Artigos de revistas: "Surface anomaly detection"

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Schwenk, J. Tyler, Steven D. Sloan, Julian Ivanov e Richard D. Miller. "Surface-wave methods for anomaly detection". GEOPHYSICS 81, n.º 4 (julho de 2016): EN29—EN42. http://dx.doi.org/10.1190/geo2015-0356.1.

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Perimeter-defense operations, geohazard assessment, and engineering characterization require the detection and localization of subsurface anomalies. Seismic waves incident upon these discontinuities generate a scattered wavefield. We have developed various surface-wave techniques, currently being fielded, that have consistently delivered accurate and precise results across a wide range of survey parameters and geographical locations. We use the multichannel analysis of surface waves approach to study the multimode Rayleigh wave, the backscatter analysis of surface waves (BASW) method to detect anomalies, 3D visualization for efficient seismic interpretation, BASW correlation for attribute analysis, and instantaneous-amplitude integration in the complex BASW method. Discrete linear moveout functions and [Formula: see text]-[Formula: see text] filter designs are optimized for BASW considering the fundamental and higher mode dispersion trends of the Rayleigh wave. Synthetic and field data were used to demonstrate multimode BASW and mode separation, which accentuated individual scatter events, and ultimately increased confidence in points of interest. Simple correlation algorithms between fundamental and higher-mode BASW data offer attribute analysis that limits the subjective interpretation of BASW images. Domain sorting and Hilbert transforms allow for 3D visualization and rapid interpretation of an anomaly’s wavefield phenomena within an amplitude cube. Furthermore, instantaneous-amplitude analysis can be incorporated into a more robust complex BASW method that forgives velocity-estimation inaccuracies, while requiring less rigorous preprocessing. Our investigations have suggested that a multifaceted surface-wave analysis provides a valuable tool for today’s geophysicists to fulfill anomaly-detection survey requirements.

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Putri, A. R. D., P. Sidiropoulos e J. P. Muller. "ANOMALY DETECTION PERFORMANCE COMPARISON ON ANOMALY-DETECTION BASED CHANGE DETECTION ON MARTIAN IMAGE PAIRS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (5 de junho de 2019): 1437–41. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1437-2019.

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Abstract. The surface of Mars has been imaged in visible wavelengths for more than 40 years since the first flyby image taken by Mariner 4 in 1964. With higher resolution from orbit from MOC-NA, HRSC, CTX, THEMIS, and HiRISE, changes can now be observed on high-resolution images from different instruments, including spiders (Piqueux et al., 2003) near the south pole and Recurring Slope Lineae (McEwen et al., 2011) observable in HiRISE resolution. With the huge amount of data and the small number of datasets available on Martian changes, semi-automatic or automatic methods are preferred to help narrow down surface change candidates over a large area.To detect changes automatically in Martian images, we propose a method based on a denoising autoencoder to map the first Martian image to the second Martian image. Both images have been automatically coregistered and orthorectified using ACRO (Autocoregistration and Orthorectification) (Sidiropoulos and Muller, 2018) to the same base image, HRSC (High-Resolution Stereo Camera) (Neukum and Jaumann, 2004; Putri et al., 2018) and CTX (Context Camera) (Tao et al., 2018) orthorectified using their DTMs (Digital Terrain Models) to reduce the number of false positives caused by the difference in instruments and viewing conditions. Subtraction of the codes of the images are then inputted to an anomaly detector to look for change candidates. We compare different anomaly detection methods in our change detection pipeline: OneClassSVM, Isolation Forest, and, Gaussian Mixture Models in known areas of changes such as Nicholson Crater (dark slope streak), using image pairs from the same and different instruments.

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Stolz, Bernadette J., Jared Tanner, Heather A. Harrington e Vidit Nanda. "Geometric anomaly detection in data". Proceedings of the National Academy of Sciences 117, n.º 33 (3 de agosto de 2020): 19664–69. http://dx.doi.org/10.1073/pnas.2001741117.

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The quest for low-dimensional models which approximate high-dimensional data is pervasive across the physical, natural, and social sciences. The dominant paradigm underlying most standard modeling techniques assumes that the data are concentrated near a single unknown manifold of relatively small intrinsic dimension. Here, we present a systematic framework for detecting interfaces and related anomalies in data which may fail to satisfy the manifold hypothesis. By computing the local topology of small regions around each data point, we are able to partition a given dataset into disjoint classes, each of which can be individually approximated by a single manifold. Since these manifolds may have different intrinsic dimensions, local topology discovers singular regions in data even when none of the points have been sampled precisely from the singularities. We showcase this method by identifying the intersection of two surfaces in the 24-dimensional space of cyclo-octane conformations and by locating all of the self-intersections of a Henneberg minimal surface immersed in 3-dimensional space. Due to the local nature of the topological computations, the algorithmic burden of performing such data stratification is readily distributable across several processors.

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Tsai, Du-Ming, e Po-Hao Jen. "Autoencoder-based anomaly detection for surface defect inspection". Advanced Engineering Informatics 48 (abril de 2021): 101272. http://dx.doi.org/10.1016/j.aei.2021.101272.

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Sattar, Shahram, Songnian Li e Michael Chapman. "Road Surface Monitoring Using Smartphone Sensors: A Review". Sensors 18, n.º 11 (9 de novembro de 2018): 3845. http://dx.doi.org/10.3390/s18113845.

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Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

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Liu, Gaokai, Ning Yang e Lei Guo. "An Attention-Based Network for Textured Surface Anomaly Detection". Applied Sciences 10, n.º 18 (8 de setembro de 2020): 6215. http://dx.doi.org/10.3390/app10186215.

