Isostasy is a simple concept, yet it has long perplexed students of geology and geophysics. This fully updated edition provides the tools to better understand this concept using a simplified mathematical treatment, numerous geological examples, and an extensive bibliography. It starts by tracing the ideas behind local and regional models of isostasy before describing the theoretical background, the observational evidence. It now also includes an exploration of the role of flexure in landscape evolution and dynamic topography and discussions of lithosphere memory, inheritance, and new NASA mission topography and gravity data. The book concludes with a discussion of flexure's role in understanding the evolution of the surface features of the Earth and its neighboring planets. Intended for advanced undergraduate and graduate students of geology and geophysics, it will also be of interest to researchers in gravity, geodesy, sedimentary basin formation, mountain building and planetary geology.
Transport properties in fluids and confined systems play a central role across a wide range of natural and technological contexts, from geology and environmental sciences to biology, energy storage, and membrane-based separation processes. Nuclear Magnetic Resonance (NMR) provides a unique, non-destructive means to probe these properties through species-selective measurements of self-diffusion coefficients. While pulsed field gradient NMR (PFG-NMR) is routinely used, its access to diffusion times is typically limited to values no shorter than about 10 ms, restricting its applicability to systems with fast dynamics and long relaxation times. Diffusion NMR in a permanent magnetic field gradient (STRAFI) offers a complementary, multiscale approach, enabling diffusion measurements over an extended temporal window, from a few hundred microseconds to several tens of seconds. Despite its strong potential, this technique remains rarely implemented due to experimental and methodological challenges. In this work, we present a robust and versatile STRAFI-based methodology, including a specifically designed experimental setup, optimized pulse sequences, and rigorous data analysis, allowing accurate extraction of self-diffusion coefficients for a broad range of nuclei. The capabilities of the approach are illustrated through diverse applications, including the study of concentrated electrolytes using "NMR-exotic" nuclei ($^{35}$Cl, $^{79}$Br/$^{81}$Br, $^{127}$I, $^{17}$O) and the characterization of micrometre-scale porosity in membranes.
Abstract Rockfall represents a sudden and highly destructive geological hazard, posing significant risks to mountainous communities and infrastructure. This study presents an integrated dynamic risk assessment for the Jiaohua perilous rock zone in Kaizhou District, Chongqing, China, by fusing multi-source data including field investigation, UAV photogrammetry, and 3D numerical simulation. Kinematic analysis identified a critical slope angle of 57° for rockfall initiation, enabling the classification of two primary susceptibility zones. High-precision 3D simulations using RAMMS: ROCKFALL were conducted on six identified hazardous rock masses (#WY1–#WY6). The simulations delineated two distinct rockfall mechanisms: #WY1–#WY3 sources generate high-energy, short-duration impacts, achieving kinetic energies up to 1.88 × 10⁴ kJ within 10–15 s, posing a direct threat to the residential area below. Conversely, rockfalls from #WY4–#WY6 involve longer travel paths with considerable energy attenuation, yet residual kinetic energy remains capable of causing zonal damage. The simulated kinetic energies were translated into quantitative impact force estimates, forming the basis for differentiated mitigation strategies. These include active reinforcement and high-strength interception for short-range, high-energy events, and multi-level buffering with trajectory control for long-runout cases. This integrated methodology offers a scientifically grounded framework for precise hazard prevention and serves as a valuable reference for rockfall risk management in analogous geological settings, particularly within the Three Gorges Reservoir area.
Gerard. T. Schuster, Jing Li, Sherif Hanafy
et al.
We investigate the feasibility of using rocket launches, specifically rocketquakes, as a seismic source to image subsurface velocity and geology of planetary bodies. Toward this goal, we record the seismic vibrations excited by a Falcon 9 rocket launch from Vandenberg Space Force Base (SFB) near Lompoc, California. Nine passive three-component (3C) seismometers were deployed every 18.75 meters along a 45-degree line from the launch site starting at the offset of about 7 km kilometers from the launch pad. Results show that coherent body waves can be recorded with a P-velocity of more than 2.0 km/s and a penetration depth of 1 km or deeper. Stronger Rayleigh waves were also recorded and inverted to give an S-velocity profile to a depth of 60 m. Notably, a 3C recorder placed approximately 15 km from the launch site did not capture any discernible body wave arrivals. The imaging techniques employed for rocketquake seismology integrate inversion methods from earthquake and exploration seismology, yielding the P- and S-velocity profiles of the subsurface. These results suggest that rocket launches as seismic sources will provide unprecedented opportunities for identifying the subsurface hazards, faults, tunnels, water ice, and mineral deposits of planetary bodies and their moons.
