For decades, scientists have searched for a clear link between the sun's explosive storms and the weather that occurs on Earth. A breakthrough study from the University of New Hampshire reveals that in the hours and days following a solar storm, parts of North America can see sharp changes in the weather—such as declines in precipitation—and the more powerful the storm, the more dramatic the shift.
Scientists investigating a proposed way to remove carbon dioxide from the atmosphere using seawater have found that adding too much alkalinity to neutralize acids can trigger chemical reactions that undermine the process.
In a Penn State lab, a small cylinder of soil sits wired with sensors, slowly cooling as it mimics conditions thousands of miles away.
SummaryMagnetic inversion is a key tool for imaging subsurface geological structures, but conventional 3-D magnetic inversion in the spatial domain is often limited by the computational and memory cost of large dense kernel matrices. Existing transformed-domain approaches improve efficiency, yet pseudo-3D implementations still rely on layer-by-layer accumulation and repeated Fourier transforms. In this study, we develop a unified wavenumber-domain framework for the forward modelling and inversion of total-field magnetic anomalies and magnetic gradient-tensor data. For regularly discretized rectangular prisms beneath a planar observation surface, the wavenumber-domain Green operator is reformulated into a factorized representation consisting of two explicitly stored diagonal/block-diagonal spectral factors and one implicitly applied separable horizontal operator. This implementation avoids repeated vertical layer superposition and reduces the forward evaluation to a single FFT/IFFT pair together with structured spectral multiplications. The factorized forward operator is then embedded in a Tikhonov-regularized inversion and solved through a Sherman-Morrison-Woodbury (SMW) reduced system. The transformed-domain data term is defined as an unweighted complex-valued least-squares residual, and its relation to the spatial-domain least-squares formulation is stated under the corresponding padding and truncation assumptions. Synthetic examples show that the method reproduces conventional spatial-domain responses and recovers the principal features of prescribed magnetization models under 5% Gaussian noise. For a 200×200×100 model, the forward modeling and core inversion times are 0.172 s and 31.73 s, respectively, on a standard laptop. Application to field data is used as a practical feasibility test and shows a data-consistent recovered magnetization distribution, but it should not be regarded as an independent geological validation of the recovered model. The current implementation assumes a planar observation surface, a regular FFT-compatible grid, and a spatially uniform magnetization direction. It does not yet address strong remanence, spatially variable magnetization, irregular topography, irregular acquisition geometries, depth weighting, focusing stabilizers, or geological constraints. Under these assumptions, the proposed framework provides an efficient, memory-economical, and scalable alternative for large-scale magnetic anomaly interpretation.
SummaryWe present a high-resolution three-dimensional P-wave attenuation tomography model of the northern Chilean subduction zone (21°–22°S), derived using the coda-normalization approach implemented in the MuRAT algorithm and a dense local earthquake dataset. This region represents an important segment of the South American margin, where the Nazca Plate subducts beneath the South American Plate, generating frequent intermediate-depth seismicity and sustained volcanic activity along the Western Cordillera. Understanding the distribution of attenuation and its relation to seismicity and fluid pathways is essential for constraining the physical state of the subduction system and its role in arc magmatism and crustal deformation. The inversion incorporates 147,639 high-quality waveforms from 42,460 local earthquakes recorded by 76 broadband stations between 2007 and 2021. The inversion was carried out using a three-dimensional velocity model with 10 km node spacing, and the resulting attenuation grid was parameterized at 14 × 25 km horizontally and 10 km vertically. The attenuation model reveals two main low-Q anomalies. The first extends along and immediately above the top of the subducting Nazca slab between 50 and 90 km depth, interpreted as the locus of fluid release from slab dehydration. The second low-Q zone ascends from the mantle wedge towards the lower crust beneath the volcanic arc, indicating fluid migration. These features coincide with high-Vp/Vs regions from velocity tomography models. Low-Q regions are generally found above seismicity concentrations in the downgoing Nazca slab, reaffirming the association of intraslab earthquakes with fluid release processes. Resolution tests confirm the robustness of the imaged structures. The obtained anomalies trace subduction-related fluids from their source in the downgoing slab through the mantle wedge towards the magmatic arc.
SummaryThe explicit inertial modes in spheres and oblate spheroids, owing to their clear and concise mathematical formulations, have been applied in many geophysical and astrophysical studies. In contrast, the implicit inertial modes are rarely used because of their mathematical complexity. Due to the presence of factorials and double factorials inherited from the associated Legendre polynomials, the computation of explicit inertial modes becomes intractable at high orders. Based on the implicit inertial modes, this research, for the first time, develops a new algorithm that enables fast computation of the inertial modes in spheres and spheroids of arbitrary eccentricity even at high orders. In addition, it offers an efficient approach to computing the geostrophic polynomials, which are a set of special inertial modes with zero frequency in spheres and spheroids. In this new algorithm the inertial modes and the half-frequencies are expressed as functions of the associated Legendre polynomials and their first derivatives with respect to the modified oblate spheroidal coordinates. Several numerical experiments demonstrate the efficiency of this new algorithm. It is also verified that both the non-penetrable boundary condition and the incompressible condition are satisfied by the numerical results produced by this algorithm.
SummaryTectonic gravity anomalies are commonly assumed as static, except during major geodynamic events like earthquakes or plate reorganizations. This study challenges such an assumption at the regional scale by examining the ongoing rifting in the Gulf of Aden. Using 3D finite element and gravitational modelling, it can be shown that horizontal motion between oceanic and continental crusts – characterized by a density contrast of 400 kg/m3 and a divergence rate of 1.25 cm/yr – generates a potentially measurable gravity rate of change, forming a dipolar pattern with peak amplitudes of ±40 nGal/yr. Numerical simulations were conducted to evaluate whether this signal could be actually measured by the forthcoming MAGIC satellite mission. To this aim, the time-variable gravity field derived from the 3D finite element was propagated into orbit simulations, considering only instrumental noise. A series of 1-year least squares solutions were computed from the simulated data in terms of spherical harmonics. Then gravity disturbance grids at 5 km height covering the Gulf of Aden were derived and the gravity rate was estimated at each point of the grid, considering different maximum harmonic degree. Results indicate that the noise level of the MAGIC instrumentation is low enough to make it sensible to this signal, despite spatial resolution limitations. The two opposing gravity stripes cannot be distinguished, but a central bump of gravity rate with an amplitude of about 6 nGal/yr can be well identified by considering a maximum harmonic degree of 70. Of course, the detectability of such a signal from MAGIC observations becomes unfeasible when considering the temporal aliasing induced by other geophysical phenomena involving stronger and faster mass transport. Nevertheless, these findings suggest that tectonic processes associated with rifting can induce measurable gravity variations (given the accuracy level of MAGIC instrumentation), even in the absence of episodic seismic activity, offering new prospects for satellite gravimetry in monitoring active plate boundaries.
The Himalayas are often seen as one of Earth's great natural barriers, separating the heavily populated and industrialized regions of South Asia from the remote Tibetan Plateau. But new research, published in Geophysical Research Letters, suggests that this mountain range is not an impenetrable wall for air pollution.
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.