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Textured surface anomaly detection is a significant task in industrial scenarios. In order to further improve the detection performance, we proposed a novel two-stage approach with an attention mechanism. Firstly, in the segmentation network, the feature extraction and anomaly attention modules are designed to capture the detail information as much as possible and focus on the anomalies, respectively. To strike dynamic balances between these two parts, an adaptive scheme where learnable parameters are gradually optimized is introduced. Subsequently, the weights of the segmentation network are frozen, and the outputs are fed into the classification network, which is trained independently in this stage. Finally, we evaluate the proposed approach on DAGM 2007 dataset which consists of diverse textured surfaces with weakly-labeled anomalies, and the experiments demonstrate that our method can achieve 100% detection rates in terms of TPR (True Positive Rate) and TNR (True Negative Rate).

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Rasul, Azad, e Luqman W. Omar. "Land Surface Temperature Anomalies Detection for the Strong Earthquakes in 2018". ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 8, n.º 2 (1 de setembro de 2020): 15–21. http://dx.doi.org/10.14500/aro.10591.

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Earthquake every year leads to human and material losses and unpredictability of it by now makes this natural disaster worsen. The objective of the current study was to determine the anomalies in land surface temperature (LST) in areas affected by earthquakes. In this research, three earthquakes (M >6) were studied. Moderate Resolution Imaging Spectroradiometer Aqua and Terra day and night LST data used from 2003 to 2018. The interquartile range (IQR) and mean ± 2σ methods utilized to select anomalies. As a result, based on the IQR method, no prior and after anomaly detected in selected cases and data. Based on mean ± 2σ, usually positive anomaly occurred during daytime. However, negative (or positive) anomaly occurred during the nighttime before the Mexico and Bolivia earthquakes. During 10 days after the earthquake, sometimes a negative anomaly detected.

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Ouyang Haoyi, 欧阳浩艺, 陈婉钧 Chen Wanjun, 李海 Li Hai e 杨初平 Yang Chuping. "平整表面反射率异常的单像素检测理论". Laser & Optoelectronics Progress 58, n.º 12 (2021): 1212003. http://dx.doi.org/10.3788/lop202158.1212003.

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Wong, Ze-Hao, C. M. Thong, W. M. Edmund Loh e C. J. Wong. "Surface Defect Detection using Novel Histogram Distance-based Multiple Template Anomalies Detection Algorithm". International Journal of Engineering & Technology 7, n.º 4.14 (24 de dezembro de 2019): 401. http://dx.doi.org/10.14419/ijet.v7i4.14.27693.

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Surface defects in manufacturing are top challenges in various manufacturing field including LED manufacturing, die manufacturing and printing industry. Quality control through automated surface defect detection has been an emphasis to speed up the production without jeopardizing the quality of the product. However, complexity and flexibility in product design, specification and dataset availability posted challenges in existing referential-based algorithm. Golden template-based algorithms are sensitive to misalignment and product variations. Deep learning and its variant can be used as non-linear filter to segment anomalies area. However, deep learning requires huge labelled database and consume long learning time. Similarly, maximum likelihood-based algorithms require large database for learning. This research proposes a novel histogram distance based multiple templates anomalies detection (MTAD) algorithm to segment surface defect. Histogram distance based on kernel-wise histograms stacked across illumination normalized database of similar size can describe the degree of anomaly intuitively across the image. Then, surface defect can be justified intuitively according to anomaly heat map generated. The algorithm is tested against industrial samples and it can handle texture and design variation existed in the product while catching anomaly in real time. This research suggests future studies on extending dimensionality of the histogram. Suggested algorithm has wide range of application other than surface defect detection. For examples, video motion detection, decolorization detection on industrial lighting.

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Nazir, Sajid, Shushma Patel e Dilip Patel. "Autoencoder Based Anomaly Detection for SCADA Networks". International Journal of Artificial Intelligence and Machine Learning 11, n.º 2 (julho de 2021): 83–99. http://dx.doi.org/10.4018/ijaiml.20210701.oa6.

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Supervisory control and data acquisition (SCADA) systems are industrial control systems that are used to monitor critical infrastructures such as airports, transport, health, and public services of national importance. These are cyber physical systems, which are increasingly integrated with networks and internet of things devices. However, this results in a larger attack surface for cyber threats, making it important to identify and thwart cyber-attacks by detecting anomalous network traffic patterns. Compared to other techniques, as well as detecting known attack patterns, machine learning can also detect new and evolving threats. Autoencoders are a type of neural network that generates a compressed representation of its input data and through reconstruction loss of inputs can help identify anomalous data. This paper proposes the use of autoencoders for unsupervised anomaly-based intrusion detection using an appropriate differentiating threshold from the loss distribution and demonstrate improvements in results compared to other techniques for SCADA gas pipeline dataset.

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Staar, Benjamin, Michael Lütjen e Michael Freitag. "Anomaly detection with convolutional neural networks for industrial surface inspection". Procedia CIRP 79 (2019): 484–89. http://dx.doi.org/10.1016/j.procir.2019.02.123.

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Ono, Y., A. Tsuji, J. Abe, H. Noguchi e J. Abe. "ROBUST DETECTION OF SURFACE ANOMALY USING LIDAR POINT CLOUD WITH INTENSITY". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (14 de agosto de 2020): 1129–36. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1129-2020.

