scholarly journals Expression of Concern: Automatic high-resolution microseismic event detection via supervised machine learning

2020 ◽  
Vol 221 (3) ◽  
pp. 2056-2056
Author(s):  
Shan Qu ◽  
Zhe Guan ◽  
Eric Verschuur ◽  
Yangkang Chen
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Event Detection ◽  
Supervised Machine Learning ◽  
Microseismic Event

scholarly journals Automatic high-resolution microseismic event detection via supervised machine learning

2019 ◽  
Vol 218 (3) ◽  
pp. 2106-2121 ◽  
Author(s):  
Shan Qu ◽  
Zhe Guan ◽  
Eric Verschuur ◽  
Yangkang Chen
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Event Detection ◽  
Supervised Machine Learning ◽  
Microseismic Event

scholarly journals Automatic high-resolution microseismic event detection via supervised machine learning

2020 ◽  
Vol 222 (3) ◽  
pp. 1881-1895 ◽  
Author(s):  
Shan Qu ◽  
Zhe Guan ◽  
Eric Verschuur ◽  
Yangkang Chen
Keyword(s):  
Feature Selection ◽  
High Resolution ◽  
Event Detection ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Gaussian Kernel ◽  
Selection Strategy ◽  
Support Vector ◽  
Microseismic Events ◽  
Microseismic Event

SUMMARY Microseismic methods are crucial for real-time monitoring of the hydraulic fracturing dynamic status during the development of unconventional reservoirs. However, unlike the active-source seismic events, the microseismic events usually have low signal-to-noise ratio (SNR), which makes its data processing challenging. To overcome the noise issue of the weak microseismic events, we propose a new workflow for high-resolution microseismic event detection. For the preprocessing, fix-sized segmentation with a length of 2*wavelength is used to divide the data into segments. Later on, 191 features have been extracted and used as the input data to train the support vector machine (SVM) model. These features include 63 1-D time/spectral-domain features, and 128 2-D texture features, which indicate the continuity, smoothness, and irregularity of the events/noise. The proposed feature extraction maximally exploits the limited information of each segment. Afterward, we use a combination of univariate feature selection and random-forest-based recursive feature elimination for feature selection to avoid overfitting. This feature selection strategy not only finds the best features, but also decides the optimal number of features that are needed for the best accuracy. Regarding the training process, SVM with a Gaussian kernel is used. In addition, a cross-validation (CV) process is implemented for automatic parameter setting. In the end, a group of synthetic and field microseismic data with different levels of complexity show that the proposed workflow is much more robust than the state-of-the-art short-term-average over long-term-average ratio (STA/LTA) method and also performs better than the convolutional-neural-networks (CNN), for this case where the amount of training data sets is limited. A demo for the synthetic example is available: https://github.com/shanqu91/ML_event_detection_microseismic.

scholarly journals Augmenting biologging with supervised machine learning to studyin situbehavior of the medusaChrysaora fuscescens

2019 ◽  
Author(s):  
Clara Fannjiang ◽  
T. Aran Mooney ◽  
Seth Cones ◽  
David Mann ◽  
K. Alex Shorter ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Laboratory Data ◽  
Supervised Machine Learning ◽  
Motion Sensors ◽  
Fine Scale ◽  
Video Footage ◽  
Research Findings ◽  
Summary Statement

AbstractZooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individualsin situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studyingin situbehavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8Chrysaora fuscescensin Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers onin siturather than laboratory data is essential.Summary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scalein situbehavior of zooplankton for long durations.

Artificial intelligence for discrimination of sediment facies based on high-resolution elemental and colour data from coastal sediments of the East Frisian Wadden Sea, Germany

2020 ◽  
Author(s):  
An-Sheng Lee ◽  
Dirk Enters ◽  
Sofia Ya Hsuan Liou ◽  
Bernd Zolitschka
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Wadden Sea ◽  
Environmental Variability ◽  
Conventional Approach ◽  
Coastal Sediments ◽  
Supervised Machine Learning ◽  
Chemical Information ◽  
Xrf Core Scanner ◽  
Physical And Chemical

<p>Sediment facies provide vital information for the reconstruction of past environmental variability. Due to rising interest for paleoclimate data, sediment surveys are continually growing in importance as well as the amount of sediments to be discriminated into different facies. The conventional approach is to macroscopically determine sediment structure and colour and combine them with physical and chemical information - a time-consuming task heavily relying on the experience of the scientist in charge. Today, rapidly generated and high-resolution multiproxy sediment parameters are readily available from down-core scanning techniques and provide qualitative or even quantitative physical and chemical sediment properties. In 2016, an interdisciplinary research project WASA (Wadden Sea Archive) was launched to investigate palaeo-landscapes and environments of the Wadden Sea. The project has recovered 92 up to 5 m long sediment cores from the tidal flats, channels and off-shore around the island of Norderney (East Frisian Wadden Sea, Germany). Their facies were described by the conventional approach into glacioflucial sands, moraine, peat, tidal deposits, shoreface sediments, etc. In this study, those sediments were scanned by a micro X-ray fluorescence (µ-XRF) core scanner to obtain high-resolution records of multi-elemental data (2000 µm) and optical images (47 µm). Here we propose a supervised machine-learning application for the discrimination of sediment facies using these scanning data. Thus, the invested time and the potential bias common for the conventional approach can be reduced considerably. We expect that our approach will contribute to developing a more comprehensive and time-efficient automatic sediment facies discrimination.</p><p>Keywords: the Wadden Sea, µ-XRF core scanning, machine-learning, sediment facies discrimination</p>

scholarly journals Interpreting High-resolution Spectroscopy of Exoplanets using Cross-correlations and Supervised Machine Learning

2020 ◽  
Vol 159 (5) ◽  
pp. 192 ◽  
Author(s):  
Chloe Fisher ◽  
H. Jens Hoeijmakers ◽  
Daniel Kitzmann ◽  
Pablo Márquez-Neila ◽  
Simon L. Grimm ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Supervised Machine Learning ◽  
High Resolution Spectroscopy ◽  
Cross Correlations

scholarly journals MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 891-898 ◽  
Author(s):  
Ye Lv ◽  
Guofeng Wang ◽  
Xiangyun Hu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Learning Algorithm ◽  
Mean Shift ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
City Development ◽  
Remote Sensing Images ◽  
High Resolution Images

At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.

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