Feed aggregator

A remotely controlled research vessel has gathered some of the first comprehensive measurements from within the massive crater left by the Hunga volcano (formerly known as Hunga Tonga-Hunga Ha'apai) after it erupted two years ago.

The U.S. Geological Survey has released a new nationwide landslide susceptibility map that indicates nearly 44% of the U.S. could potentially experience landslide activity. The new assessment provides a highly detailed, county-by-county picture of where these damaging, disruptive and potentially deadly geologic hazards are more likely as well as areas where landslide hazards are negligible.

Abstract

Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)-32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision-making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short-Term Average/Long-Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long-term, real-time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

Abstract

Recently, more advanced synchronous global-scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO-2) solar-induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the predictive understanding of both water and carbon cycles in land surface modeling. The Simplified Simple Biosphere Model version 4 (SSiB4) was coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID) and a mechanistic representation of SIF. Incorporating dynamic vegetation processes reduced global SIF root-mean-squared error (RMSE) by 12%. Offline experiments were conducted to understand the water and carbon cycles and their interactions using satellite data as constraints. Results indicate that soil hydraulic properties, the soil hydraulic conductivity at saturation (Ks) and the water retention curve, significantly impact soil moisture and SIF simulation, especially in the semi-arid regions. The wilting point and maximum Rubisco carboxylation rate (Vmax) affect photosynthesis and transpiration, then soil moisture. However, without atmospheric feedback processes, their effects on soil moisture are undermined due to the compensation between soil evaporation and transpiration. With optimized parameters based on SMAP L3 and OCO-2 data, the global RMSE of soil moisture and SIF simulations decreased by 15% and 12%, respectively. These findings highlight the importance of integrating advanced satellite data and dynamic vegetation processes to improve land surface models, enhancing understanding of terrestrial water and carbon cycles.

Abstract

Meteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land-atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under-sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under-sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station.

Abstract

The magnetotail plasma sheet boundary layer (PSBL) is a dynamic boundary layer between the hot-denser plasma sheet and the cold-tenuous tail lobes. It plays an important role in exchanging mass and energy in the magnetotail. In previous studies, the local current carried by the electron beams has been well understood. The strong energy conversion (E ⋅ J, E is electric field and J is current density), however, is barely reported at the PSBL. Here, using magnetospheric multiscale mission, we find a strong dawn-dusk current with a magnitude of 80 nA/m2 at the magnetotail PSBL. The strong current appears during crossing the PSBL and is primarily contributed by the perpendicular electron currents. Cooperating with an intense fluctuating electric field (reaching ∼40 mV/m) carried by the lower-hybrid drift waves, this dawn-dusk current leads to a strong energy conversion with a magnitude larger than 3 nW/m3. This study enhances the understanding of local energy-conversion processes at the PSBL.

Abstract

Ambient noise interferometry has become a common technique for monitoring slight changes in seismic velocity in a variety of contexts. However, the physical origin of the resolved small velocity fluctuations is not well established for long-term seasonal effects. Here we propose a physical forward model of scattered waves in a deformable medium that includes acousto-elastic effect, which refers to non-linear elasticity with third-order elastic constants. The model shows that small pressure perturbations of a few kPa due to seasonal variations in the water table can induce seismic velocity changes compatible with those measured at the surface by ambient noise interferometry. The results are consistent with field observations near the deep geothermal site of Rittershoffen (France). They illustrate the capability in modeling the diffuse wavefield from scattering synthetic waves to reproduce ambient noise signals for monitoring environmental and/or deep reservoir signals.