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Abstract. We have developed an automatic detection method for metallic corrosion in facilities by using a LiDAR point cloud. While visual inspections for monitoring facilities are widely conducted, the inspection result depends on human skill, and there is currently a shortage of inspectors. While automatic detection methods using an RGB image have been developed, such methods cannot be applied to inspections at night. Therefore, we propose a robust detection method that utilizes both 3D shapes and intensities in a LiDAR point cloud instead of RGB information. The proposed method segments the point cloud into a basic building material by using the 3D shape and then recognizes a point cloud with an abnormal intensity in each material as the corrosion area. We demonstrate through experiments that the proposed method can robustly detect corrosion spots in aging facilities during detection conducted both during the day and at night.

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Jiao, Zhong-Hu, Jing Zhao e Xinjian Shan. "Pre-seismic anomalies from optical satellite observations: a review". Natural Hazards and Earth System Sciences 18, n.º 4 (4 de abril de 2018): 1013–36. http://dx.doi.org/10.5194/nhess-18-1013-2018.

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Abstract. Detecting various anomalies using optical satellite data prior to strong earthquakes is key to understanding and forecasting earthquake activities because of its recognition of thermal-radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful tool in monitoring earthquake preparation areas at a global scale and in a nearly real-time manner. Over the past several decades, many new different data sources have been utilized in this field, and progressive anomaly detection approaches have been developed. This paper reviews the progress and development of pre-seismic anomaly detection technology in this decade. First, precursor parameters, including parameters from the top of the atmosphere, in the atmosphere, and on the Earth's surface, are stated and discussed. Second, different anomaly detection methods, which are used to extract anomalous signals that probably indicate future seismic events, are presented. Finally, certain critical problems with the current research are highlighted, and new developing trends and perspectives for future work are discussed. The development of Earth observation satellites and anomaly detection algorithms can enrich available information sources, provide advanced tools for multilevel earthquake monitoring, and improve short- and medium-term forecasting, which play a large and growing role in pre-seismic anomaly detection research.

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Tian, Hongzhi, Dongxing Wang, Jiangang Lin, Qilin Chen e Zhaocai Liu. "Surface Defects Detection of Stamping and Grinding Flat Parts Based on Machine Vision". Sensors 20, n.º 16 (13 de agosto de 2020): 4531. http://dx.doi.org/10.3390/s20164531.

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Currently, surface defect detection of stamping grinding flat parts is mainly undertaken through observation by the naked eye. In order to improve the automatic degree of surface defects detection in stamping grinding flat parts, a real-time detection system based on machine vision is designed. Under plane illumination mode, the whole region of the parts is clear and the outline is obvious, but the tiny defects are difficult to find; Under multi-angle illumination mode, the tiny defects of the parts can be highlighted. In view of the above situation, a lighting method combining plane illumination mode with multi-angle illumination mode is designed, and five kinds of defects are automatically detected by different detection methods. Firstly, the parts are located and segmented according to the plane light source image, and the defects are detected according to the gray anomaly. Secondly, according to the surface of the parts reflective characteristics, the influence of the reflection on the image is minimized by adjusting the exposure time of the camera, and the position and direction of the edge line of the gray anomaly region of the multi-angle light source image are used to determine whether the anomaly region is a defect. The experimental results demonstrate that the system has a high detection success rate, which can meet the real-time detection rEquation uirements of a factory.

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Sattar, Shahram, Songnian Li e Michael Chapman. "Developing a near real-time road surface anomaly detection approach for road surface monitoring". Measurement 185 (novembro de 2021): 109990. http://dx.doi.org/10.1016/j.measurement.2021.109990.

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Zhang, J., J. Jung, G. Sohn e M. Cohen. "THERMAL INFRARED INSPECTION OF ROOF INSULATION USING UNMANNED AERIAL VEHICLES". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (27 de agosto de 2015): 381–86. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-381-2015.

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UAVs equipped with high-resolution thermal cameras provide an excellent investigative tool used for a multitude of building-specific applications, including roof insulation inspection. We have presented in this study a relative thermographic calibration algorithm and a superpixel Markov Random Field model to address problems in thermal infrared inspection of roof insulation using UAVs. The relative thermographic radiometric calibration algorithm is designed to address the autogain problem of the thermal camera. Results show the algorithm can enhance the contrast between warm and cool areas on the roof surface in thermal images, and produces more constant thermal signatures of different roof insulations or surfaces, which could facilitate both visual interpretation and computer-based thermal anomaly detection. An automatic thermal anomaly detection algorithm based on superpixel Markov Random Field is proposed, which is more computationally efficient than pixel based MRF, and can potentially improve the production throughput capacity and increase the detection accuracy for thermal anomaly detection. Experimental results show the effectiveness of the proposed method.

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Liu, Jie, Kechen Song, Mingzheng Feng, Yunhui Yan, Zhibiao Tu e Liu Zhu. "Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection". Optics and Lasers in Engineering 136 (janeiro de 2021): 106324. http://dx.doi.org/10.1016/j.optlaseng.2020.106324.

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Liu, Gaokai, Ning Yang, Lei Guo, Shiping Guo e Zhi Chen. "A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies". Sensors 20, n.º 7 (25 de março de 2020): 1829. http://dx.doi.org/10.3390/s20071829.

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We explore a one-stage method for surface anomaly detection in industrial scenarios. On one side, encoder-decoder segmentation network is constructed to capture small targets as much as possible, and then dual background suppression mechanisms are designed to reduce noise patterns in coarse and fine manners. On the other hand, a classification module without learning parameters is built to reduce information loss in small targets due to the inexistence of successive down-sampling processes. Experimental results demonstrate that our one-stage detector achieves state-of-the-art performance in terms of precision, recall and f-score.

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Guo, Zi-Xun, e Peng-Lang Shui. "Anomaly Based Sea-Surface Small Target Detection Using K-Nearest Neighbor Classification". IEEE Transactions on Aerospace and Electronic Systems 56, n.º 6 (dezembro de 2020): 4947–64. http://dx.doi.org/10.1109/taes.2020.3011868.

