Deep Learning on SAR Imagery: Transfer Learning Versus Randomly Initialized Weights

Deploying deep learning on Synthetic Aperture Radar (SAR) data is becoming more common for mapping purposes. One such case is sea ice, which is highly dynamic and rapidly changes as a result of the combined effect of wind, temperature, and ocean currents. Therefore, frequent mapping of sea ice is necessary to ensure safe marine navigation. However, there is a general shortage of expert-labeled data to train deep learning algorithms. Fine-tuning a pre-trained model on SAR imagery is a potential solution. In this paper, we compare the performance of deep learning models trained from scratch using randomly initialized weights against pre-trained models that we fine-tune for this purpose. Our results show that pre-trained models lead to better results, especially on test samples from the melt season

Linking image processing and numerical modeling to identify potential geohazards

Faults, along with natural fractures, may enhance production when confined within the reservoir. However, if the fault is connected to an aquifer, it may cause early water breakthrough in the reservoir. Even if they are not conductive, they pose a significant geohazard during drilling as fault slippage can cause shearing of casing/tubing leading to either sidetracking, or complete abandonment of the well. In this thesis, I propose a simplistic approximation of dynamic conductivity of faults based on steady state flow equation. I use a geometric attribute; coherence, as a proxy for fault hydraulic conductivity and in a steady state flow equation to model dynamic flow. This thesis was inspired by problems faced by several companies working the Eagle Ford shale reservoir of south Texas. Surveys often exceeds 1000 km2 and exhibit hundreds of faults. Most faults are not problematic; however, some connect with the deeper Edwards limestone aquifer. Wells that complete near these faults produce water. This algorithm can provide early water production warnings and can provide simple, easy to compute useful input in field development in the absence of the more complete datasets required more rigorous reservoir simulation implemented in commercial software. This simple tool is designed to be used in a statistical, rather than deterministic manner, identifying problematic faults by comparing their orientation and connectivity to those known to be bad by previous drilling history. The computational time is less than 17% of a more rigorous conventional reservoir simulation. The model can be updated easily as more and more dataset are available during various stages of field development by ignoring important parameters for single well production such as facies, petrophysical and flow equations. This algorithm is a fast and simple approximation that can be very useful in overall field management where one wishes to quickly identify problematic faults or fault sets

Machine learning applications for geoscience problems

Geoscientists have used machine learning for at least three decades and the applications spam many fields, from seismic processing and interpretation, to remote sensing classification, to analysis of well log data, among many others. More popular in some fields (e.g. seismic interpretation, remote sensing analysis) than others (e.g. paleontology), machine learning tools can leverage research in different areas of geoscience. Although machine learning is becoming more popular in different fields of geoscience, some concepts of more modern applications, convolutional neural networks in particular, are still vaguely understood by non-practitioners. I present some of the key concepts of machine learning with more details on the foundations of convolutional neural networks and some techniques that can help better understand convolutional neural networks behavior. I then present five case studies, mostly using convolutional neural networks and transfer learning. Transfer learning is a methodology that allow us to repurpose filters created by convolutional neural networks on a primary task to perform a secondary task. The five case studies start with a broader application of convolutional neural networks for different geoscience images, including thin-sections and core photographs. Then I present a how to perform core classification using convolutional neural networks. Next, how microfossils can be classified by the same methodology. I present a more detailed analysis of transfer learning using different remote sensing datasets. In the final case study, I show applications of supervised learning techniques to help forecast Megaelectron-Volt electrons inside Earth’s outer radiation belt. I conclude the dissertation with a summary and comments on the expectation of future research

Petrographic analysis with deep convolutional neural networks

Petrographic analysis is based on the microscopic description and classification of rocks and is a crucial technique for sedimentary and diagenetic studies. When compared to hand specimens, thin sections of rocks provide better and more accurate means for analysis of mineral distribution and percentage, pore space analysis, and cement composition. Because of the rich information they contain, thin section data are commonly used not only by the mining and petroleum industry, but by the academic community as well. Most petrographic analysis relies on visual inspection of rock thin sections under a microscope, a task that is laborious even for experienced geologists. Large projects with a tight time frame requiring the analysis of a large amount of thin sections may require multiple petrographers, thereby risking the introduction of inconsistency in the analysis. To address this challenge, we explore the use of deep convolutional neural networks (CNN) as a tool that can allow the petrographer to analyze and classify more samples in a consistent manner. Unlike previous studies using deep learning models trained on large volumes of thin section data, we make use of transfer learning based on robust and reliable CNN models trained with a large amount of non-geological images. With a much smaller number of labeled thin sections used in training followed by “fine-tuning” we are able to construct convolutional neural networks that achieve low error levels (<5% when images of same quality are used for training and testing) in thin section classification. While becoming widely accepted as a useful tool in the biological and manufacturing disciplines, CNN is currently underutilized in the geoscience community; we foresee an increase of use of such techniques to help accelerate and quantify a wide variety of geological tasks

Comparison of Cross-Entropy, Dice, and Focal Loss for Sea Ice Type Segmentation

Up-to-date sea ice charts are crucial for safer navigation in ice-infested waters. Recently, Convolutional Neural Network (CNN) models show the potential to accelerate the generation of ice maps for large regions. However, results from CNN models still need to undergo scrutiny as higher metrics performance not always translate to adequate outputs. Sea ice type classes are imbalanced, requiring special treatment during training. We evaluate how three different loss functions, some developed for imbalanced class problems, affect the performance of CNN models trained to predict the dominant ice type in Sentinel-1 images. Despite the fact that Dice and Focal loss produce higher metrics, results from cross-entropy seem generally more physically consistent

