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#1Michael W. Dunham (MUN: Memorial University of Newfoundland)H-Index: 1
#2Alison E. Malcolm (MUN: Memorial University of Newfoundland)H-Index: 16
Last. J. Kim Welford (MUN: Memorial University of Newfoundland)H-Index: 10
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Abstract Well log classification, the process of mapping well log measurements to lithofacies identified from core samples, is a common procedure in the oil and gas industry. Manually assigning lithofacies to the wire-line log measurements without core can be time consuming, and can also introduce a bias. Supervised machine learning algorithms are commonly used to automate this process, but they are prone to overfitting when the training data are scarce, which is common for well log classificati...
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#2Flávio L. Santana (CNPq: National Council for Scientific and Technological Development)
Last. Aderson F. do Nascimento (CNPq: National Council for Scientific and Technological Development)H-Index: 15
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Abstract The use of methods using waveform stacking are nowadays more common in microseismic monitoring applications because they avoid manual or automatic phase picking. The Source Scanning Algorithm (SSA) is a widely known technique in which the source location is estimated using a brightness function obtained from stacking the normalized absolute amplitude seismograms recorded at several stations. The SSA has the advantage of the straightforwardness of its implementation but has the inconveni...
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#1Yihui Xiong (China University of Geosciences)H-Index: 2
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Abstract The recognition of multivariate geochemical anomalies is important for mineral exploration. Big data analytics, which involves the whole data and variables, is an alternative manner to delineate multivariate geochemical anomalies in support of machine learning algorithms due to their strong ability to capture the complex intrinsic and diverse links between geochemical characteristics and mineralization. However, this method faces the issue of data redundancy and calculation complexity, ...
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#1Jens A. de BruijnH-Index: 1
#2Hans de MoelH-Index: 15
Last. Jeroen C. J. H. AertsH-Index: 52
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Abstract While text classification can classify tweets, assessing whether a tweet is related to an ongoing flood event or not, based on its text, remains difficult. Inclusion of contextual hydrological information could improve the performance of such algorithms. Here, a multilingual multimodal neural network is designed that can effectively use both textual and hydrological information. The classification data was obtained from Twitter using flood-related keywords in English, French, Spanish an...
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Abstract This paper presents an ontology-driven representation of knowledge for geological maps. The ontological formal language allows for a machine-readable encoding of the Earth scientist's interpretation through semantic categories and properties and is credited to support knowledge sharing and interoperability. We introduce an ontology-driven method for the interpretation and the encoding of the map data that employs shared vocabularies and resources encoded through ontologies in order to p...
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#1Łukasz Maciąg (University of Szczecin)H-Index: 1
Last. Jan Harff (University of Szczecin)H-Index: 17
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Abstract Multivariate geostatistical methods were employed to describe the local facies of surface pelagic sediments from the Interoceanmetal IOM H11 and H22 perspective areas. Using these methods, we analyze spatial variability in seafloor features, sediment grain size, and mineralogical and geochemical composition. The data were acquired from the seafloor sediments sampled by box corer. Laboratory methods included laser grain size analysis, X-Ray diffractometry and Inducted Coupled Plasma Mass...
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Last. Claudio I. Meier (U of M: University of Memphis)H-Index: 8
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Abstract Long, continuously-gaged precipitation records are needed for characterizing extreme rainfall events over sub-daily durations, as well as detecting potential historical effects of climate change. However, most such records predating the 1980s are in the form of paper strip charts (pluviograms), from which it is notoriously difficult to extract extreme precipitation depths for short durations (from a few minutes to a few hours). We propose a highly accurate method for digitizing weekly s...
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Abstract Convolution Neural Networks (CNN) have demonstrated a high level of performance in the areas of image recognition and classification. The training of such networks over large corpora of imagery has facilitated additional applications such as content-based image searching and retrieval. Here we investigate the efficacy of applying a pre-trained deep CNN to the task of content searching within large environmental datasets. It is demonstrated that the learned convolution filters from a pre...
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#1Maogui Hu (CAS: Chinese Academy of Sciences)
#2Yanwei Huang (CAS: Chinese Academy of Sciences)
Abstract Geostatistical interpolation methods are used in diverse disciplines, such as environmental science, ecology, and hydrology. With the increasing availability of areal spatial data, area-to-area and area-to-point interpolations have great application potential. In this study, based on the variogram deconvolution algorithm proposed by Goovaerts (2008), an open-source area-to-area kriging package atakrig is developed in the R environment. In atakrig, point-scale variogram and cross-variogr...
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#1Luigi RanghettiH-Index: 5
#2Mirco BoschettiH-Index: 23
Last. Lorenzo BusettoH-Index: 22
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Abstract is a scalable and flexible R package to enable downloading and preprocessing of Sentinel-2 satellite imagery via an accessible and easy to install interface. It allows the execution of several preprocessing steps which are commonly performed by Sentinel-2 users: searching the Sentinel-2 archive for datasets available over a spatial area of interest and in a defined time window, downloading them, applying the Sen2Cor atmospheric correction algorithm to compute surface reflectances, mergi...
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