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SummaryIntermittent fluid injection aims at inferring and steering hydraulic transmissivity and has become an integral part of reservoir stimulation techniques. Modeling the poroelastic response of such pumping operations poses new challenges with respect to the hydromechanical coupling. This is because when a fluid pressure perturbation is introduced in the pore space of a deformable porous rock, it will induce a stress perturbation in the solid phase and this is accompanied by pore boundary motion. Within the limits of quasi-static linear poroelasticity, we analyze the macroscopic signatures of pore boundary motion during injection, i.e., when the rock frame is mechanically loaded, and after injection stop, i.e., when pore boundaries tend to relax back into equilibrium. We show that there is a pumping sequence that allows to harness the energy associated with pore boundary motion accumulated during the frame-loading cycle. Our results foster the need to distinguish how pressure diffusion in poroelastic solids proceeds: either fluid transport is of compressible or incompressible nature and the respective diffusion constant depends on undrained or drained poroelastic moduli.

SummaryThermal properties such as thermal conductivity (TC), thermal diffusivity (TD), and specific heat capacity (SHC) are essential for understanding and modelling the subsurface thermal field. In sedimentary basins, these parameters play a key role in characterizing the present-day thermal state and predicting its evolution, for example, in response to future geo-energy utilizations. Given the wide range of potential geo-energy utilizations and the frequent lack of sufficient sample material, many studies have focused on developing accurate prediction approaches. Machine learning (ML) offers promising non-linear statistical methods to enhance the mapping of interrelations between standard geophysical well-log readings and thermal rock properties. In this study, we introduce an open-access tool for computing profiles of thermal rock properties from standard geophysical borehole logging data, building upon and extending previous petrophysical studies. The tool employs various machine-learning approaches trained on large, physically modelled synthetic datasets that account for mineralogical and porosity variability across major sedimentary rock groups (clastic rocks, carbonates, and evaporates). It establishes functional relationships between thermal properties and different combinations of standard well-log data, including sonic velocity, neutron porosity, bulk density, and the gamma-ray index. We trained four different models including linear regression, AdaBoost, Random Forest, and XGBoost using 80 per cent of the synthetic group data for model development, including training and hyperparameter tuning through cross-validation. The remaining 20 per cent was held out as an independent test set for statistical validation, feature recognition, and input variable importance analysis. A total of 15 input log combinations (including all one, two, three, and four well-log configurations) were evaluated across four machine learning models (linear regression, AdaBoost, Random Forest, and XGBoost), resulting in 180 trained models. The model's predictive accuracy and reliability were further evaluated against independent laboratory drill-core measurements reported in recent studies. Our results indicate that the best-performing predictive models vary depending on the available log-combinations. However, XGBoost frequently outperforms other models in sedimentary rocks. When at least two well logs are provided as input variables, the best-performing models predict thermal conductivity with an uncertainty below 10 per cent relative to borehole validation data (with laboratory-measured thermal conductivity). In most tested model cases and for most input log combinations, predictive errors for thermal conductivity range between 10 and 30 per cent at the (point measurement) sample scale (cm to half a meter). However, when averaged over geological formations or borehole intervals (tens to thousands of meters), the accuracy of thermal-conductivity predictions improves significantly, with uncertainties of the interval mean conductivity dropping below 5 per cent for large intervals. For specific heat capacity, prediction accuracy for the best-performing models at the measurement scale is typically better than 5 per cent. Thermal diffusivity reflects a larger variation, accumulating the uncertainties from conductivity and heat capacity. The presented log-based Python prediction tool provides an automated means to compute thermal parameters using the most suitable ML model for given well-log inputs, facilitating enhanced thermal characterization in sedimentary settings. This has practical relevance for geothermal or hydrocarbon exploration, or subsurface storage projects.

The Southwest United States is currently facing its worst megadrought of the past 1,200 years. According to a recent study by the University of Texas at Austin, the drought could continue at least until the end of the century, if not longer.

Between July 3 and 6, Texas Hill Country experienced catastrophic flash flooding along the Guadalupe River system. The floods claimed at least 130 lives, with over 96 fatalities in Kerr County alone. More than 160 people were missing as of July 12, including children attending camps along the river.

