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Traces of stone working are an integral part of natural stone objects and artefacts of historical value. Each preserved trace does not only carry a value in determining the type of tool used, but also provides information about the historic stonemason’s work process and technology. For this reason, it is desirable to assess the restoration method’s influence on the change in surface topography. The effect of restoration interventions was investigated on five stone artefacts, three of ‘opuka’, one of sandstone and one of limestone, four of which showed historic working traces. For this purpose, selected restoration methods—chemical, mechanical and laser—were used. The examined artefacts were accurately photogrammetrically captured before and after the restoration interventions in order to assess and evaluate changes in the degree of preservation of the traces. Fine results using common tools were achieved in terms of geometric quality, level of detail and the documentation’s predictive power. The models’ geometric accuracy is in the single tenths of mm, as well as the matching of the two datasets (before and after).

Rock's surface micro-topography and geotechnical properties' limits are expected to be altered on weathering progress to a new form(s) and limits, respectively. The quantification of weathering damage for a given rock is of value e.g. to compute weathering rate, weathering intensity and rock's durability to weathering processes and to take a decision regarding restoration urgency. The current study aims to examine the variation/constancy of micro-topography and geotechnical properties’ limits of the Triassic sandstone constituting well-aged wall, at Aachen city, on weathering progress over short duration (12 years of investigation from 2007 to 2019). The Equotip hardness tester and micro-erosion meter are tools used for micro-topographic and rock's surface hardness investigations on one hand, and the mercury intrusion porosimetry has been used for measuring rock's pore size distribution and salt susceptibility on the other hand. These tools are accurate, numerical, comprehensive, easily applicable and preferable particularly for ancient buildings and archaeological sites where sampling is not recommended or prohibited. The wall side under consideration has been selected as its constructional blocks present a wide spectrum of weathering forms as well as rock's surface micro-topography (over 12 years of investigation) through increasing the weathering forms' dimensions and/or creation of new weathering forms. The net result of the current study indicated a noticeable variation in stone's micro-topography on weathering progress, the rock's stone surface hardness highly controls rock's weathering susceptibility and no clear relation can be noted between stone's surface roughness and its susceptibility. The pore size distribution is the most controlling parameter for rock's weathering susceptibility.

 and depictive representations produced by drawing—known from Europe, Africa and Southeast Asia after 40,000 years ago—are a prime indicator of modern cognition and behaviour 1 . Here we report a cross-hatched pattern drawn with an ochre crayon on a ground silcrete flake recovered from approximately 73,000-year-old Middle Stone Age levels at Blombos Cave, South Africa. Our microscopic and chemical analyses of the pattern confirm that red ochre pigment was intentionally applied to the flake with an ochre crayon. The object comes from a level associated with stone tools of the Still Bay techno-complex that has previously yielded shell beads, cross-hatched engravings on ochre pieces and a variety of innovative technologies 2 – 5 . This notable discovery pre-dates the earliest previously known abstract and figurative drawings by at least 30,000 years. This drawing demonstrates the ability of early Homo sapiens in southern Africa to produce graphic designs on various media using different techniques. A silcrete flake with a 73,000-year-old cross-hatched ochre drawing, from Blombos Cave, South Africa, demonstrates that early Homo sapiens used a range of media and techniques to produce graphic representations.

The restorative cleaning of natural stones has a special significance for the preservation of important cultural assets or the slowing of their deterioration. Organisms such as fungi, lichens or mosses, but also emission dirt such as soot soften and otherwise damage both the surface and the internal structure of the building stone. In order to quantify the effects and in particular the abrasiveness of selected cleaning methods, cleaning experiments were carried out on six different naturally and artificially weathered rocks using cold water under high pressure, hot water under high pressure as well as hot-water steam. The types of rocks studied include marble, limestone, granite, sandstone and tuff. Surface changes in roughness and topography were quantified using two surface-sensitive methods: confocal microscopy as well as 3D shadow triangulation. The two high-pressure cleaning methods were found to have a significantly stronger abrasive effect than steam cleaning when the distances were too small. The cleaning performance, which was compared using biologically weathered samples, was lowest for steam cleaning. However, the high temperatures of the steam also permanently eliminate much of the biological matter on and under the surface, as observed in the field test. The results presented should make it possible for the conservator to assess, which cleaning procedures to use for the different rock varieties depending on the degree of weathering.

