Lithological characterization plays a crucial role in geological studies, and outcrops serve as the primary source of geological information. Automatic identification of lithologies enhances geological mapping and reduces costs and risks associated with mapping less accessible outcrops. These outcrops are typically imaged using remote sensing techniques, enabling the identification of geological structures and lithologies through computer vision and machine learning (ML) approaches. In this context, convolutional neural networks (CNNs) have significantly contributed to lithological characterization in outcrop images. Recent advancements include novel architectures based on residual and attention blocks, which improve upon base CNN models. In addition, transformer-based architectures have surpassed CNNs in various tasks. Taking a step further, we propose a novel architecture that incorporates Fourier operators. Our proposed architecture builds upon the Transformer model, utilizing a sequential combination of Fourier neural operators (FNOs) and channelwise self-attention layers. To train our model, we adopt a transfer learning strategy, initially training it on a texture dataset with 47 classes. Subsequently, we fine-tune the same model to classify five specific lithologies in our custom dataset. These lithologies include sandstone, gray and brownish-gray shale, limestone, and laminated limestone images from the Tres Irmãos quarry within the Araripe Basin—an outcrop analogous to oil exploration reservoirs. The proposed architecture achieved an F1-score of up to 98%, performing better than reference CNN and ResNet models. This advancement holds promise for accurate lithological characterization, benefiting geological research and exploration efforts.

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.

Most fracture properties, such as orientation and density, are acquired by interpreting data obtained at the wellbores, while fracture properties between wells are typically derived from seismic data. However, this information is sparse or has low resolution leading to the study and analysis of outcrops. The data acquisition in outcrops is facilitated by its 3D representation in Digital Outcrop Models (DOM) obtained from LiDAR and Photogrammetry. It also allows virtual interpretation and fracture detection. In either case, the 3D fracture data need to be clustered in fracture sets to allow the statistical analysis necessary to upscale and resample the DFN. This clusterization is carried out by methods like k-means and fuzzy, however with caveats, like the prior definition of the number of clusters. In this work, we propose the use of agglomerative hierarchical clustering to cluster 3D fracture data obtained by a KD-Tree segmentation algorithm. Results showed that when compared to k-means, the proposed method presented more compact clusters and balance when considering Fisher’s statistics of dispersion.

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.

Improvement in spatial resolution of remote sensing images will always be a hot-topic since the sharpening of image objects is mandatory for many appications. Even thought image instruments have received great improvements in the recent years, super-resolution techniques are welcome to increase even more the level of quality of the data for further interpretations. In this paper we present a multi-frame super-resolution approach that uses a set of UAV images acquired at the same spot, but with slight different perspectives caused by random fluctuations. The small off-sets between consecutive pixels of the low spatial resolution images are considered at subpixel level to feed an arithmetic system of equations able to produce high resolution pixels, which are the unknowns of the system. The results are soundness and could show visual enhancement. However, further developments is need to in deep undertanding and possible advancement of the designed approach.

Monitoring the available volume of raw water in reservoirs is essential to ensure security of supply to the population. Climate change and global warming had substantially changed the weather bringing constant droughts and unexpected rainfalls, making the correct estimation of water bodies an important matter. The technique for recognizing water bodies is called bathymetry, where by a sonar and embedded global positioning system (GPS) collect data to recreate the depth topography. However, this work is costly and exposes the assessment team to health risks. Therefore, we are proposing a prototype of a semi-automated portable bathymetry equipment to facilitate data collection and thus enable more frequent updates of reservoir volume. The objectives of this work were to construct a reduced model of a vessel and to calibrate an adapted ultrasonic sensor for depth measurement. The ultrasonic sensor mounted on an ARDUINO micro-controller was tested in the FATEC-Jahu's test tank and its readings were compared against measurements made manually. We conclude that the reduced model is stable for data collection with an operational range between 1.0 and 20.0 meters when considering a linear adjustment of the sensor readings achieving an R2 score of 0.9998.

Reservoir rocks have intrinsic properties of porosity and per-meability that make them natural hosts for hydrocarbons, and the estimation of these properties is key to simulating reser-voir models. Additionally, fractures can greatly influence the fluid flow within the reservoir. However, fracture data from reservoirs is often disregarded due to its sub-seismic scale. In this matter, analogue outcrops play an important role in Dis-crete Fracture Network (DFN) modeling which represents the fracture data in terms of their attribute distribution allowing the scaling of the fracture data to reservoir scale which is done mostly by stochastic methods. Recent works have used Gen-erative Adversarial Networks (GANs) to scale porosity data from images, and we propose its use in fracture data from images acquired from Unmanned Aerial Vehicles (UAV) em-ploying a Deep Convolutional GAN (DC-GAN). We obtained similar attribute distribution for fracture aperture showing the viability to replicate statistical behavior.