Industrial whaling was historically a grisly affair enacted with brutal efficiency. With an eye to harpooning as many whales as possible, whalers created detailed records intended to inform and improve future expeditions.

Those records, stretching back more than a century, provide rich datasets that scientists have used to answer questions about our planet’s past, including how sea ice surrounding Antarctica ebbed and flowed in the decades before satellites enabled continuous monitoring.

“I find it a paradox. We decimated them; now, they’re helping us to do a better job for our future projections.”

In a study published earlier this year in Environmental Research: Climate, a team of cetologists, oceanographers, and climate scientists dug deep into those records and used them to show that contemporary climate models overestimate the historic extent of sea ice in the Southern Ocean.

The group relied specifically on data from humpback whaling expeditions because that species tends to skirt along the ice edge in summer, skimming krill fed in turn by algal mats that form along the underside of the ice as it thins and retreats. This behavior makes the locations of humpback harvests a useful proxy for how far north sea ice could have reached.

“I find it a paradox,” said oceanographer Marcello Vichi of the University of Cape Town in South Africa. “We decimated them; now, they’re helping us to do a better job for our future projections.” Vichi is the first author of the new study.

Icy Estimation

Accurately estimating the extent of sea ice is important for modeling because ice reflects sunlight, said Marilyn Raphael, a physical geographer at the University of California, Los Angeles, who often focuses her research on Antarctic sea ice but was not involved with the new study.

“If it doesn’t do that reflection, the large-scale [latitudinal] temperature gradient changes,” Raphael explained, “and when the temperature gradient changes, the wind changes. And when the wind changes, the climate changes.”

Sea ice also insulates parts of the Southern Ocean, limiting how much heat the water absorbs from the atmosphere.

How climate models input the historical extent of sea ice shapes how they account for Earth’s energy balance prior to the onset of climate change. More accurate historic inputs also have implications for modeled predictions about the extent of sea ice in the future.

“If you can’t get it right when you know what happened,” Raphael said, “then you’ve got to worry about if you’ll get it right when you don’t know what will happen.”

Using Catch Data to Constrain Sea Ice

Vichi and his colleagues used data acquired from the International Whaling Commission, which recorded the locations of more than 215,000 humpback catches over the first half of the 20th century, with latitude and longitude logged to the nearest degree.

They focused their study on the 1930s, a period during which whalers logged consistently high catch counts for each month of the Antarctic summer (November through February), when humpbacks feed as ice retreats. This narrowed scope left the researchers with around 13,500 records to work with, of which more than 97% had trustworthy location data.

The team compared the catch locations with the climate models that are best tuned to match today’s satellite observation data.

All the models, they found, consistently overestimate the historic extent of sea ice by an average of about 4° latitude. In some places, Vichi added, they overshoot the ice edge suggested by the whaling records by 10°.

“It’s really great to examine historical data to find ways of understanding a complex system better, especially a complex system that we don’t have a lot of observations on.”

Vichi and his colleagues don’t yet know for certain what drives the discrepancy they found. One possible explanation may be that the nature of how ice forms and behaves in the Southern Ocean has shifted, potentially entering a new regime around the 1960s.

Scientists are working with fewer than 50 years of satellite observations when it comes to sea ice, “so if there are large cycles that happen, we don’t know if that 50 year period is representative of the whole,” said climatologist Ryan Fogt of Ohio University in Athens, who wasn’t involved in the study.

Given the dearth of direct observations, this gap can be filled only with proxies like catch data. Using indirect data is an approach both Raphael and Vichi acknowledge has limitations but is crucial for better understanding the nuances of climate change.

“I think using the whaling records is a good idea,” Raphael said. “It’s important to use all the information we have to see how it matches.”

“It’s really great to examine historical data to find ways of understanding a complex system better, especially a complex system that we don’t have a lot of observations on,” said Fogt, who has also worked with historical records (though not whale catch data) to reconstruct historic sea ice extent around Antarctica. “So even though they’re imperfect, these historical sources, they have value.”

