scholarly journals Imputation of GPS Coordinate Time Series Using MissForest

2021 ◽  
Vol 13 (12) ◽  
pp. 2312
Author(s):  
Shengkai Zhang ◽  
Li Gong ◽  
Qi Zeng ◽  
Wenhao Li ◽  
Feng Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Missing Values ◽  
Mean Squared Error ◽  
Principal Component ◽  
Polar Regions ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Gps Time Series ◽  
Relationship Of

The global positioning system (GPS) can provide the daily coordinate time series to help geodesy and geophysical studies. However, due to logistics and malfunctioning, missing values are often “seen” in GPS time series, especially in polar regions. Acquiring a consistent and complete time series is the prerequisite for accurate and reliable statical analysis. Previous imputation studies focused on the temporal relationship of time series, and only a few studies used spatial relationships and/or were based on machine learning methods. In this study, we impute 20 Greenland GPS time series using missForest, which is a new machine learning method for data imputation. The imputation performance of missForest and that of four traditional methods are assessed, and the methods’ impacts on principal component analysis (PCA) are investigated. Results show that missForest can impute more than a 30-day gap, and its imputed time series has the least influence on PCA. When the gap size is 30 days, the mean absolute value of the imputed and true values for missForest is 2.71 mm. The normalized root mean squared error is 0.065, and the distance of the first principal component is 0.013. MissForest outperforms the other compared methods. MissForest can effectively restore the information of GPS time series and improve the results of related statistical processes, such as PCA analysis.

scholarly journals Discontinuity Detection in GNSS Station Coordinate Time Series Using Machine Learning

2021 ◽  
Vol 13 (19) ◽  
pp. 3906
Author(s):  
Laura Crocetti ◽  
Matthias Schartner ◽  
Benedikt Soja
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Learning Algorithms ◽  
Velocity Estimation ◽  
Machine Learning Algorithms ◽  
Global Navigation Satellite Systems ◽  
External Information ◽  
Machine Learning Classification ◽  
Coordinate Time ◽  
Coordinate Time Series

Global navigation satellite systems (GNSS) provide globally distributed station coordinate time series that can be used for a variety of applications such as the definition of a terrestrial reference frame. A reliable estimation of the coordinate time series trends gives valuable information about station movements during the measured time period. Detecting discontinuities of various origins in such time series is crucial for accurate and robust velocity estimation. At present, there is no fully automated standard method for detecting discontinuities. Instead, discontinuity-catalogues are frequently used, which provide information about when a device was changed or an earthquake occurred. However, it is known that these catalogues suffer from incompleteness. This study investigates the suitability of machine learning classification algorithms that are fully data-driven to detect discontinuities caused by earthquakes in station coordinate time series without the need for external information. For this study, Japan was selected as a testing area. Ten different machine learning algorithms have been tested. It is found that Random Forest achieves the best performance with an F1 score of 0.77, a recall of 0.78, and a precision of 0.76. Overall, 525 of 565 recorded earthquakes in the test data were correctly classified. It is further highlighted that splitting the time series into chunks of 21 days leads to the best performance. Furthermore, it is beneficial to combine the three (normalized) components of the GNSS solution into one sample, and that adding the value range as an additional feature improves the result. Thus, this work demonstrates how it is possible to use machine learning algorithms to detect discontinuities in GNSS time series.

scholarly journals Detecting earthquakes in GNSS station coordinate time series using machine learning algorithms

2021 ◽  
Author(s):  
Laura Crocetti ◽  
Matthias Schartner ◽  
Benedikt Soja
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Methods ◽  
Large Scale ◽  
Learning Algorithms ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Coordinate Time ◽  
Coordinate Time Series

<p>Earthquakes are natural hazards that occur suddenly and without much notice. The most established method of detecting earthquakes is to use a network of seismometers. Nowadays, station positions of the global navigation satellite system (GNSS) can be determined with a high accuracy of a few centimetres or even millimetres. This high accuracy, together with the dense global coverage, makes it possible to also use GNSS station networks to investigate geophysical phenomena such as earthquakes. Absolute ground movements caused by earthquakes are reflected in the GNSS station coordinate time series and can be characterised using statistical methods or machine learning techniques.</p><p>In this work, we have used thousands of time series of GNSS station positions distributed all over the world to detect and classify earthquakes. We apply a variety of machine learning algorithms that enable large-scale processing of the time series in order to identify spatio-temporal patterns. Several machine learning algorithms, including Random Forest, Nearest Neighbours, and Multi-Layer Perceptron, are compared against each other, as well as against classical statistical methods, based on their performance on detecting earthquakes from the station coordinate time series.</p>

scholarly journals A Sub-Regional Extraction Method of Common Mode Components from IGS and CMONOC Stations in China

