Biomass burning, including the combustion of wood, charcoal and agricultural residues, is a major source of PM2.5, a fine particulate matter that degrades air quality and poses risks to human health. Much of this pollution is tracked by looking at levels of levoglucosan, a chemical that is formed when cellulose in plants is burned, from biomass combustion such as residential fuel use, cooking and open burning.
Researchers at King Abdullah University of Science and Technology (KAUST) led one of the first global assessments of how marine ecosystems responded during the first year when global temperatures temporarily exceeded 1.5°C above pre-industrial levels.
With the North American fire season underway, and a record number of acres already burned nationwide, NASA's Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) satellite's three instruments are observing vegetation precursors to fires, along with plumes of smoke and their movement. These data will help scientists piece together clues that deepen their understanding of wildfires.
New research by Brown University geologists confirms that the Aleutian Islands, the archipelago stretching from Alaska to Russia's Kamchatka Peninsula, experienced a massive geological uplift between 5 million and 7 million years ago. The researchers conclude that the uplift—a rising of the Earth's crust that pushed the islands upward and transformed their topography—was driven by an ancient rotation of the Pacific tectonic plate, which subducts beneath the North American plate near the Alaska Peninsula and the North Pacific.
The eastern tropical Pacific Ocean is known for its large low-oxygen zones that are increasing in size, putting marine life at risk. New research shows that 15 million years ago, the opposite was true.
For decades, the rivers of the Murray-Darling Basin have been heavily regulated by dams and irrigation networks. As a result, the volume of water entering the ocean is about 60% smaller than 100 years ago. But nature broke through during massive floods over the summer of 2022–23, when heavy rains filled the basin's waterways.
Freshwater ecosystems worldwide have been suffering from declining oxygen levels—a trend known as deoxygenation—that threatens biodiversity, fisheries and ecosystem stability. However, a new study published in Nature Geoscience offers hope: targeted nutrient management via wastewater control can reverse this trajectory, even in the face of rapid climate warming.
As earthquakes struck from California to Venezuela to Japan, millions of people received warnings on their mobile phones, providing critical seconds to seek protection.
The two powerful earthquakes that struck Venezuela's northern coast, killing more than 180 people, were an event known as a "doublet."
SummarySeismic full waveform inversion (FWI) is a powerful technique that uses seismic waveform data to generate high resolution images of the Earth’s interior. However, significant uncertainty exists in all FWI solutions due to imperfect acquisition geometries, inherent noise in the data, nonlinearity of the forward problem, and the under-determined nature of real-world tomographic problems in which the target is heterogeneous over all length scales. Probabilistic Bayesian FWI addresses this non-uniqueness by estimating the entire family of possible model solutions and thus the solution uncertainty, described by the so-called posterior probability density function (pdf) over model parameter values. The posterior pdf can be estimated using nonlinear inversion methods to quantify full uncertainties, including those created by nonlinearity in the physics. Alternatively, by linearising (approximating) the physics relating parameters and observations around a chosen reference model solution, the posterior pdf is usually approximated by a compact distribution centred around the maximum a posteriori solution, typically a Gaussian pdf. This is referred to as the linearised method. In this work, we apply both nonlinear and linearised methods to 2D acoustic Bayesian FWI problems. We use one variational inference algorithm for the nonlinear case, in which a transformed Gaussian distribution is optimised to approximate the unknown, full posterior pdf, and a second, independent nonlinear variational algorithm – Stein variational gradient descent – for comparison. The results of both are then compared with those from a linearised, locally-Gaussian based method. The results show that while both the linearised and nonlinear methods recover the posterior mean models accurately, they exhibit different posterior uncertainty structures, especially around layer interfaces, due to the linearisation of wave physics. The differences become most obvious in partially constrained regions of the model, where posterior solutions are constrained jointly by data, prior information, and the nonlinearity of wave physics rather than being dominated by any single factor. We also demonstrate that linearised uncertainty estimates are significantly less accurate: they provide far less accurate fits to observed waveform data, and yield biased estimates of inferred or interpreted meta-properties such as volumes of geological bodies. This work therefore motivates the application of fully nonlinear inversion methods in Bayesian FWI if either accurate uncertainty estimates over parameters, or inferred or interpreted meta-properties are important.
SummaryReliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many algorithms are proposed, evaluated and applied to different areas. Many of these algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem of phase picking. Among these algorithms, the Phase Neural Operator (PhaseNO) shows promising results on regional datasets comparing to existing algorithms. But there are many use cases for the local seismic networks in our community, therefore in this paper we evaluate the performance of PhaseNO on 4 different local datasets and compare the results to PhaseNet and EQTransformer. We used both individual phase picking metrics as well as association metrics to illustrate the performance of PhaseNO. By manually reviewing the newly detected events, we find the PhaseNO model outperforms the single station-based approaches in the local-scale use cases due to its consideration of coherent signals from multiple stations. We also explored PhaseNO’s behaviors when only using one station, as well as gradually increasing the number of stations in the seismic network to better understand its behavior. Overall, using the off-the-shelf machine learning based phase pickers, PhaseNO demonstrated its good performance on local-scale seismic networks.
SummaryEarthquake fault slip arises from nonlinear coupling among frictional evolution, elastic loading, and pore-pressure changes. When pore pressure evolves dynamically, the resulting hydro-mechanical rate-and-state models can be stiff and strongly coupled, making parameter inversion computationally demanding. Here we develop a physics-informed neural network (PINN) solver for a coupled spring–slider system that combines rate-and-state friction with pore-pressure/porosity evolution. The network approximates the time-dependent state variables and is trained by enforcing the governing differential equations together with initial conditions and, for inverse problems, observational constraints. To improve training stability, we employ adaptive inverse-residual weighting and a two-stage optimization schedule (Adam followed by L-BFGS). In forward simulations, PINN predictions closely match a Runge–Kutta reference solution across steady sliding and slow-slip transients, with normalized mean squared error below 0.08 and Pearson correlation coefficient above 0.975 for block velocity and frictional shear stress in the cases tested. In inverse experiments, the framework recovers the applied normal stress from noisy shear-stress observations; uncertainty increases with noise amplitude, but the ensemble mean remains stable, and at the highest noise level considered (q = 1) the inferred normal stress deviates by less than ~1% from the reference value. These results suggest that PINNs provide a differentiable alternative for forward modeling and parameter inversion in coupled hydro-mechanical rate-and-state fault models.
SummaryWe estimated seismic moment tensors (MTs) for the 2017 magnitude M6 Hojedk, Central Iran, earthquake triplet and their aftershocks, employing 1D and 3D regional and global velocity models to evaluate source parameter stability and resolution fitting in-country waveform data. We used the Moment Tensor Uncertainty Quantification (MTUQ) software, which performs a grid search for MT estimation and uncertainty analysis. For the regional 3D velocity model, we used MEAD-M20, a full-waveform inversion model of the Middle East derived from fitting 15-s body waves with 30-s body and surface waves. We compared the regional 1D- and 3D-based results with an existing database of deviatoric MT solutions, and for both regional velocity models, we found good agreement. However, for periods T ≥ 25–30 s and events with moment magnitudes Mw≥ 4.5, the 3D regional synthetic seismograms outperformed the 1D regional model, reducing waveform misfits, time shifts, and non-double-couple contributions. We consider non-double-couple contributions spurious and their reduction an improvement, as previous studies of the sequence found predominantly shear faulting on reverse faults. Furthermore, uncertainty analysis shows that the moment tensor, non-double-couple component, magnitude, and depth are more tightly constrained for the 3D model. The current 3D model shows no clear improvements relative to the 1D model in terms of misfit, time-shifts, and non-double-couple contributions at short periods T ≈ 15-25 s relevant for modeling smaller events. Using the regional models results in lower misfits and tighter constraints on the MT solutions than with the global 1D PREM and the 3D S2.9EA models. Improved MT estimation and parameter resolution for moderate-to-large events using in-country data validate the recently developed 3D Middle East velocity model. Further model refinements are needed to model shorter-period data required to analyze and improve the resolution of smaller (M ≤ 4) seismic events. Such improvements are within reach by using available in-country data and well-constrained MT solutions from a regional moment tensor database.
