Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this ...paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8.
Abstract
The roughness property of rocks is significant in engineering studies due to their mechanical and hydraulic performance and the possibility of quantifying flow velocity and predicting the ...performance of wells and rock mass structures. However, the study of roughness in rocks is usually carried out through 2D linear measurements (through mechanical profilometer equipment), obtaining a coefficient that may not represent the entire rock surface. Thus, based on the hypothesis that it is possible to quantify the roughness coefficient in rock plugs reconstructed three-dimensionally by the computer vision technique, this research aims to an alternative method to determine the roughness coefficient in rock plugs. The point cloud generated from the 3D model of the photogrammetry process was used to measure the distance between each point and a calculated fit plane over the entire rock surface. The roughness was quantified using roughness parameters (
$$R_a$$
R
a
) calculated in hierarchically organized regions. In this hierarchical division, the greater the quantity of division analyzed, the greater the detail of the roughness. The main results show that obtaining the roughness coefficient over the entire surface of the three-dimensional model has peculiarities that would not be observed in the two-dimensional reading. From the 2D measurements, mean roughness values (
$$R_a$$
R
a
) of
$$0.35\,\upmu \hbox {m}$$
0.35
μ
m
and
$$0.235\,\upmu \hbox {m}$$
0.235
μ
m
were obtained for samples 1 and 2, respectively. By the same method, the results of the
$$R_a$$
R
a
coefficient applied three-dimensionally over the entire rocky surface were at most
$$0.165\,\upmu \hbox {m}$$
0.165
μ
m
and
$$0.166\,\upmu \hbox {m}$$
0.166
μ
m
, respectively, showing the difference in values along the surface and the importance of this approach.
Precipitable Water Vapour (
PWV
) plays an essential role in atmospheric science. Integration between meteorology stations and Global Navigation Satellite Systems (GNSS) receivers has enabled ...high-resolution space-time
PWV
retrieval. However, the quality of
PWV
values from GNSS depends on the availability of a Weighted Mean Temperature Model (
T
m
). Various Tm models have been developed using statistical-based methods. In this contribution, we present new Tm models for the Brazilian region based on Machine Learning techniques, leveraging radiosonde data provided by the Brazilian Institute of Space Research (INPE). We used radiosonde data from 1961 to 1993 for model training and data from 1999 to 2002 for the evaluation phase to assess model performance under usage conditions. Our study employs the following Machine Learning (ML) methods: Random Forest Regression, Support Vector Regression, Recurrent Neural Networks, Gated Recurrent Unit Neural Networks, and Long Short-Term Memory Neural Networks. We conducted a comparative analysis with the traditional Multiple Linear Regression method. The results reveal that there is no universally superior method; the choice of method depends on the region. Furthermore, our findings suggest that integrating classical statistical methods with ML approaches may enhance existing Tm models.
An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of modern measurement ...systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions but must be computed numerically by means of Monte Carlo. This paper provides the first results on the Monte Carlo-based critical value inserted into different scenarios of correlation between outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, over-identifications and statistical overlap associated with IDS in the presence of a single outlier. On the basis of such probability levels, we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for cases in which IDS is in play. The MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances in which the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection but the lower the rates of outlier identification. In such a case, the larger the Type I Error, the larger the ratio between the MIB and MDB. We also highlight that an outlier becomes identifiable when the contributions of the measures to the wrong exclusion rate decline simultaneously. In this case, we verify that the effect of the correlation between outlier statistics on the wrong exclusion rate becomes insignificant for a certain outlier magnitude, which increases the probability of identification.
The identification of fractures and discontinuities has great importance on the fluid flow estimation in hydrocarbon reservoirs since they influence the properties of porosity and permeability. Due ...to the inaccessibility and sparsity of reservoir data, the fracture characterization is generally assessed through the study of outcrop analogues using remote sensing or in situ observations by a specialist. Considering the remote sensing methods, the unmanned Aerial Vehicle (UAV) acquisition combined with Structure from Motion (SfM) photogrammetry is a low-cost way to generate products like orthorectified images, allowing manual and automated methods of fracture trace detection. Automatic approaches, commonly used to address this problem, present some known limitations and disadvantages due to the nature of the outcrops and weather conditions during UAV acquisitions. In this work, we focus on fracture detection over karstic regions that are highly fractured. For this, we evaluated a series of adaptive segmentation methods based on thresholding. The Sauvola local adaptive segmentation presented the best result when compared to a manually annotated ground truth. The segmentation results were further improved by the use of the binary denoising method Non-Local means. We also carried an evaluation of the influence of the sun position in the fracture detection, and to reduce this inherent bias we combined three UAV acquisitions done over the karstic carbonate outcrop, namely Rosário pavement in the Jandaíra formation northeast Brazil. With the proposed methodology we acquired more accurate fracture data over the study area, which follows the directional statistics of previous works carried out in the region.
