Earth and Space Science最新文献

The current study explores the relationship between solar variability and tropical cyclone (TC) activity using sunspot number (SSN) and TC best-track data as respective proxies. We have considered six regions of the globe, for example, EP: Eastern Pacific, NA: North Atlantic, NI: North Indian, SI: South Indian, SP: South Pacific, and WP: Western Pacific. The results show strong anti-correlation between yearly TC activity and yearly SSN while considering their 11-year moving averages. This behavior is consistent for TC counts as well as accumulated cyclone energy. However, this is true only for the North Atlantic region. Overall, when we consider all regions together, more TCs (in terms of counts) are observed during lower solar activity periods (SSN < 50) as compared to higher solar activity conditions (SSN > 100). However, the yearly rates remain more or less similar. On the other hand, extreme TC events with a maximum wind speed of 137 knots and higher (category 5) are most likely to occur during the declining phase of a solar cycle and least likely to occur during the ascending phase or the maximum phase. Although solar activity levels are similar during the declining and ascending phases, the yearly occurrence rate is nearly double in the declining phase (1.123) as compared to that in the ascending phase (0.625).

The 2020 Alex storm in southern France led to localized extreme rainfall exceeding 600 mm in less than 24 hr. In the 100 days following the storm, a series of small earthquakes swarm occurred beneath the Tinée valley, a region characterized by a low background deformation. To gain insight into the mechanisms controlling swarm evolution, we used an enhanced seismic catalog to detect 188 events. These events exhibited magnitudes comprised between −1.03 and 2.01, and 78 of them were relocated using relative locations at an average depth of 3–4 km. Additionally, we estimated the directions and velocities of seismicity migration. Our analyses reveal multiple episodes of hypocenter expansion and migration within a fluid-saturated fault system. Observations provide evidence of a bi-directional seismicity migration marked by dual velocities within a swarm. The northward seismicity migration aligns with velocities indicative of aseismic slip (∼130 m/hr), while the southward migration corresponds to velocities associated with fluid pressure diffusion (∼5 m/hr). This migration pattern underscores the interplay of multiple physical mechanisms in both triggering and driving earthquakes. A stress-driven model based on rate-and-state friction successfully explains the overall evolution of observed seismicity, whereas a fluid-driven model fails to reproduce the data. Our observations and models suggest that fluid pressure changes resulting from intense rainfall caused aseismic slip in the shallow portion of the crust. We hypothesize that aseismic deformation serves as the driving force for the earthquake swarms, coupled with the invasion of pressurized fluid due to diffusing rainfall.

Inversion of magnetic basement interfaces in basins is essential for interpreting potential field data and studying geothermal resource distribution, as well as basin formation and evolution. This paper introduces a novel method for inverting magnetic basement interfaces using a random forest regression (RFR) algorithm that combines potential field processing and machine learning techniques. The method creates magnetic base interface models and corresponding magnetic anomaly data through the random midpoint displacement method and magnetic interface finite element forward simulation. These anomalies are then processed using techniques such as directional transformations, analytical continuation, spatial derivatives, and fractional transformations. Feature attributes are extracted, and Gini importance is utilized to measure the contributions of feature factors, identify effective features, and enhance algorithm efficiency. The validity and practicality of the method are demonstrated through the analysis of both idealized and noisy models. The proposed machine learning-based approach is more intelligent, efficient, and accurately represents the relief of magnetic base interfaces. When applied to magnetic survey data in the Datong Basin, it produced a reliable basin base model that aligns with known structural information, paving the way for further research in magnetic interface inversion.

Seismic inversion holds significant importance across various domains of geoscience and engineering, including the characterization of energy resource reservoirs, the assessment of polluted sites, and CO₂ storage. It is a process of estimating rock properties from seismic data that is inherently uncertain, nonlinear, non-unique, and highly challenging. Using multiple seismic attributes increases the size of the data, requiring considerable processing resources and time. However, deep learning can accurately fit quantities of nonlinear variables, making it an excellent method for predicting spatially distributed subsurface properties. We trained some multi-output regression neural networks to carry out porosity inversion from seismic data. We initially computed a series of seismic attributes and generated the corresponding porosity using interpreted horizons, well logs, and seismic data. Subsequently, we proposed a technique to identify the most relevant seismic attributes for porosity inversion. Because our networks work as stochastic modeling entities, we created a weight-averaging ensemble approach to build a strong model with the highest level of accuracy. We combined realizations from baseline entities, considering their respective performance levels. Using the statistics between these realizations and the robust model, we determined the degree of uncertainty associated with the outcome. We found an R² of 0.993 and an MAE of 0.00112 in the F3 block offshore the Netherlands, proving the method's effectiveness. The mean porosity was 0.175193, compared to 0.175626 from a reference model, and the mean uncertainty was ±0.0008998.