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Apostol, Ioana, Marius Preda, Constantin Nila e Ion Bica. "IoT Botnet Anomaly Detection Using Unsupervised Deep Learning". Electronics 10, n.º 16 (4 de agosto de 2021): 1876. http://dx.doi.org/10.3390/electronics10161876.

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The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

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Park, YeongHyeon, e Il Yun. "Fast Adaptive RNN Encoder–Decoder for Anomaly Detection in SMD Assembly Machine". Sensors 18, n.º 10 (22 de outubro de 2018): 3573. http://dx.doi.org/10.3390/s18103573.

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Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder–Decoder with operating machine sounds. RNN Encoder–Decoder has a structure very similar to Auto-Encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the RNN Encoder–Decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation.

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Simpson, C. J., J. R. Wilford, L. F. Macias e R. J. Korsch. "SATELLITE DETECTION OF NATURAL HYDROCARBON SEEPAGE: PALM VALLEY GAS FIELD, AMADEUS BASIN, CENTRAL AUSTRALIA". APPEA Journal 29, n.º 1 (1989): 196. http://dx.doi.org/10.1071/aj88019.

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Digital image processing of advanced aircraft and Landsat Thematic Mapper (TM) satellite remotely sensed data over sandstones of the Palm Valley Gas Field, central Australia, showed a distinct colour anomaly about 6 km long by 1.5 km wide which is not obvious in visible wavelength imagery. Field inspection showed that the colour anomaly was characterised by different rock- weathering colour, a geobotanical anomaly, calcium carbonate precipitation within rock fractures, and different soil pH. Inorganic rock geochemistry indicates significant chemical differences in some major elements. A limited number of soil gas samples were analysed and within the remotely sensed colour anomaly some had above- threshold concentrations of methane, ethane, propane and butane. Preliminary processing of airborne magnetic and gamma spectrometric data over the anticline did not indicate any significant values that suggested abnormal development of magnetite or clay minerals within the colour anomaly. Carbon and oxygen isotope analyses on calcrete from within the colour anomaly suggest, somewhat inconclusively, that hydrocarbons have not contributed significantly to the formation of the calcium carbonate component of the calcrete. Consideration of all available information suggests that the colour anomaly detectable by aircraft and Landsat TM satellite remote sensing corresponds to a zone of surface alteration resulting from long- term seepage of hydrocarbon gases. This colour anomaly, the first of its type reported from Australia, was detected because of spectral reflectance differences resulting from a combination of increased soil carbonate and different geobotanical characteristics from those of the surrounding terrain.

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Patel, Darsh, Kathiravan Srinivasan, Chuan-Yu Chang, Takshi Gupta e Aman Kataria. "Network Anomaly Detection inside Consumer Networks—A Hybrid Approach". Electronics 9, n.º 6 (1 de junho de 2020): 923. http://dx.doi.org/10.3390/electronics9060923.

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With an increasing number of Internet of Things (IoT) devices in the digital world, the attack surface for consumer networks has been increasing exponentially. Most of the compromised devices are used as zombies for attacks such as Distributed Denial of Services (DDoS). Consumer networks, unlike most commercial networks, lack the infrastructure such as managed switches and firewalls to easily monitor and block undesired network traffic. To counter such a problem with limited resources, this article proposes a hybrid anomaly detection approach that detects irregularities in the network traffic implicating compromised devices by using only elementary network information like Packet Size, Source, and Destination Ports, Time between subsequent packets, Transmission Control Protocol (TCP) Flags, etc. Essential features can be extracted from the available data, which can further be used to detect zero-day attacks. The paper also provides the taxonomy of various approaches to classify anomalies and description on capturing network packets inside consumer networks.

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Saradjian, M. R., e M. Akhoondzadeh. "Thermal anomalies detection before strong earthquakes (M > 6.0) using interquartile, wavelet and Kalman filter methods". Natural Hazards and Earth System Sciences 11, n.º 4 (12 de abril de 2011): 1099–108. http://dx.doi.org/10.5194/nhess-11-1099-2011.

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Abstract. Thermal anomaly is known as a significant precursor of strong earthquakes, therefore Land Surface Temperature (LST) time series have been analyzed in this study to locate relevant anomalous variations prior to the Bam (26 December 2003), Zarand (22 February 2005) and Borujerd (31 March 2006) earthquakes. The duration of the three datasets which are comprised of MODIS LST images is 44, 28 and 46 days for the Bam, Zarand and Borujerd earthquakes, respectively. In order to exclude variations of LST from temperature seasonal effects, Air Temperature (AT) data derived from the meteorological stations close to the earthquakes epicenters have been taken into account. The detection of thermal anomalies has been assessed using interquartile, wavelet transform and Kalman filter methods, each presenting its own independent property in anomaly detection. The interquartile method has been used to construct the higher and lower bounds in LST data to detect disturbed states outside the bounds which might be associated with impending earthquakes. The wavelet transform method has been used to locate local maxima within each time series of LST data for identifying earthquake anomalies by a predefined threshold. Also, the prediction property of the Kalman filter has been used in the detection process of prominent LST anomalies. The results concerning the methodology indicate that the interquartile method is capable of detecting the highest intensity anomaly values, the wavelet transform is sensitive to sudden changes, and the Kalman filter method significantly detects the highest unpredictable variations of LST. The three methods detected anomalous occurrences during 1 to 20 days prior to the earthquakes showing close agreement in results found between the different applied methods on LST data in the detection of pre-seismic anomalies. The proposed method for anomaly detection was also applied on regions irrelevant to earthquakes for which no anomaly was detected, indicating that the anomalous behaviors can be related to impending earthquakes. The proposed method receives its credibility from the overall capabilities of the three integrated methods.