—Syris Valentine (@shapersyris.bsky.social), Science Writer

Citation: Valentine, S. (2025), Whaling records can help improve estimates of sea ice extent, Eos, 106, https://doi.org/10.1029/2025EO250251. Published on 15 July 2025. Text © 2025. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

A strange planet orbiting a distant star may be even weirder than we realized. Already thought to have “iron rain” and an unusual polar orbit, this ultrahot Jupiter might also have begun life far away from its star before diving into a tight 30-hour orbit.

The planet, WASP-121b, or Tylos, is about 850 light-years from Earth and was discovered in 2015. Observing the planet in October 2022 with the James Webb Space Telescope (JSWT), researchers found it hosted a surprising amount of methane and silicon monoxide. Their observations mark the first time silicon monoxide has been conclusively found on another world.

“Something weird happened dynamically in its past.”

The presence of methane and silicon monoxide, researchers say, might mean WASP-121b initially formed much farther from its star—as far away as 30 astronomical units, about the same distance Neptune lies from our Sun. (One astronomical unit is the average distance between the Sun and Earth.) The findings were published in Nature Astronomy and The Astronomical Journal.

“Something weird happened dynamically in its past,” said Tom Evans-Soma, an astronomer at the University of Newcastle in Australia and lead author of the Nature paper. “And it may be a big factor in how it moved from far out to close in.”

Iron Rain

Hot Jupiters are a class of gas giant planets that orbit extremely close to their stars and have temperatures exceeding 1,500 K (2,200°F). Ultrahot Jupiters are even closer and hotter, sometimes reaching temperatures above 2,000 K (3,100°F).

WASP-121b is one such ultrahot world, orbiting its star (WASP 121) within 2 times the star’s radius. At this proximity, the planet is tidally locked to the star, the way the Moon is to Earth, so the same face always points to the star. Atmospheric temperatures on WASP-121b can reach more than 3,000 K (4,900°F) on the dayside and 1,100 K (1,500°F) on the nightside.

This discrepancy in temperature may help explain the concept of iron rain on WASP-121b. Metals are likely to vaporize on the fiery dayside, and as these particles blow to the nightside, the drop in temperature creates conditions for droplets of liquid metal to form and fall from the planet’s atmosphere. “The nightside temperatures drop low enough for a whole bunch of these materials to condense,” possibly within seconds, said Evans-Soma.

The planet’s proximity to its star has also stretched the world into an oblong shape, and it orbits its star in a strange 90° orientation, almost pole to pole above and below the star. The planets of our solar system, by comparison, orbit in a flat plane.

A Distant Origin

These characteristics alone had already painted WASP-121b as an unusual world, but the latest observations further add to its mystery.

The researchers used JWST to observe the planet for 40 hours and pick apart its light, revealing the presence of water, carbon monoxide, and silicon monoxide on the dayside. These compounds may have been pulled from the nightside by a powerful equatorial jet with wind speeds of up to 10 kilometers (6 miles) per second.

The team detected methane in the planet’s nightside—a surprising result because methane shouldn’t survive WASP-121b’s high temperatures.

The team also detected methane in the planet’s nightside—a surprising result because methane shouldn’t survive WASP-121b’s high temperatures at all. “People have been looking for methane in exoplanets, but generally focusing on much cooler planets,” said Evans-Soma.

The presence of methane suggests the planet has a source of the compound replenishing its atmospheric supply. The team thinks the source might be trapped methane pulled up from the planet’s interior by strong convection currents.

The presence of methane might also point to WASP-121b forming much farther from its star. At a greater distance, icy pebbles of the methane were more abundant. Here, too, the gas giant may have consumed 21 Earths’ worth of rocky material during its formation, which would explain the presence of silicon.

A Starward Migration

Richard Booth, a planet formation expert at the University of Leeds in the United Kingdom who was not involved in the research, said that in general, scientists think hot Jupiters migrate inward over time. It is unlikely the planets formed close to their stars, he explained, because the stars’ gravity would have been too strong for planets to coalesce.

“Hot Jupiters definitely don’t form in situ,” said Booth.

But finding “evidence for migration is hard,” he continued, because migration can happen quickly (at least on planetary timescales)—in just millions or even thousands of years.

The WASP-121 system is thought to have formed about 1.1 billion years ago, with its migration possibly happening as a result of a gravitational nudge from a passing star or other planets in the system. Such a nudge might also explain the planet’s odd orbit.