Daniel Jenson, Jhonathan Navott, Piotr Grynfelder
et al.
Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic processes. While early architectures were developed primarily as a scalable alternative to Gaussian Processes (GPs), modern NPs tackle far more complex and data hungry applications spanning geology, epidemiology, climate, and robotics. These applications have placed increasing pressure on the scalability of these models, with many architectures compromising accuracy for scalability. In this paper, we demonstrate that this tradeoff is often unnecessary, particularly when modeling fully or partially translation invariant processes. We propose a versatile new architecture, the Biased Scan Attention Transformer Neural Process (BSA-TNP), which introduces Kernel Regression Blocks (KRBlocks), group-invariant attention biases, and memory-efficient Biased Scan Attention (BSA). BSA-TNP is able to: (1) match or exceed the accuracy of the best models while often training in a fraction of the time, (2) exhibit translation invariance, enabling learning at multiple resolutions simultaneously, (3) transparently model processes that evolve in both space and time, (4) support high dimensional fixed effects, and (5) scale gracefully -- running inference with over 1M test points with 100K context points in under a minute on a single 24GB GPU.
Tess Heeremans, Simon Lépinay, Romane Le Dizès Castell
et al.
Spherulites are complex polycrystalline structures that form through the self-assembly of small aggregated nanocrystals starting from a central point and growing radially outward. Despite their wide prevalence and relevance to fields ranging from geology to medicine, the dynamics of spherulitic crystallization and the conditions required for such growth remain ill-understood. Here, we report on the conditions to induce controlled spherulitic growth of sodium sulfate from evaporating aqueous solutions of sulfate salt mixtures at room temperature. We reveal that introducing divalent metal ions in the solution cause spherulitic growth of sodium sulfate. For the first time, we quantify the supersaturation at the onset of spherulitic growth from salt mixtures and determine the growth kinetics. Our results show that the nonclassical nucleation process induces the growth of sodium sulfate spherulites at high supersaturation in highly viscous solutions. The latter reaches approximately 111 Pa$\cdot$s, triggered by the divalent ions, at the onset of spherulite precipitation leading to a diffusion limited growth. We also show that spherulites, which are metastable structures formed under out-of-equilibrium conditions, can evolve into other shapes when supersaturation decreases as growth continues at different evaporation rates. These findings shed light on the conditions under which spherulites form and offer practical strategies for tuning their morphology.
Corrosion mechanism of minerals and glass is a critical study domain in geology and materials science, vital for comprehending material durability under various environmental conditions. Despite decades of extensive study, a core aspect of these mechanisms - specifically, the formation of amorphous alteration layers upon exposure to aqueous environments - remains controversial. In this study, the corrosion behavior of a boro-alumino-phospho-silicate glass (BAPS) was investigated using advanced solid-state nuclear magnetic resonance (SSNMR) and SEM techniques. The results reveal a uniform nanoscale phase separation into Al-P-rich and Al-Si-rich domains. During corrosion, the Al-P-rich domain undergoes gelation, whereas the Al-Si-rich domain remains vitreous, forming a gel layer comprised of both phases. Although SEM images show a sharp gel/glass interface - suggestive of a dissolution-precipitation mechanism - the phase coexistence within the gel layer provides definitive evidence against such a mechanism. Instead, we propose an in situ transformation mechanism governed by chemical reactions, involving: (i) preferential hydrolysis of Al-P-rich domain leading to porous gel regions; (ii) retention of Al-Si glass domains within the gel layer, with water infiltrating inter-network spaces; and (iii) selective leaching of phosphorus over aluminum, leading to reorganization of the gel network.