Abstract

Detection of earthquake precursors has long been a controversial issue with regard to its possibility and realizability. Here we present the detection of electromagnetic anomalous signals before large earthquakes using an observation network of very high frequency radio wave receivers close to major tectonic lines in Japan. The receivers are equipped with specifically designed narrowband filters to suppress noises and to detect extremely weak signals. We detected different types of electromagnetic anomalies before earthquakes around mountainous and coastal regions, where presence of electric charges is anticipated on the surface located in the middle of the radio wave paths near major tectonic lines in Japan. We use numerical electromagnetic wave analysis to show that when electric charges are present on a ground surface as a consequence of tectonic activity, the surface charges interact strongly with radio waves and eventually cause strong diffraction of the radio waves. The analysis was performed using the three-dimensional finite-difference time-domain method with digital elevation models of the actual geographical landforms on a massively parallel supercomputer. The results confirm the consistent mechanisms of the electromagnetic precursors, which explains the anomalous electromagnetic signals observed by the authors before large earthquakes.

Author(s): C. A. McAnespie, P. Chaudhary, L. Calvin, M. J. V. Streeter, G. Nersysian, S. J. McMahon, K. M. Prise, and G. Sarri

We report on the first systematic characterization of a tuneable laser-driven electron source capable of delivering Gy-scale doses in a duration of 10–20 ps in a single irradiation, thus reaching unprecedented dose rates in the range of 1010–1012 Gy/s. Detailed characterization of the source indicat…

[Phys. Rev. E 110, 035204] Published Thu Sep 12, 2024

Author(s): H. Martin, R. W. Paddock, M. W. von der Leyen, V. Eliseev, R. T. Ruskov, R. Timmis, J. J. Lee, A. James, and P. A. Norreys

Dense, hot plasmas are susceptible to the electrothermal instability: a collisional process which permits temperature perturbations in electron currents to grow. It is shown here that linearizing a system comprised of two opposing currents and a mobile ion background as three distinct fluids yields …

[Phys. Rev. E 110, 035205] Published Thu Sep 12, 2024

Ozone gas is reducing the growth of tropical forests—leaving an estimated 290 million tonnes of carbon uncaptured each year, new research shows.

A research team led by the Leibniz Institute for Baltic Sea Research Warnemünde (IOW) has analyzed 50,000 years of mid-latitude hydroclimate of the South-East Pacific using special moisture related indicators in marine sediment cores. They have found that natural variations in the Earth's orbital parameters exert a decisive influence.

Abstract

In this contribution, we introduce the ambiguity-resolved (AR) detector and study its distributional characteristics. The AR-detector is a new detector that lies in between the commonly used ambiguity-float (AF) and ambiguity-known (AK) detectors. As the ambiguity vector can seldomly be known completely, usage of the AK-detector is questionable as reliance on its distributional properties will then generally be incorrect. The AR-detector resolves the shortcomings of the AK-detector by treating the ambiguities as unknown integers. We show how the detector improves upon the AF-detector, and we demonstrate that the, for ambiguity-resolved parameter estimation, commonly required extreme success rates can be relaxed for detection, thus showing that improved model validation is also possible with smaller success rates. As such, the AR-detector is designed to work for mixed-integer GNSS models.

Nature Geoscience, Published online: 12 September 2024; doi:10.1038/s41561-024-01530-1

Anthropogenic ground-level ozone substantially reduces the productivity of tropical forests and so their carbon drawdown, according to ozone susceptibility experiments and dynamic global vegetation modelling.

SummaryJoint inversion, such as the combination of receiver function and surface wave dispersion, can significantly improve subsurface imaging by exploiting their complementary sensitivities. Bayesian methods have been demonstrated to be effective in this field. However, there are practical challenges associated with this approach. Notably, most Bayesian methods, such as the Markov Chain Monte Carlo (MCMC) method, are computationally intensive. Additionally, accurately determining the data noise across different data sets to ensure effective inversion is often a complex task. This study explores the unscented Kalman inversion (UKI) as a potential alternative. Through a data-driven approach to adjust estimated noise levels, we can achieve a balance between actual noise and the weights assigned to different data sets, enhancing the effectiveness of the inversion process. Synthetic tests of joint inversion of receiver function and surface wave dispersions indicate that the UKI can provide robust solutions across a range of data noise levels. Furthermore, we apply the UKI to real data from seismic arrays in Pamir and evaluate the accuracy of the joint inversion through posterior Gaussian distribution. Our results demonstrate that the UKI presents a promising supplement to conventional Bayesian methods in the joint inversion of geophysical data sets with superior computational efficiency.