Future work could tell us how this seemingly strange exoplanet compares with other ultrahot Jupiters. “It’s not clear that it is particularly unusual,” said Evans-Soma. “It just happens to be one of the planets we can study in really exquisite detail.”

—Jonathan O’Callaghan, Science Writer

Citation: O’Callaghan, J. (2025), This exoplanet may have grown stranger as it journeyed starward, Eos, 106, https://doi.org/10.1029/2025EO250250. Published on 15 July 2025. Text © 2025. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

From more frequent wildfires to rising sea levels, climate change is disrupting ecosystems and upending once-stable weather patterns. One particularly alarming consequence of rising global temperatures is the potential collapse of the Atlantic Meridional Overturning Circulation (AMOC), a conveyor-belt-like system of ocean currents driven by the sinking of cold, salty waters in the North Atlantic.

Author(s): Zekun Xu, Fuyuan Wu, Zhuang Zhao, Shigeo Kawata, and Jie Zhang

Magnetized fusion has been used in various inertial confinement fusion concepts to relax ignition requirements. Strong magnetic fields would induce burning asymmetry, which may be usually harmful to the central ignition schemes. However, it could be utilized in some intrinsically asymmetric ignition…

[Phys. Rev. E 112, 015206] Published Tue Jul 15, 2025

A new paper (Kharismalatri, Gomi & Sidle 2025) in the journal Natural Hazards uses the concepts of the inflow angle and the channel gradient to examine the behaviour of large landslides after failure.

Large landslides in areas with steep terrain that either block the valley or turn into a long runout debris flows are an increasing problem globally as the impacts of climate change accelerate. A key question for any large, potentially unstable slope is whether it will block the valley or transition into a long runout flow. Neither is good, clearly, but the risks and management approaches differ.

There is a very interesting paper (Kharismalatri, Gomi & Sidle 2025) in the journal Natural Hazards that uses a database of 188 large landslides from around the world to examine this issue. The paper has been published open access and using a creative commons licence (hurrah!), so please do take a look.

This diagram, from the paper, explains a key and very interesting metric in the study – the inflow angle:-

Key concepts, including the inflow angle, in the study of large landslides by Kharismalatri, Gomi & Sidle (2025).

There are two key ideas here – one is the inflow angle, which is the angle between the main axis of the landslide and the main axis of the channel, and the other is the channel gradient – the gradient of the river valley into which the landslide is moving, measured using a consistent distance scaled to the landslide length.

The most important diagram in the paper is this one, which shows the inflow angle plotted against channel gradient:-

The relationship between the inflow angle, and the channel gradient , from the study of large landslides by Kharismalatri, Gomi & Sidle (2025).

This is quite remarkable. The inflow angle plays a key controlling role in what happens when the landslide reaches the valley. If that angle is greater than about 60o, the landslide nearly always blocks the valley. If it is less, then it generally turns into a debris flow.

Similarly, if the channel gradient is less than about 10o, the landslide almost always blocks the valley. If it is less, it generally turns into a debris flow.

There are some overlaps, but the number of these cases is remarkably low.

It is also very interesting that there are no cases of landslides with both a high inflow angle and a high channel gradient (i.e. where inflow angle is >60o and channel gradient is >15o). I am not sure why this is the case.

This will be a very useful finding for those who are having to manage developing failures in large slopes. It allows a first order prediction of likely behaviour of the slope upon failure. So, for example, I wrote yesterday about a study of the potential failure of the large landslide at Joshimath in India. It would be interesting to see where on the graph that slope plots – it appears to me that the inflow angle is c.90o?