2019 ◽  
Vol 11 (11) ◽  
pp. 1389 ◽  
Author(s):  
Shuguang Wu ◽  
Guigen Nie ◽  
Jingnan Liu ◽  
Kezhi Wang ◽  
Changhu Xue ◽  
...  
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Satellite System ◽  
Common Mode ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
North East ◽  
Comparison Results ◽  
Gps Coordinate Time Series

There is always a need to extract more accurate regional common mode component (CMC) series from coordinate time series of Global Positioning System (GPS) stations, which would be of great benefit to describe the deformation features of the Earth’s surface with more reliability. For this purpose, this paper combines all 11 International Global Navigation Satellite System (GNSS) Service (IGS) stations in China with over 70 stations selected from the Crustal Movement Observation Network of China (CMONOC) to compute CMC series of IGS stations by using a principal component analysis (PCA) method under cases of one whole region and eight sub-regions. The comparison results show that the percentage of first-order principal component (PC1) in North, East and Up components increase by 10.8%, 16.1% and 25.1%, respectively, after dividing the whole China region into eight sub-regions. Meanwhile, Root Mean Square (RMS) reduction rates of residual series that have removed CMC also improve obviously after partitioning. In addition, we compute displacements of these IGS stations caused by environmental loadings (including atmospheric pressure loading, non-tidal oceanic loading and hydrological loading) to analyze their contributions to the non-linear variation in GPS coordinate time series. The comparison result shows that the method we raise, PCA filtering in sub-regions, performs better than the environmental loading corrections (ELCs) in improving the signal-to-noise ratio (SNR) of GPS coordinate time series. This paper raises new criteria for selecting appropriate CMONOC stations around IGS stations when computing sub-regional CMC, involving three criteria of interstation distance, geology and self-condition of stations themselves. According to experiments, these criteria are implemental and effective in selecting suitable stations, by which to extract sub-regional CMC with higher accuracy.

Filling missing values of multi-station GNSS coordinate time series based on matrix completion

Measurement ◽  
2021 ◽  
pp. 109862
Author(s):  
Zhi Bao ◽  
Guobin Chang ◽  
Laihong Zhang ◽  
Guoliang Chen ◽  
Siyu Zhang
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Matrix Completion ◽  
Coordinate Time ◽  
Coordinate Time Series

A Method Based on MTLS and ILSP for GNSS Coordinate Time Series Analysis with Missing Data

2021 ◽  
Author(s):  
Yingying Ren ◽  
Hu Wang ◽  
Lizhen Lian ◽  
Jiexian Wang ◽  
Yingyan Cheng ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Analysis ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Series Analysis

Spectral analysis for GNSS coordinate time series using chirp Fourier transform

2017 ◽  
Vol 65 (6) ◽  
pp. 1111-1118
Author(s):  
Shengtao Feng ◽  
Wanju Bo ◽  
Qingzun Ma ◽  
Zifan Wang
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Fourier Transform ◽  
Coordinate Time ◽  
Coordinate Time Series

Correlating geologic and seismic data with unconventional resource production curves using machine learning

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. O39-O47 ◽  
Author(s):  
Ryan Smith ◽  
Tapan Mukerji ◽  
Tony Lupo
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Oil And Gas ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Full Time ◽  
Data Types ◽  
Production Time ◽  
Well Production ◽  
Unconventional Resource

Predicting well production in unconventional oil and gas settings is challenging due to the combined influence of engineering, geologic, and geophysical inputs on well productivity. We have developed a machine-learning workflow that incorporates geophysical and geologic data, as well as engineering completion parameters, into a model for predicting well production. The study area is in southwest Texas in the lower Eagle Ford Group. We make use of a time-series method known as functional principal component analysis to summarize the well-production time series. Next, we use random forests, a machine-learning regression technique, in combination with our summarized well data to predict the full time series of well production. The inputs to this model are geologic, geophysical, and engineering data. We are then able to predict the well-production time series, with 65%–76% accuracy. This method incorporates disparate data types into a robust, predictive model that predicts well production in unconventional resources.

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