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Wongpornchai, Pisanu, e Chanida Suwanprasit. "Thermal Anomalies Detection Using Comparative Method for Small Earthquake". MATEC Web of Conferences 186 (2018): 01008. http://dx.doi.org/10.1051/matecconf/201818601008.

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Thermal anomaly is one of the earthquake precursor in the earthquake preparatory phase. Remote sensing in thermal region has been employed based on the concept of stress accumulation in the active plate tectonics region, which may be transformed as temperature variation prior to earthquake. MODIS Land Surface Temperature has been commonly used to locate the thermal anomalies before the earthquake. Recently researches have been focusing on moderate or large magnitude earthquake events. In Thailand, small earthquake can severely damage the unprepared area. This study, the daily day- and nighttime data of MODIS MOD11A1 product for 30 days before and 15 days after the earthquake on April 22, 2007, in Wiang Pa Pao District, Chiang Rai Province, Thailand, were processed and analysed to locate possibility of thermal anomalies. Thermal anomalies before and after the earthquakes were detected using the comparative method. The result found that the thermal anomaly temperature could be high up to 4.1 - 10.9 C which occurred in 21 - 22 days prior to the earthquake. Therefore, it may conclude that small earthquake can also release energy as the detectable thermal anomaly. However, more study about the relationship between thermal precursor and earthquake is needed to continue.

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Parinussa, R. M., T. R. H. Holmes e W. T. Crow. "The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations". Hydrology and Earth System Sciences Discussions 8, n.º 4 (11 de julho de 2011): 6683–719. http://dx.doi.org/10.5194/hessd-8-6683-2011.

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Abstract. For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the modern Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and Windsat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and Windsat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and Windsat to obtain surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer. Because of this, ancillary – and potentially less accurate – sources of surface temperature information (e.g. re-analysis data from operational weather prediction centers) must be sought to produce surface soil moisture retrievals. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the R value data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature predictions on the accuracy of Windsat and AMSR-E surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of Ka-band radiometric land surface temperature leads to better soil moisture anomaly estimates compared to those retrieved using MERRA land surface temperature predictions. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates retrieved using MERRA land surface temperature are superior. In addition, the surface temperature phase shifting approach is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a high degree of consistency is noted between evaluation results produced by the TC and Rvalue soil moisture verification approaches.

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Parinussa, R. M., T. R. H. Holmes, M. T. Yilmaz e W. T. Crow. "The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations". Hydrology and Earth System Sciences 15, n.º 10 (17 de outubro de 2011): 3135–51. http://dx.doi.org/10.5194/hess-15-3135-2011.

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Abstract. For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the Rvalue data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7%) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7%) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (R2 = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results.

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Burkard, E., D. Bulatov e B. Kottler. "TOWARDS DETECTION OF THERMAL ANOMALIES IN LARGE URBAN AREAS USING SIMULATION". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (14 de agosto de 2020): 1195–202. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1195-2020.

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Abstract. Anomaly detection in imagery has widely been studied and enhanced towards the requirements of today’s available sensor data, whereas many of them require a background estimation in order to identify an anomaly or target. In this paper, we examine an analysis of simulation as background estimator for anomaly detection in thermal images of urban sceneries. We generate a surface temperature image and a sensor-like infrared image by combined image and elevation data and a thermal model suited for large scenes and fast simulation. With the simulated thermal image, we define anomalies as deviation between measurement and simulation. Pixel-wise image differencing of the measured and simulated temperatures and infrared images respectively are performed and evaluated concerning the full images as well as class-wise, including a material classification of the observed area. Our approach shows complementary results compared to RXD application on the measured infrared images. Metal roofs which appear warm in the thermal image and are not visually distinguishable from the residual image are detected.

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Liu, Jie, Peng Wang, Dexun Jiang, Jun Nan e Weiyu Zhu. "An integrated data-driven framework for surface water quality anomaly detection and early warning". Journal of Cleaner Production 251 (abril de 2020): 119145. http://dx.doi.org/10.1016/j.jclepro.2019.119145.

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Flach, Milan, Fabian Gans, Alexander Brenning, Joachim Denzler, Markus Reichstein, Erik Rodner, Sebastian Bathiany et al. "Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques". Earth System Dynamics 8, n.º 3 (8 de agosto de 2017): 677–96. http://dx.doi.org/10.5194/esd-8-677-2017.

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Abstract. Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

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Abdelazeem, Maha, e Mohamed M. Gobashy. "A solution to unexploded ordnance detection problem from its magnetic anomaly using Kaczmarz regularization". Interpretation 4, n.º 3 (1 de agosto de 2016): SH61—SH69. http://dx.doi.org/10.1190/int-2016-0001.1.

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Old military events pose a critical and severe problem for many countries, including the Egyptian northwestern coast. These result in extensive areas of surface/subsurface landmines that affect the economic development plans of many countries. Detection of these landmines becomes a target for many geophysical research teams. Currently, unconventional near-surface flight technologies, such as quad-hexacopters instead of regular land surveys, are used for safety reasons in the acquisition phase. We have introduced a new processing and modeling technique of magnetic data conducted over mines or near-surface geophysical targets for accurate and precise determination of location and depth. The technique is based on the application of the Kaczmarz regularization method to the ill-posed magnetic inverse problem. The advantage of this method is the optimum transformation of regularized normal equations to an equivalent augmented regularized normal system of equations. The condition number of the updated system, which determines the degree of ill posedness, is greatly lower than the original one; this improves and guarantees a good solution to the system. The method is applied to an unexploded ordnance (UXO) test site in the United Kingdom. Our results have determined that the technique is appropriate and promising in efficiently addressing a wide number of problems that are important to near-surface geophysicists, including UXO detection.