SummaryMonitoring the activity of non-natural seismic events is crucial for constructing an accurate seismic catalog, overseeing the safety of industrial operations, and mitigating the potential threats to local residents. However, recent studies have shown that neural network models trained on local data sets may not generalize well to a different region. Here, we leverage the Siamese neural network (SNN) to enhance the generalization of neural network models in discriminating between blasts, collapses, and natural earthquakes under regional shifts. Two distinct data sets are analyzed. The model is trained on a data set from northeastern China and tested on an out-of-region data set from the Inner Mongolia Autonomous Region and Gansu Province. We evaluate the prediction performance of the SNN model against the Convolutional neural network (CNN) model using the K-fold cross-validation technique. Results show that both the CNN and the SNN models achieve highly comparable performance on the in-domain validation data set. However, when applied to the out-of-region test data set, the SNN model with target-region anchors can improve the predicted AUPRC values by 7% and 4% compared with that of the out-of-region CNN model and the CNN model with transfer learning using target-region anchors, respectively. Furthermore, Grad-CAM importance weight analysis shows that the SNN model mainly relies on early-arrival P- and S-wave trains. The study suggests that SNN model with target-region anchors can deliver better generalization and flexibility than the conventional CNN model under regional shifts, which is particularly valuable for regions lacking labeled data sets.
SummaryAfter the 2015 Ms 6.5 Pishan earthquake, three moderate-magnitude earthquakes, the Ms 5.4 Pishan earthquake on September 4, 2021, the Ms 5.4 Yecheng earthquake on September 5, 2021 and the Ms 5.4 Pishan earthquake on October 23, 2022, occurred in the seismically active western Kunlun Range foreland thrust system. The seismogenic structures responsible for the three most recent earthquakes and their relationships with the 2015 Ms 6.5 Pishan event are still not understood. Integrated analysis of relocation results of the three main events and their aftershock sequences, focal mechanism solutions, and regional geology has identified the seismogenic fault and structural geometry near the earthquake source. Our results recognize a gentlely S-dipping Kuoshi fault ramp, which is the frontal structure at the west part of the WKFTS and is responsible for the three most recent moderate-magnitude earthquakes. The 2015 Ms 6.5 Pishan earthquake and the recent moderate-magnitude events were all generated by the frontal fault ramp, indicating a deformation pattern characterized by simple outward thrusting. In the western Kunlun Range foreland, the most active deformation and topographic growth have migrated northward relative to those of the higher terrace folds as the rear ramp slip ceases. Our results provide new insights into deformation pattern and seismic hazard in the region.
For nearly 50 years, scientists have suspected that microscopic marine plankton play a role in cloud formation over the oceans. Now, an experiment led by the University of Helsinki suggests that it may be more important than previously thought. The findings are published in the journal Nature.
Forests and land play an important role in absorbing carbon dioxide emissions, but current models and forecasts don't incorporate a surprising ecological discovery: Despite more available carbon, climate change and warmer temperatures are slowing forest growth.
To understand where Earth might be headed, it's important to know where it has been. Throughout its existence, especially over the past couple of million years, Earth has experienced periodic cold and warm intervals, known as glacial and interglacial periods.
New research led by Curtin University and QUT (Queensland University of Technology) has revealed that repeated asteroid impacts may have been the dominant force shaping early Earth, delivering vast amounts of heat into the planet's interior and delaying the formation of stable continents. The study suggests that during the Hadean—more than four billion years ago—Earth was struck far more frequently than today, with each impact injecting energy deep into the planet.
On Wednesday evening just after 6 p.m. local time, two earthquakes violently shook northern Venezuela.
