pure and applied geophysics最新文献

Many broadband seismic stations deployed permanently and temporarily in the Australian continent have been used for various seismological investigations in and around Australia. Although two horizontal components are generally assumed to be oriented in the north and east directions, as reported by data providers, misorientations of horizontal components from the true geographic north direction cannot be avoided in practical field observations, even at well-maintained permanent stations. In this paper, we applied a polarization analysis to almost all stations in Australia to estimate the misorientations of horizontal components using long-period teleseismic P-waves. A large data set of P-wave arrival angles allows us to successfully detect probable horizontal misorientations, including significant temporal changes in some stations, which generally coincide with reported equipment replacements included in the EarthScope (IRIS) catalog. However, we also detected some unreported temporal changes in station orientation that may result from undocumented maintenance activities, such as sensor reorientation, which are typically not reflected in metadata. Improper corrections for orientation may affect waveform-based studies for the Earth’s internal exploration, as demonstrated by teleseismic receiver function analyses, especially in the transverse component. Compiling the information on such time-dependent misorientations, we created a full catalog of horizontal-component orientations for both permanent and temporary stations in Australia, which is widely available for the community.

This study enhances thunderstorm prediction accuracy by integrating the Hallett-Mossop (HM) secondary ice production mechanism into a coupled Weather Research and Forecasting (WRF-ARW) model with an electrification module. The WRF-ARW model was configured with a 2 km convection-permitting grid and the Thompson-Eidhammer two-moment microphysics scheme, explicitly resolving deep convective updrafts over Central Russia during the 2021 convective season (May 15–August 31). The HM mechanism, active at temperatures between −8 °C and −3 °C, increases ice crystal number concentration (m⁻³), amplifying volume charge density (nC/m³) via collisional fragmentation. Validated against multi-source lightning data (WWLLN, TLN, MGO, VGI), the HM-enhanced model outperforms traditional non-inductive/inductive schemes, increasing thunderstorm detection accuracy (Probability of Detection) by 16% and reducing false alarms by 13%. Coupling HM with the Tiedtke (Monthly Weather Review 117(8): 1779–1800, 1989) convection parameterization at 6 km resolution confirms robustness, but the 2 km configuration achieves the highest skill scores (Gilbert criterion = 59%, Bagrov-Heidke criterion = 0.39). This framework advances short-term thunderstorm forecasts, with applications in aviation safety, energy infrastructure protection, and disaster preparedness.

Based on the earthquake catalog and waveform data recorded by Yunnan Seismic Networks (YSN) within one year before And after the 2018 Tonghai Ms5.0 earthquake sequence, we obtain locations of a total of 667 template events by simultaneously using double-difference earthquake locating method And waveform cross-correlation Analysis. Using the relocated events as templates, an earthquake catalog containing 2851 earthquakes within a two-year timescale in the Tonghai earthquake source area was obtained based on the Match and Location method. The number of earthquakes in the new catalog was approximately five times that of the template events and three times that of the events in the YSN catalog. We conduct analyses of the spatiotemporal characteristics of seismic activity in the Tonghai area for two catalogs. Moreover, the cascading model may dominate the nucleation process of the Ms5.0 mainshock due to the absence of aseismic slip and fluid diffusion in the source area before the mainshock. Comprehensive analyses of the aftershock distribution, decay rate, and the type of earthquake sequence inferred that the seismogenic structure of this earthquake is a relatively simple and geometrically straightforward secondary fault in the western branch of the southern segment of the Xiaojiang Fault Zone.

Inversion of controlled source electromagnetic (CSEM) data estimates a conductivity model for the subsurface and thus can directly indicate the occurrence of hydrocarbons. However, the diffusive regime of low-frequency electromagnetic fields in the subsurface compromises the resolution of the estimates. To mitigate this problem, we propose a modified steepest descent inversion strategy for CSEM data (which basically depends on the gradient of the objective function), using an image-guided gradient that carries out reliable structural information from a model of the target area. As a result, at each iteration, the estimates are updated to attain a better fit of the CSEM data with a conductivity distribution compatible with the available structural model. This structural model can be constructed, for instance, from depth-migrated seismic images of the target area. Through selected numerical experiments, we show that the proposed image-guided inversion approach can significantly improve the accuracy and resolution of resistivity estimates compared to the standard steepest descent inversion.

Seismic traveltime is crucial information of seismic waves. However, conventional numerical methods for three-dimensional traveltime simulation face significant challenges. These methods require extensive computations across the entire 3D domain, particularly for complex velocity models with dense grid points. Numerical eikonal solvers face a bigger limitation regarding efficiency for 3D scenarios when multiple simulations are needed for different sources in practical applications. To overcome these limitations, we have developed a deep learning-based approach for 3D traveltime modeling, capable of handling multiple seismic sources and diverse velocity models, named the Physics-Informed Unet Fourier Neural Operator (PIUFNO). We use velocity models and background traveltimes as inputs and perturbed traveltimes as outputs, and the eikonal equation is integrated as a loss function. PIUFNO achieves efficient and accurate traveltime modeling. The proposed approach demonstrates can not only calculate the traveltimes accurately and efficiently for velocity models used in the training domin but also be generalized to the new velocity models to obtain reasonable traveltime solutions, showcasing robust predictive capabilities.