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Wang, Nan, Bo Li, Qizhi Xu e Yonghua Wang. "Automatic Ship Detection in Optical Remote Sensing Images Based on Anomaly Detection and SPP-PCANet". Remote Sensing 11, n.º 1 (29 de dezembro de 2018): 47. http://dx.doi.org/10.3390/rs11010047.

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Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.

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Kadam, Vaibhav, Satish Kumar, Arunkumar Bongale, Seema Wazarkar, Pooja Kamat e Shruti Patil. "Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products". Applied System Innovation 4, n.º 2 (14 de maio de 2021): 34. http://dx.doi.org/10.3390/asi4020034.

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In the era of Industry 4.0, the idea of 3D printed products has gained momentum and is also proving to be beneficial in terms of financial and time efforts. These products are physically built layer-by-layer based on the digital Computer Aided Design (CAD) inputs. Nonetheless, 3D printed products are still subjected to defects due to variation in properties and structure, which leads to deterioration in the quality of printed products. Detection of these errors at each layer level of the product is of prime importance. This paper provides the methodology for layer-wise anomaly detection using an ensemble of machine learning algorithms and pre-trained models. The proposed combination is trained offline and implemented online for fault detection. The current work provides an experimental comparative study of different pre-trained models with machine learning algorithms for monitoring and fault detection in Fused Deposition Modelling (FDM). The results showed that the combination of the Alexnet and SVM algorithm has given the maximum accuracy. The proposed fault detection approach has low experimental and computing costs, which can easily be implemented for real-time fault detection.

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Yi, J., Y. Du, Z. He e C. Zhou. "Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly". Ocean Science Discussions 10, n.º 2 (29 de abril de 2013): 825–51. http://dx.doi.org/10.5194/osd-10-825-2013.

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Abstract. Automated methods are important for automatically detecting mesoscale eddies in large volumes of altimeter data. While many algorithms have been proposed in the past, this paper presents a new method, called Hybrid Detection (HD), to enhance the eddy detection accuracy and the capability of recognizing eddies' multi-core structures from maps of sea level anomaly (SLA) by integrating the ideas of the Okubo–Weiss (OW) method and the sea-surface-height-based (SSH-based) method, two well-known eddy detection algorithms. Detection evaluation using an objective validation protocol shows that the HD method owns ~ 96.6% successful detection rate and ~ 14.2% excessive detection rate, which outperforms the OW method and other methods that identify eddies by SLA extrema and confirms the improvement in detection accuracy. The capability of recognizing multi-core structures and its significance in tracking eddies' splitting or merging events have been well illustrated by comparing with other detection algorithms and historical studies.

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Li, Huo, Goldberg, Chu, Yin e Hammond. "Embracing Crowdsensing: An Enhanced Mobile Sensing Solution for Road Anomaly Detection". ISPRS International Journal of Geo-Information 8, n.º 9 (13 de setembro de 2019): 412. http://dx.doi.org/10.3390/ijgi8090412.

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Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests’ results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster’s member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems.

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Yi, J., Y. Du, Z. He e C. Zhou. "Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly". Ocean Science 10, n.º 1 (10 de fevereiro de 2014): 39–48. http://dx.doi.org/10.5194/os-10-39-2014.

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Abstract. Automated methods are important for automatically detecting mesoscale eddies in large volumes of altimeter data. While many algorithms have been proposed in the past, this paper presents a new method, called hybrid detection (HD), to enhance the eddy detection accuracy and the capability of recognizing eddy multi-core structures from maps of sea level anomaly (SLA). The HD method has integrated the criteria of the Okubo–Weiss (OW) method and the sea surface height-based (SSH-based) method, two commonly used eddy detection algorithms. Evaluation of the detection accuracy shows that the successful detection rate of HD is ~ 96.6% and the excessive detection rate is ~ 14.2%, which outperforms the OW and those methods using SLA extrema to identify eddies. The capability of recognizing multi-core structures and its significance in tracking eddy splitting or merging events have been illustrated by comparing with the detection results of different algorithms and observations in previous literature.

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Sledz, A., e C. Heipke. "THERMAL ANOMALY DETECTION BASED ON SALIENCY ANALYSIS FROM MULTIMODAL IMAGING SOURCES". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2021 (17 de junho de 2021): 55–64. http://dx.doi.org/10.5194/isprs-annals-v-1-2021-55-2021.

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Abstract. Thermal anomaly detection has an important role in remote sensing. One of the most widely used instruments for this task is a Thermal InfraRed (TIR) camera. In this work, thermal anomaly detection is formulated as a salient region detection, which is motivated by the assumption that a hot region often attracts attention of the human eye in thermal infrared images. Using TIR and optical images together, our working hypothesis is defined in the following manner: a hot region that appears as a salient region only in the TIR image and not in the optical image is a thermal anomaly. This work presents a two-step classification method for thermal anomaly detection based on an information fusion of saliency maps derived from both, TIR and optical images. Information fusion, based on the Dempster-Shafer evidence theory, is used in the first phase to find the location of regions suspected to be thermal anomalies. This classification problem is formulated as a multi-class problem and is carried out in an unsupervised manner on a pixel level. In the following phase, classification is formulated as a binary region-based problem in order to differentiate between normal temperature variations and thermal anomalies, while Random Forest (RF) is chosen as the classifier. In the seconds phase, the classification results from the previous phase are used as features along with temperature information and height details, which are obtained from a Digital Surface Model (DSM). We tested the approach using a dataset, which was collected from a UAV with TIR and optical cameras for monitoring District Heating Systems (DHS). Despite some limitations outlined in the paper, the presented innovative method to identify thermal anomalies has achieved up to 98.7 percent overall accuracy.