Reference

Kharismalatri, H.S., Gomi, T. & Sidle, R.C. 2025. Geomorphic thresholds for cascading hazards of debris flows and natural dam formation caused by large landslides. Natural Hazards. https://doi.org/10.1007/s11069-025-07402-0

Return to The Landslide Blog homepage Text © 2023. The authors. CC BY-NC-ND 3.0
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Abstract

To meet the high-precision and high-integrity positioning demands of safety–critical applications, monitoring the quality of precise satellite products in global navigation satellite system (GNSS) precise point positioning (PPP) is crucial. This work employs ionosphere-free (IF) PPP with ambiguity resolution (PPP-AR) phase residuals to construct test statistics for monitoring the quality of precise satellite corrections. By utilizing precise satellite orbit and clock products from CODE, WUM, and GRG, the PPP-AR phase residuals were first analyzed with sample moments, Allan variance and power spectral density (PSD). The key findings are as follows: (1) The skewness and kurtosis results indicate that ambiguity-fixed phase residuals deviate from an ideal zero-mean Gaussian distribution and exhibit a super-Gaussian distribution. (2) Allan variance and PSD analysis reveal that flicker noise dominates the phase residuals. (3) The noise amplitudes are similar for all satellites, but certain differences are observed among different GNSS systems and satellite types. (4) The noise level of phase residuals is influenced by the receiver types, antenna types, and precise products from different analysis centers. Leveraging the error characteristics, the two-step Gaussian overbounding (OB) method was employed to estimate the corresponding OB parameters of the phase residuals. The overbounding results demonstrate that, under similar conditions, phase residuals can be bounded by the calculated bound within the acceptable integrity risk after removing the detected outliers. Anomaly monitoring experiments further show that phase residuals can effectively capture anomalies in precise satellite corrections, with the set threshold successfully detecting such anomalies.

The measurement of virtual height of the sporadic E layer (h'Es) is very sensitive to the type of ionosonde used and the calibration processes. The ionosondes used by the national institute of communication an...

Abstract

Solar photospheric abundances and CI-chondrite compositions are reviewed and updated to obtain representative solar system abundances of the elements and their isotopes. The new photospheric abundances obtained here lead to higher solar metallicity. Full 3D NLTE photospheric analyses are only available for 11 elements. A quality index for analyses is introduced. For several elements, uncertainties remain large. Protosolar mass fractions are H (X = 0.7060), He (Y = 0.2753), and for metals Li to U (Z = 0.0187). The protosolar (C+N)/H agrees within 13% with the ratio for the solar core from the Borexino experiment. Elemental abundances in CI-chondrites were screened by analytical methods, sample sizes, and evaluated using concentration frequency distributions. Aqueously mobile elements (e.g., alkalis, alkaline earths, etc.) often deviate from normal distributions indicating mobilization and/or sequestration into carbonates, phosphates, and sulfates. Revised CI-chondrite abundances of non-volatile elements are similar to earlier estimates. The moderately volatile elements F and Sb are higher than before, as are C, Br and I, whereas the CI-abundances of Hg and N are now significantly lower. The solar system nuclide distribution curves of s-process elements agree within 4% with s-process predictions of Galactic chemical evolution models. P-process nuclide distributions are assessed. No obvious correlation of CI-chondritic to solar elemental abundance ratios with condensation temperatures is observed, nor is there one for ratios of CI-chondrites/solar wind abundances.

The January 2022 Tonga volcanic eruption generated atmospheric pressure waves that propagated over the ocean’s surface and triggered a meteotsunami. This meteotsunami caused significant amplitudes exceeding 10...

Abstract

Accurately modeling and prediction the nonlinear motion of GNSS (Global Navigation Satellite System) coordinate time series holds significant theoretical and practical value for the study of geodynamics. A novel integrated network, named Ensemble Learning method based on Signal Source Driver (ELSSD), is proposed, which leverages the strengths of Long Short-Term Memory (LSTM) and Deep Self-Attention Neural Network (DSANN), while integrating GNSS loading data as an additional data source. Additionally, a multi-track synchronous sliding window data processing strategy is designed to address the challenge of multi-source data fusion input. The effectiveness of this algorithm is validated using GNSS coordinate time series from 186 global stations over a period of 10 years. Experimental results initially illustrate that, when accounting for displacement caused by environmental loading effects, there is a marked improvement in the modeling and prediction accuracy compared with GNSS input-only. Furthermore, the application of three ensemble network strategies-Bagging, Boosting, and Stacking-have further been demonstrated to enhance modeling and prediction accuracy. Compared with LSTM and DSANN networks, the proposed ELSSD algorithm achieves an average RMSE (Root Mean Square Error) of 3.6 mm for both modeling and prediction, with modeling accuracy improvements of 4.8% and 6.2%, while prediction accuracy improvements of 5.4% and 5.9%, respectively. With respect to the traditional Least Square method, there is an improvement of 22.1% and 27.9% in modeling and prediction accuracy, respectively. Regarding noise characteristics, there is a significant reduction in colored noise amplitude, with decreases of 36.7% and 36.0% observed in modeling and prediction, respectively. Simultaneously, the velocity uncertainty experiences an average reduction of 27.1% and 27.5%. The average velocity differences are measured at 0.06 mm/year and 0.24 mm/year, respectively. Hence, our findings suggest that the ELSSD algorithm emerges as an effective methodology for handling multi-source data input in GNSS coordinate time series, presenting promising practical applications in the field.