IntroductionEthiopia’s livestock sector is critically vulnerable to a wide range of geological and hydrometeorological hazards that undermine animal health, productivity, and the livelihoods of pastoral communities. The country’s geographic location along the East African Rift System increases its susceptibility to geological threats such as volcanic eruptions, earthquakes, and landslides, while climate variability exacerbates hydrometeorological risks including droughts and floods.MethodsThis systematic review adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and employs a structured search strategy across major academic databases including Scopus, Web of Science, and Google Scholar. Studies were selected based on predefined inclusion and exclusion criteria to ensure the relevance and quality of the literature reviewed.ResultsThe review synthesizes findings from high-quality studies to qualitatively assess the compound impacts of geological and hydrometeorological hazards on livestock production in Ethiopia, particularly within pastoral and agro-pastoral systems. Drought emerges as the most significant hazard, with more than 6.8 million livestock deaths reported since 2020 due to successive failed rainy seasons. Floods have also caused severe damage; for instance, the 2006 flooding in the Southern Nations, Nationalities, and Peoples’ Region (SNNPR) resulted in the loss of approximately 15,600 livestock. In contrast, direct data on geological hazards such as volcanic eruptions and earthquakes remain limited, though their indirect effects—such as ashfall on grazing lands, water contamination, and disruption of grazing routes—further compromise livestock productivity and resilience.DiscussionThe review highlights critical gaps in data and research, particularly regarding the direct impacts of geological hazards. It identifies key adaptation and mitigation strategies, including early warning systems, hazard mapping, veterinary service enhancement, livestock diversification, and the promotion of livestock insurance schemes. Strengthening policy frameworks, community engagement, and economic instruments is essential to build resilience in the livestock sector. Evidence-based interventions are urgently needed to safeguard livelihoods, ensure food security, and promote sustainable adaptation in Ethiopia’s hazard-prone regions.
Abstract Sabkha environments are a prevalent topographic feature in arid coastal areas. Along the Arabian Gulf, sabkhas overlie substantial hydrocarbon reservoirs and exhibit intricate lithological characteristics and an extremely shallow water table. These factors contribute to elevated seismic velocities and signal distortion. Static correction, a crucial initial step in seismic reflection processing, is employed to mitigate the impact of shallow surface layers. In this study, we investigate the variations in seismic properties along the uppermost part of mature and developing sabkhas. We employed high‐resolution seismic experiments with geophone spacing of 10 cm to explore the upper tens of centimeters. Conventional surveys with a 2 m spacing complement this approach to investigate deeper layers. Both sabkhas exhibit a unique characteristic of a partially saturated zone, which affects the seismic velocity, leading to lower velocities and consequently influencing the accuracy of the static correction. The high‐resolution surveys demonstrated superior accuracy to conventional approaches in determining the top of the partial saturation zone and hardground layer, hence resulting in a more reliable velocity delineation. Moreover, velocities derived from conventional, replacement, and tomogram approaches resulted in unreliable static corrections in mature coastal sabkha compared with developing inland sabkha, attributed to the considerable geological complexity that is characteristic of mature coastal sabkha environments. Carrying out a high‐resolution seismic survey in sabkha environments is therefore necessary to mitigate near‐surface velocity effects.
Hao-Hsiang Ku, Shih-Cheng Yang, Huai-An Hsiao
et al.
In recent years, due to the rapid pace of urbanization and increasingly hectic modern lifestyles that leave little time for home cooking, more and more people prefer to dine at food stands, restaurants, or supermarkets due to convenience. This type of people are often called the eating-out population. The general public may have a concept that most of the food items consumed by people eating out are first prepared for storage by vendors and are likely to contain more food preservatives. Excessive exposure to benzoic acid (BA), sorbic acid (SA), and dehydroacetic acid (DHA), which account for the highest number of violations of the amount of preservatives permitted in food, may cause potential human health risk. The purpose of this study was to investigate the human health risks of consuming preservatives used in food among for Taiwanese people who eat out. We applied the total diet study (TDS) method to analyze the concentrations of BA, SA, and DHA in the food items frequently consumed when people dine outside. The hazard index in percent acceptable daily intake (%ADI) of BA and SA for four exposure groups classified by age were calculated. In high-intake consumers, the highest hazard index of BA was 2.5%ADI for the 6–9 years old age group of the eating-out population, which still fell within the acceptable risk range. In addition, the risk appeared to be decreasing year-on-year, which may be related to year-on-year improvements of the way food products are processed in the food industry.
