse problems and showcase their effectiveness in coupling. Following this, we construct a deep-learning-based joint inversion workflow and provide a synthetic test to demonstrate its superiority by applying an attention mechanism, which enhances the model’s capability to focus on specific features within the data. This study proves the potential of integrating artificial intelligence into joint inversion and understanding the deep Earth interior by incorporating multiple geophysical data.