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Peter Olson savours a study of Earth's protective magnetic field -- and the people who discovered it.

The Early Permian (275.3 ± 3.1 Ma) .sup.40Ar/.sup.39Ar plateau age of detrital mica (Cr-phengite) from clasts in listvenite sandstones of the ophiolite-derived clastic sequence of the Rassokha Terrane of the Chersky Range probably corresponds (or is close) to the time of the formation of listvenites of the provenance (ophiolitic massifs of the range) and restricts the maximum deposition age of clastic rocks. A partial loss of Ar by mica as a result of deformations and postsedimentation transformation of rocks of the ophiolite-derived clastic sequence in the Early Permian is possible but it is less probable.

Many volcanological and geoarchaeological studies in the ancient city of Pompeii (Italy) have been devoted to the 79 CE Plinian eruption of Vesuvius, which sealed the city under a thick pyroclastic sequence. Only fragmentary information exists regarding the stratigraphy of the volcanic sediments sandwiched between the 79 CE street level and the volcanic rocks that form the geological framework of the hill on which Pompeii was built, which constitutes the “Pompeii bedrock”. The stratigraphic survey of twenty-one trenches throughout the city, coupled with a geochemical characterization, highlighted that the pre-79 CE stratigraphy includes at least eight late Pleistocene to Holocene tephra layers. Six eruptions were sourced from Somma–Vesuvius (Pomici di Base, Mercato, AP1 to AP4) and two originated from Campi Flegrei (Neapolitan Yellow Tuff and Soccavo 4). The Pompeii bedrock is the product of local vents, the last activity of which possibly shortly predates the 22 ka Pomici di Base eruption. From a geoarchaeological perspective, a relevant result is the absence of the 3.9 ka Avellino tephra in all trenches. This evidence, along with the reappraisal of the stratigraphy of the nearby archaeological site of S. Abbondio, suggests that the Avellino eruption possibly only marginally affected the Pompeii area during the Early Bronze Age.

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning. Complex Earth system challenges can be addressed by incorporating spatial and temporal context into machine learning, especially via deep learning, and further by combining with physical models into hybrid models.

The representation of subsurface structures is an essential aspect of a wide variety of geoscientific investigations and applications, ranging from geofluid reservoir studies, over raw material investigations, to geosequestration, as well as many branches of geoscientific research and applications in geological surveys. A wide range of methods exist to generate geological models. However, the powerful methods are behind a paywall in expensive commercial packages. We present here a full open-source geomodeling method, based on an implicit potential-field interpolation approach. The interpolation algorithm is comparable to implementations in commercial packages and capable of constructing complex full 3-D geological models, including fault networks, fault–surface interactions, unconformities and dome structures. This algorithm is implemented in the programming language Python, making use of a highly efficient underlying library for efficient code generation (Theano) that enables a direct execution on GPUs. The functionality can be separated into the core aspects required to generate 3-D geological models and additional assets for advanced scientific investigations. These assets provide the full power behind our approach, as they enable the link to machine-learning and Bayesian inference frameworks and thus a path to stochastic geological modeling and inversions. In addition, we provide methods to analyze model topology and to compute gravity fields on the basis of the geological models and assigned density values. In summary, we provide a basis for open scientific research using geological models, with the aim to foster reproducible research in the field of geomodeling.

The formation and preservation of cratons—the oldest parts of the continents, comprising over 60 per cent of the continental landmass—remains an enduring problem. Key to craton development is how and when the thick strong mantle roots that underlie these regions formed and evolved. Peridotite melting residues forming cratonic lithospheric roots mostly originated via relatively low-pressure melting and were subsequently transported to greater depth by thickening produced by lateral accretion and compression. The longest-lived cratons were assembled during Mesoarchean and Palaeoproterozoic times, creating the stable mantle roots 150 to 250 kilometres thick that are critical to preserving Earth’s early continents and central to defining the cratons, although we extend the definition of cratons to include extensive regions of long-stable Mesoproterozoic crust also underpinned by thick lithospheric roots. The production of widespread thick and strong lithosphere via the process of orogenic thickening, possibly in several cycles, was fundamental to the eventual emergence of extensive continental landmasses—the cratons. Cratons are the oldest parts of the Earth’s continents; this Review concludes that the production of widespread, thick and strong lithosphere via the process of orogenic thickening was fundamental to the eventual emergence of extensive continental landmasses.