Exposed rock formations, also know as outcrops, are a great source of geological data, enabling the characterization of fracture networks that are less visible when analyzing reservoir data from seismic and well logs. Remote sensing techniques like LiDAR and UAV Photogrammetry allow the generation of Digital Outcrop Models (DOM) that provide accurate outcrop geometry enabling further investigation of fracture attributes, necessary when creating Discrete Fracture Network (DFN) models and reservoir simulation models. Besides the manual fracture interpretation in DOMs, automatic fracture plane segmentation and clusterization can provide significantly more data. However, the identification of family sets is still done visually in most cases. In this work, we develop a modification to the elbow method using spherical Fisher statistics of dispersion to determine the ideal number of clusters or fracture family sets. We employed this method to cluster fracture families in a point cloud DOM obtained from UAV-SfM processing of a flight over a carbonate outcrop in the Jandaira formation, Northeast Brazil, obtaining clusters with low dispersion values.

Carbonate outcrops are widely studied due to their character-istic analogous to oil and gas reservoirs. Analyzes made with data from these outcrops are used to assist in the description of reservoirs located at great depths. These rocky expo-sures can be easily detected in satellite images using machine learning. Considering the diversity of techniques used in the identification/classification of features on the satellite image data, this work aimed to evaluate the best machine learning technique for identifying carbonate outcrops in Landsat 8 data, considering the carbonate outcrops from the Jandaíra formation, in northeast Brazil. We employed techniques like Grid Search, cross-validation, and validation statistics like Friedman and Nemenyi. Considering the metrics F1 score and Mathews Correlation Coefficient, the MLP technique presented the best results, achieving a F1 score of 0.823, against the SVM with a F1 score of 0.810 and the Random Forest technique with a F1 score of 0.812.

Differently form RGB photos, multi-frame acquisitions of hyperspectral data are unfeasible due to the pushbroom mechanisms of hyperspectral sensors. Therefore, 3D digi-tal surface models including good quality hyperspectral in-formation are available only by integrating 2D images to the point cloud generated using photogrametric cameras or laser scanners. However, geometric differences presented by sen-sor mechanisms resulting in varied perspectives of the same objects present on the scene deeply harm the spatial registration, and thus, the post integration procedure. In this paper we suggest a pre-processing step aiming to adjust objects to the same geometric perspective in order to later integrate the RGB cloud mesh to the hyperspectral data in a more efficient manner. We firstly build the RGB point cloud for later simu-lating the same perspective of push-broom sensor generating a RGB orthomosaic similar to the hyperspectral image. Pre-liminary results indicate an increasing in the number of tie points when registering is performed using the resulting syn-thetic sampling compared to the registration of original data.

Oil and gas reservoirs are well studied in petroleum engineering, using seismic data to estimate fluid flow and well placement. However, seismic data cannot capture fractures due to their scale, and fractures may affect rock porosity and permeability. Consequently, rock fracture segmentation and quantification from aerial images of analogous outcrops can input essential information into those studies. This paper presents a new method, namedinteractive Forest Growing(iFG), for fracture segmentation. The image is initially segmented into superpixels, defining a superpixel graph. The user selects seed superpixels, a path-cost threshold, and fractures are delineated by growing one optimum-path tree from each seed with path costs limited to the selected threshold. iFG considerably increases efficiency while reducing human effort in fracture segmentation compared to pixel-by-pixel manual annotation. We evaluate iFG with three specialists and against aninteractive Region Growing(iRG) method using 15 images to measure bias in user interpretations, verify efficiency gain over a similar approach, and generate a dataset with consolidated annotation for future work. The experiments show that iFG reduces user interventions from 19% to 33% compared to iRG, users with more experience in fracture analysis complete segmentation 4-5 times faster, and segmentation effectiveness is independent of user experience since the average F1 scores between users uing both methods ranged from 0.966 to 0.979, allowing us to create a consolidated segmentation.

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).

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.

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.

The Total Electron Content (TEC) derived from Global Navigation Satellite System (GNSS) data processing has been used as a tool for monitoring earthquakes. The purpose of this study is to bring an alternative approach to the prediction of earthquakes and to determine their magnitudes based on Artificial Neural Networks (ANN) and ionospheric disturbances. For this, the Vertical Total Electron Content (VTEC) data from the National Oceanic and Atmosphere Administration (NOAA) were used to train the ANN. Results show that the ANN process achieved an accuracy of 85.71% in validation assessment to predict Tres Picos Mw=8.2 earthquake from 1:30 UTC to 04:00 UTC, approximately 3 hours before the seismic event. For magnitude classification, the ANN achieved an accuracy of 94.60%. The Matthews Correlation Coefficient (MCC) which takes into account all true/false positives and negatives was also evaluated and showed promising results.

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.