<p>Air pollution adversely affects health, ecosystems, and infrastructure. In the <i>Western Balkans</i> (Albania, Bosnia and Herzegovina, Kosovo<span class="note-anchor" id="fna_Ch1.Footn1"><a href="#fn_Ch1.Footn1"><sup>1</sup></a></span>, Montenegro, the Republic of North Macedonia, and Serbia), the air pollution situation is more adverse than in the European Union in general. Understanding the air quality situation requires high-quality emission data with a high-resolution spatial distribution, especially for enabling remediation efforts, which is lacking in the Western Balkan region.</p>
<p>In this work, we have calculated air pollution emissions from the heating of individual housing units in the Western Balkan region. The basis for the dataset is a geographical dataset of buildings detected from satellite imagery by artificial intelligence (AI) methods. The building data have been combined with geospatial land-use datasets and statistical data for heating needs for residential buildings in the countries included and finally with emission factors to calculate the heating emissions.</p>
<p>Using this novel approach, the resulting datasets provide high-resolution heating emission data for common pollutants and are published as open data (<a href="https://doi.org/10.5281/zenodo.13906810">https://doi.org/10.5281/zenodo.13906810</a>, <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx2">Asker</a>, <a href="#bib1.bibx2">2024</a></span>). When comparing national totals for emissions, the datasets in this work are comparable to other, spatially coarser datasets, though the agreement strongly depends on the fuel usage data for each country/region.</p>
While three-dimensional measurement technology is spreading fast, its meaningful application to sedimentary geology still lacks content. Classical shape descriptors (such as axis ratios, circularity of projection) were not inherently three-dimensional, because no such technology existed. Recently a new class of three-dimensional descriptors, collectively referred to as mechanical descriptors has been introduced and applied for a broad range of sedimentary particles. First order mechanical descriptors (registered for each pebble as a pair $\{S,U\}$ of integers), refer to the respective numbers of stable and unstable static equilibria and can be reliably detected by hand experiments. However, they have limited ability of distinction as the majority of coastal pebbles fall into primary class $\{S,U\}=\{2,2\}$. Higher order mechanical descriptors offer a more refined distinction. However for the extraction of these descriptors (registered as graphs for each pebble) hand measurements are not an option and even computer-based extraction from 3D scans offers a formidable challenge. Here we not only describe and implement an algorithm to perform this task, but also apply it to a collection of 271 pebbles with various lithologies, illustrating that the application of higher order descriptors is a viable option for geomorphologists. We also show that the so-far uncharted connection between the two known secondary descriptors, the so-called Morse-Smale graph and the Reeb-graph can be established via a third order descriptor which we call the master graph.
Danilo Chamorro, Jiahua Zhao, Claire Birnie
et al.
Multi-channel Analysis of Surface Waves (MASW) is a seismic method employed to obtain useful information about shear-wave velocities in the near surface. A fundamental step in this methodology is the extraction of dispersion curves from dispersion spectra, which are obtained after applying specific processing algorithms onto the recorded shot gathers. Whilst this extraction process can be automated to some extent, it usually requires extensive quality control, which can be arduous for large datasets. We present a novel approach that leverages deep learning to identify a direct mapping between seismic shot gathers and their associated dispersion curves (for both fundamental and first modes), by-passing therefore the need to compute dispersion spectra. Given a site of interest, a set of 1D velocity and density models are created using prior knowledge of the local geology; pairs of seismic shot gathers and Rayleigh-wave phase dispersion curves are then numerically modeled and used to train a simplified residual network. The proposed approach is shown to achieve high quality predictions of dispersion curves on a synthetic test dataset and is, ultimately, successfully deployed on a field dataset. Various uncertainty quantification and CNN visualization techniques are also developed to assess the quality of the inference process and better understand the underlying learning process of the network. The predicted dispersion curves are finally inverted, and the resulting shear-wave velocity model is shown to be plausible and consistent with prior geological knowledge of the area.
Swagata Roy, Johannes P. Dürholt, Thomas S. Asche
et al.
The reactivity of silicates in an aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential can accurately capture silicate-water reactivity. The model was trained on a new dataset comprising 400,000 energies and forces of molecular clusters at the $ω$-B97XD def2-TVZP level. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. Our trained potential is found to reproduce static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.
Stuart D. C. Walsh, Laura Easton, Changlong Wang
et al.
Australia has ambitions to become a major global hydrogen producer by 2030. The establishment of Australia’s and the world’s hydrogen economy, however, will depend upon the availability of affordable and reliable hydrogen storage. Geological hydrogen storage is a practical solution for large scale storage requirements ensuring hydrogen supply can always meet demand, and excess renewable electricity can be stored for later use, improving electricity network reliability. Hosting thick, underground halite (salt) deposits and an abundance of onshore depleted gas fields, Australia is well placed to take advantage of geological hydrogen storage options to support its ambition of building a global hydrogen hub export industry. Using the Bluecap modelling software, we identify regions in Australia that are potentially profitable for large scale hydrogen production and storage. We use the results of this work to suggest high-potential regions for hydrogen development, supporting policymaker and investor decisions on the locations of new infrastructure and hydrogen projects in Australia.