Increasing the Spatial Coverage of Atmospheric Aerosol Depth Measurements Using Random Forest and Mean Filters

Aerosols play a critical role in atmospheric chemistry, and affect clouds, climate, and human health. However, the spatial coverage of satellite-derived aerosol optical depth (AOD) products is limited by cloud cover, orbit patterns, polar night, snow, and bright surfaces, which negatively impacts the coverage and accuracy of particulate matter modeling and health studies relying on air pollution characterization. We present a random forest model trained to capture spatial dependence of AOD and produce higher coverage through imputation. By combining the models with and without the mean filters, we are able to create full-coverage high-resolution daily AOD in the conterminous U.S., which can be used for aerosol estimation and other studies leveraging air pollutant concentration levels.Comment: IEEE International Geoscience and Remote Sensing Symposium 2023 6 Pages, 2 Figure

Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification

Comportamento de novilhas leiteiras após aumento da quantidade de sucedâneo do leite em pó adicionado ao leite integral

The objective of this work was to evaluate the effects of increasing the contents of total solids (TS) in whole milk, by adding increasing amounts of milk replacer powder, on the behavior of dairy heifers. Holstein-Gyr crossbred heifers (n = 60) were distributed in four treatments: 13.5, 16.1, 18.2, and 20.4% TS. From 5 to 55 days of age, heifers received 6 L per day of liquid feed, which was reduced by half from 56 to 59 days. Heifers were weaned at 60 days of age and monitored until 90 days, and their behavior was evaluated weekly. During gradual weaning and after weaning, heifer behavior was recorded 1 hour before and 1 hour after liquid feed was offered, and, during weaning, it was evaluated by the scan method. Heifers fed liquid feed containing 20.4% TS present a higher number of play behaviors, spent less time standing, and spent more time ruminating than those that received liquid feed with 13.5% TS, indicating that a higher nutritional plan during weaning is an effective strategy to reduce stress in this period.O objetivo deste trabalho foi avaliar os efeitos do aumento das concentrações de sólidos totais (ST) no leite, pela adição de quantidades crescentes de sucedâneo do leite em pó, sobre o comportamento de bezerras leiteiras. Bezerras mestiças Holandês-Gir (n = 60) foram distribuídas em quatro tratamentos: 13,5, 16,1, 18,2 e 20,4% de ST. Dos 5 aos 55 dias de idade, as bezerras receberam 6 L por dia de dieta líquida, que foi reduzida à metade dos 56 aos 59 dias. As bezerras foram desaleitadas aos 60 dias de idade e monitoradas até os 90 dias, e o seu comportamento foi avaliado semanalmente. Durante o desaleitamento gradual e após o desaleitamento, o comportamento das bezerras foi anotado 1 hora antes e 1 hora após o oferecimento da dieta liquida, e, durante a fase de desaleitamento, foi avaliado pelo método de varredura instantânea. As bezerras alimentadas com 20,4% de ST na dieta líquida apresentam maior número de comportamentos de brincadeiras, menor tempo de permanência em pé e maior tempo ruminando, em comparação às que receberam 13,5% de ST na dieta líquida, o que indica que maior plano nutricional durante o desaleitamento é uma estratégia efetiva para a redução do estresse nessa etapa

Caracterização de queixas escolares e diagnósticos no ambulatório de neuro-dificuldades de aprendizagem

The objective of this study was to characterize the children referred to the Neuro-Learning Disorder Clinic at the Public Hospital of the Universidade Estadual de Campinas (State University of Campinas) in 2010 focusing on the demographics, parents' concerns, and the diagnoses given by the health care professionals. A total of 203 male and female children and young people, aged 4-17 years old, attending kindergarten to high school were analyzed. The children that were referred due to school-related problems underwent interdisciplinary evaluations aiming to establish a diagnosis. After thorough evaluation, the children were treated according to their specific needs. The study sample was predominately comprised of males (67.0%), fifth graders with average age of 10 years and 11 months. The main problems identified were global learning difficulties and inattention. The evaluation results indicated 43.8% of pedagogy-related learning difficulties and 32.2% of intellectual disability issues. The findings corroborate those of other studies on the characterization of behavior and school-related problems of children and adolescents in specialized centers. The results obtained emphasize the importance of an interdisciplinary team work to evaluate school-related problems331161171O objetivo do trabalho foi caracterizar as crianças encaminhadas ao Ambulatório de Neuro-Dificuldades de Aprendizagem da Universidade Estadual de Campinas em 2010. Foram analisados dados de 203 crianças e jovens, de ambos os gêneros, com faixa etária entre 4 e 17 anos de idade, cursando do ensino infantil ao médio. As crianças encaminhadas com queixas escolares passaram por avaliações interdisciplinares com objetivo diagnóstico e após o processo avaliativo foram devidamente encaminhadas para intervenções específicas conforme o caso. Na amostra de crianças atendidas houve maior frequência do gênero masculino (67,0%), uma idade média de 10 anos e 11 meses, com maior número de pessoas cursando o 5ª ano. As principais queixas foram dificuldades globais de aprendizagem e desatenção. Dos resultados das avaliações, 43,8% foram de dificuldades escolares de ordem pedagógica e 32,2% de deficiência intelectual. Os achados obtidos corroboram outros estudos de caracterização de queixas escolares e comportamentais de crianças e adolescentes em centros especializados. Os dados também reforçam a importância da avaliação de equipe interdisciplinar em um processo de investigação das queixas escolare