Surveys in Geophysics最新文献

Deep seismic reflection (DSR) profiling is an effective technique for mapping subsurface structures. Generally, reflections in DSR data are used to constrain underground structures at the crustal scale. In addition to reflections, surface waves in DSR data can be used to investigate shallow/near-surface structures. In this study, we extracted multimodal dispersion curves and estimated their uncertainties from the DSR data in the Beijing Plain, North China, using the frequency-Bessel transform method. Compared to other surface wave surveys conducted in this area, the dispersion curves obtained from DSR data have a unique frequency band, which enables an accurate image of the structure to a depth of 200 m. The 2-D shear wave velocity model obtained by surface wave inversion is consistent with the borehole data and existing shallow/near-surface geophysical studies, which can effectively resolve the faults in the study area. Given the extensive deployment of DSR surveys worldwide and the potential of DSR surface wave analysis, we believe that the development of DSR surface wave analysis could be highly beneficial.

The gravity anomalies reflect density perturbations at different depths, which control the physical state and dynamics of the lithosphere and sub-lithospheric mantle. However, the gravity effect of the crust masks the mantle signals. In this study, we develop two frameworks (correction with density contrasts and actual densities) to calculate the gravity anomalies generated by the layered crust. We apply the proposed approaches to evaluate the global mantle gravity disturbances based on the new crustal models. Consistent patterns and an increasing linear trend of the mantle gravity disturbances with lithospheric thickness and Vs velocities at 150 km depth are obtained. Our results indicate denser lithospheric roots in most cratons and lighter materials in the oceanic mantle. Furthermore, our gravity map corresponds well to regional geological features, providing new insights into mantle structure and dynamics. Specifically, (1) reduced anomalies associated with the Superior and Rae cratons indicate more depleted roots compared with other cratons of North America. (2) Negative anomalies along the Cordillera (western North America) suggest mass deficits owing to the buoyant hot mantle. (3) Positive anomalies in the Baltic, East European, and Siberian cratons support thick, dense lithosphere with significant density heterogeneities, which could result from thermo-chemical modifications of the cratonic roots. (4) Pronounced positive anomalies correspond to stable blocks, e.g., Arabian Platform, Indian Craton, and Tarim basin, indicating a thick, dense lithosphere. (5) Low anomalies in the active tectonic units and back-arc basins suggest local mantle upwellings. (6) The cold subducting/detached plates may result in the high anomalies observed in the Zagros and Tibet.

The mission of Advanced Ionospheric Probe (AIP) onboard FORMOSAT-5 (F5) satellite is to detect pre-earthquake ionospheric anomalies (PEIAs) and observe ionospheric space weather. F5/AIP plasma quantities in the nighttime of 22:30 LT (local time) and the total electron content (TEC) of the global ionosphere map (GIM) are used to study PEIAs of an M7.3 earthquake in the Iran–Iraq border area on 12 November 2017, as well as signatures of two magnetic storms on 7 and 21–22 November 2017. Statistical analyses of the median base and one sample test are employed to find the characteristics of temporal PEIAs in GIM TEC over the Iran–Iraq area. The anomalous increases of the GIM TEC and F5/AIP ion density over the epicenter area on 3–4 November (day 9–8 before the M7.3 earthquake) agree with the temporal PEIA characteristics that the significant TEC increase frequently appears on day 14–6 before 53 M ≥ 5.5 earthquakes in the area during 1999–2016. The spatial analyses together with odds studies show that the PEIAs frequently appear specifically over the epicenter day 9–8 before the M7.3 earthquake and day 10–9 before a M6.1 earthquake on 1 December, while proponent TEC increases occur at worldwide high latitudes on the two magnetic storm days. The F5/AIP ion velocity uncovers that the PEIAs of the two earthquakes are caused by associated eastward electric fields, and the two positive storm signatures are due to the prompt penetration electric fields.

Very Long Period (VLP) signals with periods longer than 2 s may occur during eruptive or quiet phases at volcanoes of all types (shield and stratovolcanoes with calderas, as well as other stratovolcanoes) and are inherently connected to fluid movement within the plumbing system. This is supported by observations at several volcanoes that indicate a correlation between gas emissions and VLPs, as well as deformation episodes due to melt accumulation and migration that are followed by the occurrence of VLPs. Moment tensors of VLPs are usually characterized by large volumetric components of either positive or negative sign along with possibly the presence of single forces that may result from the exchange of linear momentum between the seismic source and the Earth. VLPs may occur during a variety of volcanological processes such as caldera collapse, phreatic eruptions, vulcanian eruptions, strombolian activity, and rockfalls at lava lakes. Physical mechanisms that can generate VLPs include the inflation and deflation of magma chambers and cracks, the movement of gas slugs through conduits, and the restoration of gravitational equilibrium in the plumbing system after explosive degassing or rockfalls in lava lakes. Our understanding of VLPs is expected to greatly improve in the future by the use of new instrumentation, such as Distributed Acoustic Sensing, that will provide a much denser temporal and spatial sampling of the seismic wavefield. This vast quantity of data will then require time efficient and objective processing that can be achieved through the use of machine learning algorithms.

The potential of blended seismic acquisition to improve acquisition efficiency and cut acquisition costs is still open, particularly with efficient deblending algorithms to provide accurate deblended data for subsequent processing procedures. In recent years, deep learning algorithms, particularly supervised algorithms, have drawn much attention over conventional deblending algorithms due to their ability to nonlinearly characterize seismic data and achieve more accurate deblended results. Supervised algorithms require large amounts of labeled data for training, yet accurate labels are rarely accessible in field cases. We present a self-supervised multistep deblending framework that does not require clean labels and can characterize the decreasing blending noise level quantitatively in a flexible multistep manner. To achieve this, we leverage the coherence similarity of the common shot gathers (CSGs) and the common receiver gathers (CRGs) after pseudo-deblending. The CSGs are used to construct the training data adaptively, where the raw CSGs are regarded as the label with the corresponding artificially pseudo-deblended data as the initial training input. We employ different networks to quantitatively characterize decreasing blending noise levels in multiple steps for accurate deblending with the help of a blending noise estimation–subtraction strategy. The training of one network can be efficiently initialized by transfer learning from the optimized parameters of the previous network. The optimized parameters trained on CSGs are used to deblend all CRGs of the raw pseudo-deblended data in a multistep manner. Tests on synthetic and field data validate the proposed self-supervised multistep deblending algorithm, which outperforms the multilevel blending noise strategy.

Earth/rock-fill dams and embankments are the main water retaining structures in hydraulic projects, and they can effectively resist floods and are of great significance for protecting people's lives and property. Leakage is a common problem in these structures. Investigation activities, including geotechnical, geoelectric, and tracing methods, are required to locate the leakage path and provide a basis for risk mitigation and reinforcement. These three methods provide information on different leakage characteristics, uncertainties, and spatiotemporal distributions. This work first introduces the micro-mechanism of internal erosion and then, provides a site case base for leakage investigation of earth/rock-fill dams and embankments from all over the world. For each investigation method, the basic principle, investigation process, data interpretation, and future potential are summarized. It should be emphasized that geotechnical, geoelectric, and tracing methods are placed on an equal level to assist dam managers and researchers in selecting the most appropriate method to assess dam leakage against specific geological backgrounds and structural types. Finally, the advantages, disadvantages, and applicable conditions of each investigation method are compared. The role of surface investigation methods and internal investigation methods in different stages of leakage is explained. The application of combined methods is discussed at four levels, and a new combined method is proposed.