The study of human remains can provide valuable information about aspects of past populations. Here we present an updated database consisting of 590 radiocarbon (14C) dates for human remains from Holocene South Africa before European contact. We calibrated or recalibrated all the previously published dates using the most recent calibration curve for the southern hemisphere. Each date is roughly georeferenced and plotted according to their Stone Age or Iron Age contexts, revealing the broad distribution pattern of dated Holocene human remains across South Africa—perhaps reflecting aspects of past population distribution and densities, but also underscoring historical collection practices, archaeological research focus, and preservation conditions. We use Kernel Density Estimation models to show peaks and troughs of dated remains through time, with Later Stone Age peaks at ∼5.5 ka cal BP, ∼2 ka cal BP and ∼0.5 ka cal BP, and Iron Age peaks ∼1.1 ka cal BP and ∼0.5 ka cal BP, some of which show broad correspondence to climatic data. Our data, based on dated remains only, do not provide a full reflection of past populations, and our large-scale, coarse-grained analysis cannot yet assess the reasons for the peaks in dated human remains in detail. Yet, the study provides a new resource, and a data-driven overview that highlights aspects to be explored with further contextual analyses against the available archaeological records, population histories and climatic indicators through time and across space.

The Sinemurian/Pliensbachian rifting phase produced isolated topographic highs and more spatially continuous rift shoulders, where the Caloveto Group developed in the form of narrow shallow-water fringing carbonate bodies, locally reefal (Santantonio 2012; Innamorati and Santantonio 2018; Santantonio and Fabbi 2020), along the borders of footwall blocks. A further pulse of extension in the Toarcian caused the fragmentation and drowning of the benthic carbonate factories, and widespread onset of pelagic sedimentation in the form of an Ammonitico Rosso facies, which evolved into the already mentioned Sant’Onofrio Subgroup, unconformably draping the rugged submarine topographic surface. The age of the ammonites allows to constrain the timing of the rock failure episode, which must be bracketed between the late Pliensbachian (age of the shallow-water limestone) and the latest Toarcian, falling therefore in a time interval of widespread extensional tectonic activity, and consequent sea-floor instability, in the Longobucco Basin (Santantonio et al. 2016; Santantonio and Fabbi 2020). The uniqueness of the Il Torno Geosite lies in the exceptional preservation of fine details referable to the submarine topography of a Jurassic basin-margin affected by a catastrophic collapse, where the slide scar and the main body of a mass transport deposit, made of megaclasts of granite and limestone, are directly observable, with the original spatial relationships unmolested, so that a geologist can literally walk on them (Fig. 1).

The origin of the Yellowstone and Snake River Plain volcanism has been strongly debated. The mantle plume model successfully explains the age-progressive volcanic track, but a deep plume structure has been absent in seismic imaging. Here I apply diffractional tomography to receiver functions recorded at USArray stations to map high-resolution topography of mantle transition-zone discontinuities. The images reveal a trail of anomalies that closely follow the surface hotspot track and correlate well with a seismic wave-speed gap in the subducting Farallon slab. This observation contradicts the plume model, which requires anomalies in the mid mantle to be confined in a narrow region directly beneath the present-day Yellowstone caldera. I propose an alternative interpretation of the Yellowstone volcanism. About 16 million years ago, a section of young slab that had broken off from a subducted spreading centre in the mantle first penetrated the 660 km discontinuity beneath Oregon and Idaho, and pulled down older stagnant slab. Slab tearing occurred along pre-existing fracture zones and propagated northeastward. This reversed-polarity subduction generated passive upwellings from the lower mantle, which ascended through a water-rich mantle transition zone to produce melting and age-progressive volcanism.

Detecting submerged targets in shallow waters from satellite platforms remains a challenge, as the optical spectral information of targets is significantly distorted by the absorption and scattering effects of the water column. In this study, we propose a new framework as the bathymetry‐informed target extraction, which integrates the spaceborne lidar data and multispectral imagery. By using lidar assisted Satellite‐Derived Bathymetry model, we convert the complex multispectral information into relative depth data. Through this transformation, the challenging issue of distorted color domain image segmentation is converted into the task of depth anomaly detection. The method is validated on submerged artificial stone weirs and breakwaters in typical open ocean and coastal waters, which indicates significant improvements in target detection rate and reliability compared to direct color‐based methods. This approach promises large‐scale surveys of submerged targets in shallow waters, offering an alternative solution to on‐site surveys such as shipborne sonars.

We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but “distance to road” was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas.

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0–20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index—TRI) and remotely sensed data (ferric oxides, normalized difference moisture index—NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area.