Virtual reality systems have been extending their usability in many areas that could not be contemplated before. One of these fields is paleontology, which can now use virtual reality to build realistic models of real fossils with many goals, including preservation of originals, optimized visualization, or even for restoring missing parts. In this paper we present an immersive system where a entire scenario is reconstructed using digital photogrammetry and Mosis LAB application. The system has shown useful for many applications from touristic purposes and paleontological studies, since the immersive system provides an increased sense of manipulation, as well as the possibility of detailed inspection both inside and outside structures.

Hyperspectral images often have low spatial resolution due to the sensor sizes required to capture the required spectral response. Super-resolution (SR) techniques try to mitigate this by injecting more detail in the upscaled image, either with numerical methods or deep learning and Convolution Neural Networks. In the deep learning methods, the models learn image details by inferring a high-resolution (HR) image from a synthetic low-resolution (LR) image that simulates the natural degradation of sensors by applying resampling (to reduce the image detail) and noising (to add small errors and interference). Often disregarded in the literature, the resampling method applied to generate the synthetic image can impact greatly the deep learning model training. This work, evaluate several resampling techniques to measure this impact using the Harvard hyperspectral dataset. Results showed that the Lanczos filter was the best among eight other resampling methods. The Nemenyi and Friedman ranking statistical tests also indicated that the Cubic-Spline, Bicubic, and RMS achieved good results.

The study of outcrops is one of the main ways of carrying out research in geology, because it offers direct analysis on the behavior of rocks. Thus, the study of analogous outcrops has been used by the oil industry, as they can be associated with other data and offer a more effective analysis in geological modeling of reservoir rocks. With the advancement of technology in recent years, the use of virtual environments has been gaining more space in geosciences, as they allow the user to analyse areas that are difficult to access and allow structural and geometric analysis of real-scale outcrops. Even though the use of Digital Outcrop Models (DOM) is becoming popular, the industry still suffers from a lack of software appropriate to interpret digital outcrops. In this paper we assess the performance of DOM to classify Lithofacies on out-crop of carbonate rocks in northeastern Brazil by using Mosis XP tools. Mosis XP is a software developed by Vizlab - X-Reality and GeoInformatics Lab specifically to perform DOM analysis and interpretation. Results confirm that texture and color attributes were satisfatory preserved in the virtual representation. Also, interpretation tasks were promising since allowed correctly identification of at least four distinct facies over the outcrop profile.

Kerogen type in source rocks is directly related to its hydrocarbon generation potential. Its determination is often carried out with destructive methods. This study presents a non-destructive technique as an alternative to determine kerogen type using hyperspectral data and machine learning techniques. To present the technique, models were training using Support Vector Machines, K Nearest Neighbors, and Random Forest classifiers on spectral data collected in rock samples acquired from Taubaté Basin, Brazil, of an outcrop with high hydrocarbon generation potential. The models were trained and evaluated using spectral signatures measured with a spectroradiometer and the results were also tested on hyperspectral images of the samples. The experiments described here achieved accuracy above 0.8 with precision and recall above 0.62 and 0.8, respectively, for every kerogen type, indicating the soundness of the classification.

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).

This paper presents VizSpectralData, a web based application that runs entirely in the front end and allows spectral data from csv files to be opened, visualized and processed. The system is presented together with the algorithms it implements using real data collected from several carbonate rock samples. It is an open source alternative for simple visualization and processing to proprietary softwares, it is developed in javascript, html and css. It has features to visualize the reflectance, continuum removed spectra, and the derivative of the spectra. It allows to import and export spectral libraries in CSV format.

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).

New sensors aboard recently launched satellites have induced the development of several measures aimed to indicate the presence of many materials over the Earth. Karsts are places rich in carbonate rocks and present large economic and environmental importance. This paper aimed at assessing the performance and consistency of different carbonate estimators derived from orbital images acquired over a controlled karst area. Experiments were assisted by a multi-scaled reference data built through a high spatial resolution Unmanned Aerial Vehicle (UAV) image acquired over the selected area. Results show a considerable unconformity among selected measures and better performance presented by indices exploiting measures along visible and infrared spectral regions.