<p>We estimate the snow depth and snow freeboard of Antarctic sea ice using a comprehensive retrieval method (referred to as CryoSat-2 Waveform Fitting for Antarctic sea ice, or CS2WFA) consisting of a physical waveform model and a waveform-fitting process that fits modeled waveforms to CryoSat-2 data.
These snow depth and snow freeboard estimates are combined with snow, sea ice, and sea water density values to calculate the sea ice thickness and volume over an 11<span class="inline-formula">+</span> year span between 2010 and 2021. We first compare our snow freeboard, snow depth, and sea ice thickness estimates to other altimetry- and ship-based observations and find good agreement overall in both along-track and monthly gridded comparisons. Some discrepancies exist in certain regions and seasons that are theorized to come from both sampling biases and the differing assumptions in the retrieval methods. We then present an 11<span class="inline-formula">+</span> year time series of sea ice thickness and volume both regionally and pan-Antarctic. This time series is used to uncover intra-decadal changes in the ice cover between 2010 and 2021, showing small, competing regional thickness changes of less than 0.5 cm yr<span class="inline-formula"><sup>−1</sup></span> in magnitude.
Finally, we place these thickness estimates in the context of a longer-term, snow freeboard-derived, laser–radar sea ice thickness time series that began with NASA's Ice, Cloud, and land Elevation Satellite (ICESat) and continues with ICESat-2 and contend that reconciling and validating this longer-term, multi-sensor time series will be important in better understanding changes in the Antarctic sea ice cover.</p>
Elif Özlem Arslan- Aydoğdu, Yağmur Avci, Nahdhoit Ahamada Rachid
et al.
Abiotic and biotic factors, especially microorganisms, play a role in the development of cave formations and the existence of unique characteristics of each cave. Due to the ecological conditions that characterize the cave environments, highly specialized microorganisms that are the main source of diverse bioactive compounds, inhabit these environments. The aim of this study is to determine the role and biotechnological potential of the bacteria isolated from Yarık Sinkhole located in Antalya (Turkey) by screening their ability to induce the CaCO3 precipitation, to hydrolyze urea, to induce calcite dissolution, and screening their possession of NRPS/PKS gene clusters. The most prevalent phylum is the Bacillota (synonym Firmicutes) (75.7 %), while the dominant species is Bacillus pumilus (33 %). All the isolates showed crystal formation on B4 agar medium, and the Energy dispersive X-Ray spectroscopy (EDS) analyses showed that the crystals are predominately composed of calcium, carbon and oxygen. Ninety-six (96 %) of our isolates have negative ureolytic activity. According to this result and having the ability to induce the CaCO3 precipitation, bacteria in this environment use other biosynthesis pathways than urea hydrolysis. MgCO3 and CaCO3 were dissolved by 61 % and 59 % of the isolates, respectively. In addition, 5.9 % and 53.7 % of the isolates showed the possession of PKS and NRPS genes, respectively. This result reveals that our isolates have high industrial and biotechnological potential. They may constitute good candidates for further biotechnological applications such as construction of bio-concretes, bioremediation, soil fertility, and production of biologically active secondary metabolites.
Abstract Sea surface salinity (SSS) is a master variable in oceanography and important to understand marine biogeochemical and physical processes. In the East China Sea (ECS), a random forest based regression ensemble model (RF) was developed to estimate the SSS with a spatial resolution of ∼1 km based on a large synchronous data set of in situ SSS observations, MODIS‐derived remote sensing reflectance (Rrs) and sea surface temperature (SST). The model showed the best performance when the Rrs(412), Rrs(488), Rrs(555), Rrs(667), SST and Julian day (JD) were used as inputs, with a root mean square error (RMSE) of 0.84, mean absolute error (MAE) of 0.31 and coefficient of determination (R2) of 0.81 for model training (N = 4,504), and a RMSE of 0.77, MAE of 0.30 and R2 of 0.86 for the model test (N = 1,153). The accuracy of the SSS model was examined using an independent data set during the period of 2020–2022 with a RMSE of 0.66 and MAE of 0.39 (N = 2,151). The interannual and seasonal signal of modeled SSS of the ECS, showed that important drivers of variability are the Changjiang discharge and the East‐Asian monsoon. Applications of the model to other Chinese marginal seas (Yellow and Bohai seas) showed good agreement in distribution patterns when compared with the estimated SSS from NASA Soil Moisture Active Passive. Once more empirical oceanographic data is made available, this robust model can be applied to other regions retraining the model with informed local data sets.