The digital geoscience revolution is modifying the technologies geoscientists use to acquire and process data with the coming of digital outcrop models, hyperspectral data among many methods. These improvements in technology create new challenges in visualization, manipulation, and modeling of the data acquired, opening new research possibilities. In this paper, we present a novel system to visualize, manipulate and correlate geochemical and hyperspectral data, Digital Outcrop Models, and 3D rock samples using state-of-the-art immersive virtual reality techniques. We present a study case using a visualization and data set on an open pit quarry outcrop of a potential analog for hydrocarbon source rocks from Tremembé Formation (Taubaté Basin, Brazil).

The present study explores the capabilities of a low-cost and standard drone for the generation of a mixed dataset processed as an orthophotomosaic, and surface exposure mesh over the inaccessible wave-washed Wildlife Refuge of Ilha dos Lobos in the southern Brazilian coast. The area comprises an important Marine Protected Area used as a seasonal resting refuge of South American sea lions and fur seals that is continuously washed by wave actions, restricting human access for conventional field mapping. The results have estimated a total surface exposure of the island of 20,806.8 m2, which is an increase of 14.01 % on the total area relative to the most exposed single survey done so far. In futures works, these results will provide data for the geological characterization of the island, which will increase the information on the environment given support to the Wildlife Refuge of Ilha dos Lobos management plan.

Fractures affect the intrinsic properties of permeability and porosity of reservoir geobodies, making its network characterization an important task for fluid flow modeling. Direct acquisition of data on reservoirs is labor-intensive and generally produces sparse information. Thus, the study of analogue outcrops with similar characteristics is often carried out by using unmanned aerial vehicle image acquisition and digital photogrammetry. However, the accurate automatic recognition of the fractures network over the outcrop images remains a challenge. Image segmentation methods based on convolution neural networks (CNNs) were successfully applied in medicine, biology, and other areas, however, not yet in geological fracture detection. This work proposes the validation of two popular CNNs - Segnet and Unet - for pixel-to-pixel segmentation targeting fracture detection. Initial results showed acceptable scores of the metrics mean intersection over union (mIoU) and dice intersection (F1) in both CNNs.

Very high spatial resolution data seems to reach its maximum for orbital images due to unavoidable atmospheric interactions. At the same time, special hyperspectral cameras are being developed to operate on-board manned or unmanned aircrafts at a fixed optics, which prevents its using for imaging near objects in laboratory conditions. Both limitations can only be surpassed by using super-resolution principles. In this paper, we present a multi-looking approach for enhancing the spatial resolution of images acquired by systems that exhausted their natural ability to provide finer images. The method exploits multiple image takes with controlled spatial differences to produce a higher resolution output. Experiments with static hyperspectral sensor and synthetic data have proven the approach is sound and robust to many applications (e.g., rock samples).

In this paper we propose a new approach to automatically detect and extract fractures as well as estimate the aperture measures from orbital/aerial images. We show, in a first step, the capability of a translation invariant wavelet multiscale decomposition (NDWT) to separate the information related to the fractures from other features in the image. In the second stage, the aperture size (widths of the fractures) in different positions are estimated across scales using curvature analysis. In a third step, the fractures can be automatically extracted using a growing algorithm. Besides the measures of the fracture apertures in an image of Thingvellir in Iceland, we showed the correlation between the fractures of interest extracted automatically and the respective extracted manually were high (0.9) while only 51 % of then were extracted using just curvature analysis and growing algorithm (without NDWT).

In conventional work environments, visualization and integration of correlated data has always been limited by the amount of software windows a regular PC can display. On the other hand, immersive virtual environments present possibilities of interaction that fit scalable and adaptable three-dimensional space, allowing the integration of different types of data, and relating information. This article describes VROffice, an immersive virtual reality office to handle and correlate georeferenced data. VROffice is an immersive and interactive virtual reality office used to handle and correlate georeferenced data, providing access to 2D and 3D elements (i.e rocks, lab samples, organisms) organized as a virtual library. These objects work as data containers ready to store and display data from its location. In the virtual office, data is visualized in a way that allows different forms of interactions, such as observation, free-hand manipulation, inspection of details and object location in a 3D georeferenced space. Users can also organize datasets, perform analysis between different types of objects, combine characteristics, and interpret data in a Geographic Information System (GIS) environment. It enables different forms of interaction with potential to improve insights over observations, instantly turning multivariable datasets into intuitive immersive displays. Different forms of interaction, as well as UI (user interface) and UX (user experience) decisions have been customized to ensure that the experience in using the system is as comfortable as possible. Therefore, in order to demonstrate the possibilities of its use, case studies were created in two different fields of knowledge, geology and biology, which have georeferenced data available.

This work proposes a workflow methodology that acquires and processes Google Earth imagery to generate 3D Virtual Field Environments (VFE) using SfM algorithm processing software. These models can be visualized in MOSIS V2 (Multi Outcrop Sharing and interpretation System - Vizlab) software and can transport earth science academics to any high-resolution environment from Google Earth in immersive virtual reality (iVR) with a set of geological tools for interpretation and teaching.

This work evaluates robust statistical assessments for 3D models generated by the process of photogrammetry using images captured from Google Earth screenshots in a method named Printgrammetry. This method can make 3D models more accessible to be explored in other ways, including virtual reality and use in other programs with incorporated tools that are not available in the Google Earth. To verify the quality of the Printgrammetry model we proposed the use of skewness-adjusted robust statistics, an approach that avoids the exclusion of relevant data. The results confirmed that the proposed outlier detection method is more realistic to the skewness of the data than the usual outlier detection methods. The robust statistical methods adequate to our data assessed the quality of the Printgrammetry model.

The virtual reality is becoming an important asset in the recent years bringing immersion to simple tasks as gaming, modeling, and learning. Following this trend, the MOSIS (Multi Outcrop Sharing and Interpretation System) was created to help earth scientists and other users to visualize and study outcrops, important sources of information to the Oil & Gas industry. Basing on the user's feedback some updates were identified to improve the user experience. Thus, a new version of the software (MOSIS V2) was developed, with updates in both, interface and toolset. New possibilities and the model scalability were introduced in the toolset. MOSIS V2 was well graded in SUS (System Usability Scale) [1] which has validated the changes that were made.

For the past decades, environmental studies have been mostly a field activity, especially when concerning geosciences, where rock exposures could not be represented or taken into laboratories. Besides that, VR (Virtual Reality) is growing in many academic areas as an important technology to represent 3D objects, bringing immersion to the most simple tasks. Following that trend, MOSIS (Multi Outcrop Sharing and Interpretation System) was created to help earth scientists and other users to visualize and study VFEs (Virtual Field Environments) from all over the world in immersive virtual reality.