The impact of global warming on lightning flash rates remains relatively unknown. In this study, the South China Sea (SCS) and the surrounding areas within Southeast Asia were selected to examine the long-term trend and future projection of lightning activity based on the currently longest satellite-based lightning data set available and climate models. Our study revealed a reduction in the observed lightning flash rates around the SCS, with a linear trend of −0.11 fl km⁻² yr⁻² during 1996–2013. In contrast, the precipitation around the SCS exhibited an increasing trend and was negatively correlated with the local lightning flash rate. The sea surface temperature gradient over equatorial Pacific Ocean, latent heat flux over the equatorial Indian Ocean, local convective available potential energy, precipitation and aerosol changes collectively accounted for 82% of the variance in the lightning fluctuations over the SCS and Southeast Asia. Multiple linear regression proxies of lightning flash rates were constructed and applied to the climate models. The models indicated that lightning activity around the SCS is projected to intensify by 10% and 12% by the end of the 21st century under SSP245 and SSP370, respectively.

We use the Godano et al. (2022, https://doi.org/10.1029/2021ea002205) method for evaluating the b maps of the faults associated with the largest earthquakes M ≥ 7.0 that occurred in California. The method allows an independent evaluation of the b parameter, avoiding the overlap of the cells and the omission of some earthquakes, while keeping all the available information in the catalog. We analyzed four large earthquakes: Landers, Hector Mine, Baja California, and Searles Valley. The maps obtained confirm that the b value can be considered as a strain meter and allow us to elucidate the presence of barriers, such as obstacles to the propagation of the fracture, on the fault of the analyzed earthquakes. A further estimated parameter is the time window during which aftershocks occur in the cell, Δt. This quantity is very useful for a better definition of the aftershock generation mechanism. It reveals where the stress is released in a short time interval and how the complexity of the faulting process controls the occurrence of aftershocks on the fault, and also the duration of the entire sequence.

Despite significant recent technological advances, oceanographic observations on horizontal scales of meters to a few kilometres prove challenging. Exploiting legacy seafloor cables presents a disruptive prospect to address this gap, as it may provide low-cost sustained observations with high space-time resolution, enabled through novel opto-electronic interrogation of optical fibers within the cables. Here, we demonstrate this approach in a renewable tidal energy cable embedded within a region with a strong barotropic tide. By making remote measurements continuously over 12 hr, we obtain the distributed differential strain experienced by 2 km of offshore cable from a diverse range of oceanic flow processes, with an along-cable resolution of 2.04 m. We successfully identify: (a) nearshore wave breaking and its modulation by changes in water depth; (b) along-cable tidal velocity, shown to be linearly related to the differential strain; and (c) high-frequency motions consistent with 3-dimensional turbulent processes, either of natural origin or from flow-cable interaction. These inferences are supported by nearby conventional measurements of water depth and velocity.

The Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual-frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis-derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high-latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user-friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation.

The dynamical and microphysical aspects of two different precipitating systems have been investigated using the ARIES Stratosphere-Troposphere Radar (ASTRad) facility and further substantiated by Weather Research Forecasting (WRF) model over Manora Peak. The first event (Case-I) is associated with the southwest Indian summer monsoon that occurred on 4 August 2020, with a vertical extension of 10–12 km and leads to liquid phase precipitation. The second event (Case-II) linked to the winter western disturbance occurred on 5 February 2021. This precipitating system was developed with a vertical extension of 6–7 km, resulting in both liquid and solid phase precipitation. Such distinct vertical extension of the systems is found to be associated with the thermodynamical conditions and prevailed large-scale circulations. By analyzing the vertical structure of these systems using three Doppler moments estimated from the ASTRad (equivalent Reflectivity dBZe, Doppler velocity, and Spectral width), maximum dBZe (∼60 dB) is observed in Case-II, while higher spectral width (>2 m s⁻¹) is associated to Case-I. The microphysical processes assessed by the WRF model pointed out that Case-I involved snow accretion on supercooled droplets, leading to graupel and raindrop formation, while in Case–II, solid and liquid precipitation resulted from ice processes, including accretion or autoconversion. These findings highlight the significance of integrating radar and modeling data to understand the dynamical and microphysical evolution of precipitation under the influence of orography in the Himalayan region.

We report new PKS, SKS, and SKKS splitting measurements for 88 seismic stations in Namibia, Botswana, South Africa, and Mozambique. When combined with measurements from previous studies, the ensemble of measurements shows a fairly uniform NNE to NE (∼41° on average) fast-polarization direction (ϕ) and delay time (δt) (∼0.7 s on average) across the entire southern African subcontinent. It is difficult to attribute the NNE-NE ϕ direction to just one source of anisotropy either within the lithospheric or sublithospheric mantle. We instead propose the observed anisotropy pattern could result from a combination of several sources that together give rise to a pervasive NNE-NE ϕ direction; (a) fossil anisotropy in the lithospheric mantle resulting from the Neoproterozoic collision of the Congo and Kalahari cratons to form the Damara Belt, (b) movement of the African plate over the asthenosphere, and (c) flow in the upper mantle induced by the African Superplume. In addition, a contribution from anisotropy in the lowermost mantle in the vicinity of the African large low shear velocity province cannot be ruled out.