The study of outcrops in geosciences is being significantly improved by the enhancement of technologies that aims to build digital outcrop models (DOMs). Usually, the virtual environment is built by ...a collection of partially overlapped photographs taken from diverse perspectives, frequently using unmanned aerial vehicles (UAV). However, in situations including very steep features or even sub-vertical patterns, incomplete coverage of objects is expected. This work proposes an integration framework that uses terrestrial spherical panoramic images (SPI), acquired by omnidirectional fusion camera, and a UAV survey to overcome gaps left by traditional mapping in complex natural structures, such as outcrops. The omnidirectional fusion camera produces wider field of view images from different perspectives, which are able to considerably improve the representation of the DOM, mainly where the UAV has geometric view restrictions. We designed controlled experiments to guarantee the equivalent performance of SPI compared with UAV. The adaptive integration is accomplished through an optimized selective strategy based on an octree framework. The quality of the 3D model generated using this approach was assessed by quantitative and qualitative indicators. The results show the potential of generating a more reliable 3D model using SPI allied with UAV image data while reducing field survey time and complexity.
Fracture modeling plays a valuable role to understand the fluid flow in carbonate reservoirs. For this, the fracture characterization to generate Discrete Fracture Networks (DFNs) can take advantage ...of analogue outcrops through Virtual Outcrop Models (VOMs), acquired by Unmanned Aerial Vehicles (UAV) and digital photogrammetry. The stochastic DFN generation is an important step in reservoir modeling as it brings more representative data to the process and has long been studied. However, optimizations concerning automatizing some of the steps necessary to its generation like data clustering are still open to advancements. In this sense, this work aims the fracture data clustering and the definition of the number of clusters when gathering data for the stochastic process, developing an Elbow method for spherical data and a balanced K-means, both based on Fisher statistics. For this, we interpreted fracture planes in a VOM that recreates a carbonate reservoir analogue from the Jandaíra Formation, in the Northeast, Brazil. As result, we show a workflow for immersive fracture interpretation alongside a 3D stochastic DFN model with fracture intensity of 22.57m −1 for cell sizes of 1m 3 . Regarding the clustering balance, our method achieved a lower standard deviation between sets while maintaining the Fisher values greater to obtain fracture sets with lower dispersion. Additionally, the Elbow method implementation proved a beneficial step to the workflow as it reduced the interpretation bias of family clusters. These results alongside the proposed workflow bring a better understanding of the outcrop geometry while offering data scalability for reservoir modeling.
The quantitative determination of average roughness parameters, from the determination of height variations of the surface points, is frequently used to estimate the adhesion between an adhesive and ...the surface of a substrate. However, to determine the interaction between an adhesive and a surface of a heterogeneous material, such as a red ceramic, it is essential to define other roughness parameters. This work proposes a method for determining the roughness of red ceramic blocks from a three-dimensional evaluation, with the objective of estimating the contact area that the ceramic substrate can provide for a cementitious matrix. The study determines the average surface roughness from multiple planes and proposes the adoption of 2 more roughness parameters, the valley area index and the average valley area. The results demonstrate that there are advantages in using the proposed multiple plane method for roughness computation and that the valley area parameters are efficient to estimate the extent of adhesion between the materials involved.
Reliability analysis allows for the estimation of a system's probability of detecting and identifying outliers. Failure to identify an outlier can jeopardize the reliability level of a system. Due to ...its importance, outliers must be appropriately treated to ensure the normal operation of a system. System models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed where such functional relations can be slightly violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate their uncertainties to the model parameters during the data processing.
This article proposes a technique named Printgrammetry, a structured workflow that allows the extraction of 3-D models from Google Earth platform through the combination of image captures from the ...screen monitor with structure from motion algorithms. This technique was developed to help geologists and other geoscientists in acquiring 3-D photo-realistic models of outcrops and natural landscapes of big proportions without the need of field mapping and expensive equipment. The methodology is detailed aiming to permit easy reproducibility and focused on achieving the highest resolution possible by working with the best images that the platform can provide. The results have shown that it is possible to obtain visually high-quality models from natural landscapes from Google Earth by acquiring images at high level of detail regions of the software, using a 4K monitor, multidirectional screenshots, and by marking homogeneously spaced targets for georeferencing and scaling. The geometric quality assessment performed using light detection and ranging ground truth data as comparison shows that the Printgrammetry dense point clouds have reached 98.1% of the total covered area under 5 m of distance for the Half Dome case study and 96.7% for the Raplee Ridge case study. The generated 3-D models were then visualized and interacted through an immersive virtual reality software that allowed geologists to manipulate this virtual field environment in different scales. This technique is considered by the authors to have a promising potential for research, industrial, and educational projects that do not require high-precision models.
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