The Mw 7.5 Noto Peninsula earthquake, which occurred on January 1, 2024, was considerably hazardous to the peninsula and surrounding regions owing to a strong motion, large-scale crustal deformation, and subse...

Nature Geoscience, Published online: 21 February 2025; doi:10.1038/s41561-025-01653-z

Quartz-rich clasts in Martian meteorite NWA 7533 indicate the presence of granitic rocks on early Mars that formed via hydrothermal activity and impact melting, according to petrologic and in situ geochemical analyses.

Abstract

The predictive learning of total electron content (TEC) spatiotemporal sequences aims to generate future TEC maps by learning from historical data, where both the spatial appearances and temporal variations are crucial for accurate predictions. However, the state-of-the-art TEC map prediction models typically employ sequential stacking of ConvLSTM, ConvGRU, and their variants. These models focus more on modeling temporal variations, and the spatial features extracted from the historical sequence are highly abstracted, resulting in the fine-grained spatial appearances not being adequately memorized or transmitted, leading to fuzzy prediction results during storm time. In this paper, we used PredRNN to propose a storm-time ionospheric TEC spatiotemporal prediction model with multichannel features, named Multichannel PredRNN, which can simultaneously remember the temporal patterns and spatial appearances in input sequence. The temporal memory as well as the spatial memory are updated repeatedly over time, ensuring that both temporal memory and spatiotemporal memory are fully utilized in prediction. According to Dst index, 60 magnetic storm events from 2011 to 2019 were selected as the dataset. We first discussed the impact of feature combinations on predictive performance. The results show that using multichannel feature (TEC + Dst&F10.7), the Multichannel PredRNN and the comparison models ConvGRU and ConvLSTM have the best prediction performance. Then we used the optimal feature combination for prediction. We compared Multichannel PredRNN with IRI-2016, COPG, ConvLSTM and ConvGRU under various conditions, including the entire test magnetic events, periods of quiet and storm, different phases of geomagnetic storms, and the most severe geomagnetic storms. Finally, we compared the performance of different output steps. The experimental results indicate that in all cases, Multichannel PredRNN with dual memory state and zigzag flow is superior to four compared models.

Abstract

The gravimetry measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission provide an essential way to monitor changes in ocean bottom pressure ( \(p_b\) ), which is a critical variable in understanding ocean circulation. However, the coarse spatial resolution of the GRACE(-FO) fields blurs important spatial details, such as \(p_b\) gradients. In this study, we employ a self-supervised deep learning algorithm to downscale global monthly \(p_b\) anomalies derived from GRACE(-FO) observations to an equal-angle 0.25  \( ^{\circ }\) grid in the absence of high-resolution ground truth. The optimization process is realized by constraining the outputs to follow the large-scale mass conservation contained in the gravity field estimates while learning the spatial details from two ocean reanalysis products. The downscaled product agrees with GRACE(-FO) solutions over large ocean basins at the millimeter level in terms of equivalent water height and shows signs of outperforming them when evaluating short spatial scale variability. In particular, the downscaled \(p_b\) product has more realistic signal content near the coast and exhibits better agreement with tide gauge measurements at around 80% of 465 globally distributed stations. Our method presents a novel way of combining the advantages of satellite measurements and ocean models at the product level, with potential downstream applications for studies of the large-scale ocean circulation, coastal sea level variability, and changes in global geodetic parameters.