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Bellaoui, Mebrouk, Abdelatif Hassini e Kada Bouchouicha. "Remote Sensed Land Surface Temperature Anomalies for Earthquake Prediction". International Journal of Engineering Research in Africa 31 (julho de 2017): 120–34. http://dx.doi.org/10.4028/www.scientific.net/jera.31.120.

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Thermal anomaly Detection prior to earthquake events has been widely confirmed by researchers over the past decade. In this paper, we use robust satellite technique approach (RST) on a collection of six years of MODIS satellite data, representing land surface temperature (LST) images to predict 21st May 2003 Boumerdès Algeria earthquake. The thermal anomalies results were compared with the ambient temperature variation measured in three meteorological stations of Algerian National Office of Meteorology (ONM) (DELLYS-AFIR, TIZI-OUZOU, and DAR-EL-BEIDA). The results confirm the importance of robust satellite technique as an approach highly effective for monitoring the earthquakes.

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Pánisová, Jaroslava, e Roman Pašteka. "The use of microgravity technique in archaeology: A case study from the St. Nicolas Church in Pukanec, Slovakia". Contributions to Geophysics and Geodesy 39, n.º 3 (1 de janeiro de 2009): 237–54. http://dx.doi.org/10.2478/v10126-009-0009-1.

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The use of microgravity technique in archaeology: A case study from the St. Nicolas Church in Pukanec, SlovakiaThe detection of subsurface cavities, such as crypts, cellars and tunnels, in churches and castles belongs to successful applications of the employment of surface gravity measurement techniques in archaeo-prospecting. The old historic building exploration requires using of non-invasive methods, and hence the microgravity technique is a proper candidate for this task. On a case study from the Roman-Catholic Church of St. Nicolas in the town Pukanec the results of using microgravity for detection and delineation of local density variations caused by a near-surface void are shown. The acquired negative anomaly in the residual Bouguer anomalies field suggested the presence of a possible void feature. Euler deconvolution and 3D modelling were used to estimate the depth and shape of the anomalous source. Additionally, measurements of the vertical gravity gradient on several stations were performed. We tested how the use of a downward continuation of gravity, utilizing the real vertical gravity gradient, influences the shape and amplitude of the final Bouguer anomaly map.

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Chou, Hsi-Chiang. "Concrete Object Anomaly Detection Using a Nondestructive Automatic Oscillating Impact-Echo Device". Applied Sciences 9, n.º 5 (4 de março de 2019): 904. http://dx.doi.org/10.3390/app9050904.

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The goal of this study was to develop an impact-echo device that can conduct automatic oscillation tests, process signals rapidly, and apply it to concrete object anomaly analysis. The system presented in this study comprises three parts, namely the impact device, the oscillator circuit, and signal processing software. The design concept of the impact-echo device was inspired by a pendulum clock, and its implementation used a nondestructive wooden hammer instead of a conventional manual steel hammer. In this study, we used a pulse generator in the adjustable oscillator circuit to produce delayed changes. The delayed changes would activate the wooden hammer that struck the surface of the object. To process the signal, our lab used a built-in sound card in the computer to transfer the reflection soundwave from striking the wall to MATLAB software to analyze the energy of the frequency spectrum. This was conducted to evaluate whether the object contained anomalies and, if so, to determine the location of the anomalies to serve as a reference for real-life implementation.

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ONO, Hye-Sook Park, Tetsuzo YASUNARI, Riko OKI e Toshinori ODA. "Detection of the Urban Climatic Component Based on the Seasonal Variations of Surface Air Temperature Anomaly". Geographical Review of Japa,. Ser. A, Chirigaku Hyoron 67, n.º 8 (1994): 561–74. http://dx.doi.org/10.4157/grj1984a.67.8_561.

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Thanh, Luong Duy, Nguyen Canh Thai, Nguyen Manh Hung, Nguyen Cong Thang e Luong Thi Thanh Huong. "SELF-POTENTIAL METHOD FOR DETECTION OF WATER LEAKAGE THROUGH DAMS". Earth Science Malaysia 4, n.º 2 (7 de outubro de 2020): 152–55. http://dx.doi.org/10.26480/esmy.02.2020.152.155.

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The self-potential (SP) method is a passive, non-invasive and cost-effective geophysical method based on the measurement of electrical potential naturally occurring on the earth’s surface. One of the main causes for the electrical potential at the earth’s surface is water seepage under the ground. In this work, we perform the SP measurement on a small artificial earthen dam built at Thuyloi University. Our result shows that the selection of electrode types is crucial in the SP measurements. Namely, Cu/CuSO4 porous pots are much better than copper stake electrodes for the SP measurement. Additionally, it is shown that the SP measurement using suitable electrodes can be applied to detect underground water leakage and flow direction in the dam based on an anomaly and variation of electric potential with position on the survey area.

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Kabak, S. L., V. V. Zatochnaya, Yu M. Mel’nichenko, N. A. Savrasova e E. A. Dorokh. "FOSSA NAVICULARIS MAGNA AT THE SKULL BASE: EMBRYOGENESIS AND ITS DETECTION BY COMPUTED TOMOGRAPHY". Journal of radiology and nuclear medicine 99, n.º 3 (27 de julho de 2018): 153–57. http://dx.doi.org/10.20862/0042-4676-2018-99-3-153-157.