There is an abrupt transition from alkaline to acid soil pH when mean annual precipitation exceeds mean annual potential evapotranspiration, demonstrating that climate creates a nonlinear pattern in soil solution chemistry at the global scale. Climate creates a global threshold in soil chemistry Soil pH affects nutrient supply and storage in soils and, in consequence, can influence plant productivity. Transitions from alkaline to acid soils are caused by variations in the water balance across natural climate gradients, but a global-scale investigation of the influence of climate on soil pH is lacking. Here Eric Slessarev et al . evaluate the global relationship between water balance and soil pH, and find that there is an abrupt transition from alkaline to acid soil pH that occurs where mean annual precipitation begins to exceed mean annual potential evapotranspiration. The authors suggest that deviations from the observed global pattern may result from seasonality, climate history, erosion and mineralogy. Soil pH regulates the capacity of soils to store and supply nutrients, and thus contributes substantially to controlling productivity in terrestrial ecosystems 1 . However, soil pH is not an independent regulator of soil fertility—rather, it is ultimately controlled by environmental forcing. In particular, small changes in water balance cause a steep transition from alkaline to acid soils across natural climate gradients 2 , 3 . Although the processes governing this threshold in soil pH are well understood, the threshold has not been quantified at the global scale, where the influence of climate may be confounded by the effects of topography and mineralogy. Here we evaluate the global relationship between water balance and soil pH by extracting a spatially random sample ( n  = 20,000) from an extensive compilation of 60,291 soil pH measurements. We show that there is an abrupt transition from alkaline to acid soil pH that occurs at the point where mean annual precipitation begins to exceed mean annual potential evapotranspiration. We evaluate deviations from this global pattern, showing that they may result from seasonality, climate history, erosion and mineralogy. These results demonstrate that climate creates a nonlinear pattern in soil solution chemistry at the global scale; they also reveal conditions under which soils maintain pH out of equilibrium with modern climate.

The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.

On January 1, 2024, an Mw 7.5 reverse-fault earthquake occurred along the submarine active faults just offshore of the Noto Peninsula in central Japan. This earthquake is one of the largest inland crustal earthquakes that ever recorded in Japan. We performed inversions of near-field strong-motion waveforms (0.05-0.25 Hz) to investigate the kinematic rupture process of this earthquake. For the inversions, we assumed listric fault planes, where the dip angles are steeper near the seafloor and become gentler with increased depth, based on the location of the submarine fault offsets, the structure at shallow depths revealed by seismic reflection surveys, and the relocated aftershock distribution at deep depths. To constrain our kinematic source model, we also utilized geodetic information published by the Geospatial Information Authority of Japan. We identified that the rupture process had three phases. The first phase was the initial rupture with a small slip of 2 m around the rupture initiation area. After several seconds, the second and third phases, which were the main ruptures, propagated in the southwest and northeast directions, respectively. During the second and third phases, five large slip areas with average slips of 5-6 m significantly contributed to the strong-motion waveforms. By forward-simulating the geodetic data, we found that the rupture at shallow depths included an oblique component close to a right-lateral component and that, in the eastern part of the source region, the rupture transferred from a southeastward-dipping fault to a northwestward-dipping fault. The listric fault with high dip angles at shallow depths is better than a planar fault with a single dip angle for accurately modeling the fault slip near the surface and the coseismic displacement close to the fault trace during the reverse-fault earthquake.