Drought is a natural disaster that causes land and water resources to be undesirably affected from climate change-induced reductions in precipitation. Prospective water deficits may have negative effects on social, economic and political aspects of especially arid and semi-arid climate zones. Therefore, spatio-temporal characteristics of previous and future droughts should be analyzed to mitigate or reduce the negative impacts of droughts on land and water resources. In this study, the temporal and spatial characteristics of significant meteorological droughts between 1976 and 2022 in the Kızılırmak Basin, which has a semi-arid climate in Türkiye, were analyzed using the Reconnaissance Drought Index (RDI) and the Standard Precipitation Index (SPI). Drought intensity-areal extent-frequency (DIAF) curves were constructed for the basin at different recurrence periods and the spatio-temporal characteristics of chosen historical droughts were investigated in detail. The results show that the selected droughts (1984, 1994, 2001, 2008, 2013, 2017, 2020 and 2022) have a wide range of recurrence periods (from 2 to 100 years) and extreme droughts prevail in a significant part of the basin (up to 65%). Both SPI and RDI results revealed a considerable increase in drought duration and severity from 2000 to 2022. According to the findings, it was concluded that RDI and SPI provide useful information about the spatio-temporal characteristics of droughts in the basin.

This study employs a blend of satellite and reanalysis data to investigate fluctuations in Chlorophyll-a (Chl-a) concentrations in the vicinity of the Sulu Sea. The dataset covers the 25-year period from 1998 to 2022. The seasonal fluctuations in the Sulu Sea indicate that Chl-a concentrations reach their peak during the months of December, January, and February (DJF). Moreover, the primary factor influencing the interannual variations in Chl-a concentrations during the DJF is the occurrence of ENSO events. Specifically, Chl-a concentrations will increase during El Niño events and drop during La Niña events. The highest peak in Chl-a concentrations occurred during the El Niño period of 2015/2016, whilst the lowest concentrations were seen during the La Niña period of 2020/2021. In addition to the northeasterly wind, which undergoes a strengthening (weakening) pattern during El Niño (La Niña), the annual fluctuation of Chl-a concentrations is also influenced by the propagation of the Rossby wave from the central Pacific to the western Pacific. Also, we found that the mechanisms responsible for the increase in Chl-a concentrations in the Sulu archipelago and the western coast of Mindanao were distinct for each region.

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

The current research discusses the validity of utilizing Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) for the geo-hazard assessment and the detection of the subsurface voids associated with mining activities. The presence of subsurface voids poses a significant safety challenge to any operating mine due to the associated geotechnical risks and the severe consequences for human life, the environment, and mine infrastructure if left unaddressed. Traditional subsurface investigation tools in mines are borehole and probe drilling, which are costly, time-consuming, and unable to provide a comprehensive image of subsurface structures. In contrast, geophysical techniques have demonstrated through decades of research their exceptional ability to obtain a complete subsurface image, surpassing other invasive methods. In this case study, a geophysical survey was conducted at the actively operating Sukari gold mine (SGM) in Egypt utilizing a combination of GPR and ERT to detect unknown subsurface voids. Based on the joint interpretation of both techniques, risk zones were identified and delineated. Moreover, the obtained results were validated through numerical simulations and existing borehole data. This study demonstrates the efficiency of integrating geophysical techniques with geological and mining information in active mines to enhance operational safety by minimizing the risks of subsurface subsidence and failure in a timely/cost-effective way. Additionally, It represents the first published work on identifying subsurface voids in a large-scale operating gold mine in Egypt utilizing near-surface geophysical techniques.

Two of the key factors that influence the Indian summer monsoon rainfall (ISMR) are the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The study focuses on assessing the lead/lag relationship between climatic events (ENSO and IOD) and extreme precipitation, specifically at 95th (R95p) and 99th (R99p) percentiles during Positive IOD (pIOD), Negative IOD (nIOD), El Niño, La Niña, and the combination of ENSO and IOD events. The climate indices, Oceanic Niño Index (ONI) and Dipole Mode Index (DMI), have shown a lag relationship with extreme precipitation over all the homogeneous rainfall zones of India from 1955 to 2014 during the Southwest monsoon. However, the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble mean (MMEM) indicates a lead relationship in some of the regions. This disparity may be due to the underlying uncertainties associated with CMIP6 simulations of sea surface temperature (SST). The model uncertainty is found to be the major source of uncertainty in long-term SST projections, followed by scenario and ensemble uncertainties. The changes in the extreme precipitation along with the lead/lag relationships were examined under Shared Socioeconomic Pathway 585 (SSP5-8.5). In contrast to historical simulations, the correlation is observed to be high with a time lag for all climatic events. During El Niño, there is a 2-month lag between ONI and extreme precipitation, whereas a 1-month lag is observed during La Niña over the arid NW region.