A numerical model has been elaborated to calculate ULF electromagnetic fields in the ground-atmosphere–ionosphere system created by an underground horizontal current source of a finite length. The modeling has...

Optically stimulated luminescence (OSL) dating utilises the detection of trapped charge in minerals, and has an ultralow closure temperature. There is the potential for direct dating of fault movement using fa...

University of Minnesota researchers have developed a new tool to measure ethane from space, leading to a better understanding of fossil fuel emissions worldwide. Ethane is commonly found in natural gas and is primarily used in plastics manufacturing.

Abstract

To investigate the combined impacts of anthropogenic and biogenic emissions on the formation of secondary organic aerosols (SOA), SOA molecular tracers, their corresponding volatile organic compound precursors, and other air pollutants were measured online during the winter and summer seasons of 2022 in an industrial city, Zibo, China. The results indicate that the average concentrations of SOA tracers were 16.1 ± 9.8 ng m−3 in winter and 99.4 ± 57.2 ng m−3 in summer. During winter, anthropogenic SOA (ASOA, the sum of SOA derived from naphthalene and mono-aromatic volatile organic compounds) dominated, whereas isoprene SOA (SOAI) prevailed in summer. Correlation analysis between SO4 2− and both SOAI and high-order monoterpene SOA tracers (SOAM-H) (R = 0.46–0.72, p < 0.001) revealed that higher aerosol acidity facilitated the formation of SOAI and SOAM-H, with SO2 emissions playing a significant role in leading to higher acidity. Most biogenic SOA (BSOA) tracers exhibited a significant positive correlation with NO3 −, particularly in winter, implying the remarkable influence of NO x emissions on BSOA formation. The levels of BSOA tracers increased with NH3, indicating that NH3 can enhance the formation of BSOA. In summer, SOA formation correlated with O x (O x  = O3 + NO2), indicating the substantial impact of atmospheric oxidizing capacity on SOA formation. During winter, aerosol liquid water content (ALWC) correlated well with SOAI tracers (i.e., 3-hydroxyglutaric acid (3-HGA) and 3-hydroxy-4,4-dimethylglutaric acid (3-HDMGA)), and 2,3-dihydroxy-4-oxopentanoic acid (DHOPA) (R > 0.5, p < 0.001), indicating the important contribution of aqueous-phase formation of SOA. These findings underscore the significant role of anthropogenic pollutant emissions in the formation of ASOA and BSOA in urban environments.

Abstract

The plasmasphere accounts for the majority of the mass of Earth's magnetosphere and contains most of the cold ion (1 eV) population. The plasmasphere is extremely dynamic, undergoing a constant cycle of erosion and refilling. In this paper we perform a statistical study of erosion and refilling rates using 6 years of data from the Van Allen Probes from the beginning of 2013 through the end of 2018. Using in-situ density measurements derived from the upper hybrid resonance line, we create global maps of the erosion and refilling rates over a wide range of L shells and local times. Sorting the data by L shell, magnetic local time, and distance to the plasmapause, we characterize the absolute and relative rates of erosion and refilling during a variety of geomagnetic conditions. We also examine three case studies of geomagnetic storms and compare their density evolutions during the recovery period. Our results are in agreement with refilling rates found by previous statistical studies using different methods, but somewhat lower than many of the case studies reported. We find median erosion rates of 164, 83, and 43 cm−3/day and refilling rates of 87, 42, and 27 cm−3/day at L = 3, 4 and 5, respectively when Kp ≤ ${\le} $ 3. We also find little local time dependence for both erosion and refilling rates.