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Fossa navicularis magna was detected in multislice spiral computed tomography in two patients who turned to the medical centers with pathology of the paranasal sinuses. Its appearance is determined during the development of the basilar part of the occipital bone and the body of the sphenoid bone in embryogenesis. This fossa has the appearance of an edge defect on the ventral surface of the clivus in CBCT scans. Practical radiologist should interpret such a finding as a congenital anomaly of development, but not as an invasive lesion.

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Roberts, Steven Andrew. "A Shape‐Based Local Spatial Association Measure (LISShA): A Case Study in Maritime Anomaly Detection". Geographical Analysis 51, n.º 4 (19 de novembro de 2018): 403–25. http://dx.doi.org/10.1111/gean.12178.

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Nakatsuka, Tadashi, Mitsuru Utsugi, Shigeo Okuma, Yoshikazu Tanaka e Takeshi Hashimoto. "Detection of aeromagnetic anomaly change associated with volcanic activity: An application of the generalized mis-tie control method". Tectonophysics 478, n.º 1-2 (novembro de 2009): 3–18. http://dx.doi.org/10.1016/j.tecto.2009.02.018.

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Kim, Tae-Ho, e Chan-Su Yang. "Preliminary Study on Detection of Marine Heat Waves using Satellite-based Sea Surface Temperature Anomaly in 2017-2018". Journal of the Korean Society of Marine Environment and Safety 25, n.º 6 (31 de outubro de 2019): 678–86. http://dx.doi.org/10.7837/kosomes.2019.25.6.678.

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Chen, Chi-Farn. "Extraction of oil slicks on the sea surface from optical satellite images by using an anomaly detection technique". Journal of Applied Remote Sensing 4, n.º 1 (1 de dezembro de 2010): 043565. http://dx.doi.org/10.1117/1.3529942.

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Mariana S., Rini, Ibnu Athoillah, Rahmawati Syahdiza, Erwin Mulyana, Findy Renggono, Tri Handoko S., Jon Arifian, Budi Harsoyo, Edvin Aldrian e Yunus Subagyo S. "Detection of dry season anomaly using radiosonde data during intensive observation period (IOP) in 2017". MATEC Web of Conferences 229 (2018): 02008. http://dx.doi.org/10.1051/matecconf/201822902008.

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In the period between July - August 2017, Indonesia experiences drought which was caused by The Australian Monsoon wind. Radiosonde data obtained from launches conducted at the Palembang Meteorological Station during the IOP of 17 July - 16 August 2017 and those from cities that represents monsoon area (Pangkalpinang, Jakarta, and Surabaya) were also added to analyze the connection between Australian monsoon and precipitation in Indonesia. During IOP, the Australian Monsoon Index (AUSMI) is weaker than during normal conditions. Australian Monsoon index is normally around 6 m/s. Here, The Australian Monsoon Index chart shows a sinusoidal pattern in which during the peaks and troughs of the index there were drought anomalies in the aforementioned cities. In addition, medium to heavy rainfall also occurs during the Australian Monsoon index peaks and troughs. That conditions are affected by MJO and local influence. When MJO is a negative anomaly, AUSMI Index can be at peak or at the troughs. During the drought anomalies in all of the four cities, moisture profile at the surface to 6000-8000m is very wet (65-100%) with vertical wind profile dominated by the southeasterly-southerly direction.

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Ochadlick, Andrew R. "Magnetic exploration of ocean crust for craters of impact origin: Model results". GEOPHYSICS 56, n.º 8 (agosto de 1991): 1153–57. http://dx.doi.org/10.1190/1.1443134.

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Magnetic data sets over deep ocean areas may contain clues to the existence of craters formed by the impact of an extraterrestrial body with the Earth’s ocean crust. To aid in the magnetic exploration of the ocean crust for oceanic impact craters, basic but effective computations from an impact model are studied from an aeromagnetic point of view. The main assumption of the analysis is that a sufficiently large impact can excavate large volumes of magnetized basalt, vaporize basalt, and raise basalt to temperatures above the Curie temperature (approximately 500°C) to alter the preimpact magnetization of the ocean floor and result in a magnetic anomaly being associated with an oceanic impact crater. In the absence of an existing theory on the influence of impacts on ocean crustal magnetization, the representation of a crater on the ocean floor by a simple potential provides, apparently for the first time, quantitative estimates of the crater’s magnetic anomaly along a horizontal surface. Numerical results from the model suggest that the detection of the anomaly of a Cretaceous‐Tertiary (K-T) type of impact is well within the capabilities of aeromagnetic technology.

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Marlton, G. J., P. D. Williams e K. A. Nicoll. "On the detection and attribution of gravity waves generated by the 20 March 2015 solar eclipse". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, n.º 2077 (28 de setembro de 2016): 20150222. http://dx.doi.org/10.1098/rsta.2015.0222.

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Internal gravity waves are generated as adjustment radiation whenever a sudden change in forcing causes the atmosphere to depart from its large-scale balanced state. Such a forcing anomaly occurs during a solar eclipse, when the Moon’s shadow cools part of the Earth’s surface. The resulting atmospheric gravity waves are associated with pressure and temperature perturbations, which in principle are detectable both at the surface and aloft. In this study, surface pressure and temperature data from two UK sites at Reading and Lerwick are examined for eclipse-driven gravity wave perturbations during the 20 March 2015 solar eclipse over northwest Europe. Radiosonde wind data from the same two sites are also analysed using a moving parcel analysis method, to determine the periodicities of the waves aloft. On this occasion, the perturbations both at the surface and aloft are found not to be confidently attributable to eclipse-driven gravity waves. We conclude that the complex synoptic weather conditions over the UK at the time of this particular eclipse helped to mask any eclipse-driven gravity waves. This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’.

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