Geofísica

Magmas are the ultimate source of volcanism on Earth. At high-temperatures these complex silicate melts may easily chaotically mix, on their way towards the surface of the planet.

Banded iron formations (BIFs) are chemical sedimentary rocks commonly utilized for exploring the chemistry and redox state of the Precambrian ocean. Despite their significance, many aspects regarding the crystallization pathways of iron oxides in BIFs remain loosely constrained. In this study, we combine magnetic properties characterization with high-resolution optical and electron imaging of finely laminated BIFs from the 2.7 Ga Carajás Formation, Brazil, to investigate their nature and potential for preserving ancient environmental conditions.

Seismic azimuthal anisotropy within the crust and upper mantle offers important information of past and present tectonic deformation. We used ambient seismic noise to map azimuthal anisotropy in the lithosphere beneath W and SE Brazil, providing new insights into the amalgamation history of the various cratonic blocks in SW Gondwana, which are now partly buried by Phanerozoic basins. We used 72 stations from January 2016 to September 2018. To correct the non-uniform distribution of the energy flow around each inter-station path, the weighted rms (WRMS) stacking method was applied.

The Uberaba Formation, a significant paleontological site within the Bauru Group, was deposited over the Early Cretaceous volcanic rocks of the Paraná Basin in southern Brazil. It occupies a restricted northeastern area of the basin surrounding the homonymous city. Recent works on stratigraphy and paleontology indicate a Campanian age, a novel finding that challenges the previously held belief of a Cenomanian age based on dinosaur bones.

Road traffic is one of the main sources of particulate matter in the urban environment, emitting particulate organic and elemental carbon compounds and metal-rich particles through combustion and brakes and tires wear. In Western Africa, the carbon and metal composition of airborne particles is also influenced by additional sources linked to biomass combustion and recent industrialization.

Metallic structures are a common noise source in Slingram electromagnetic surveys because they interact with the primary magnetic field generated by the transmitter coil and create a secondary magnetic field that distorts the apparent conductivity readings from a layered or homogeneous subsurface. The main challenge is understanding how their influence varies depending on the loop configuration and the distance at which the surface structure no longer distorts the apparent conductivity readings.

In this study, we present a new methodology for 2D joint inversion, or data fusion, of DC electro-resistivity and Transient Electromagnetic methods. These geophysical techniques have traditionally been used separately, but by combining them, we aim to decrease ambiguities and increase the robustness of the resulting subsurface model. The inversion process was conducted using the classical Occam method with smooth models and synthetic studies were also conducted to understand the limitations and advantages of the method.

Unveiling the tempo and mode of animal evolution is necessary to understand the links between environmental changes and biological innovation. Although the earliest unambiguous metazoan fossils date to the late Ediacaran period, molecular clock estimates agree that the last common ancestor (LCA) of all extant animals emerged ~850 Ma, in the Tonian period, before the oldest evidence for widespread ocean oxygenation at ~635–560 Ma in the Ediacaran period. Metazoans are aerobic organisms, that is, they are dependent on oxygen to survive.

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning.

In February 2023, anomalously heavy rainfall caused widespread landslides in the coastal city of São Sebastião (Southeastern Brazil). This report describes the first version of a landslide inventory dataset for this event. The inventory is primarily based on the analysis of aerial images with 10 cm spatial resolution acquired immediately after the event, as well as archive images from Google Earth and PlanetScope. Delimitation of the landslides relied on a comparison of the images along with the area’s Digital Surface Model (DSM) and hydrography.