Mimicking the process of manual sequence stratigraphy well correlation

2021 ◽  
pp. 1-65
Author(s):  
Huijing Fang ◽  
Yihuai Lou ◽  
Bo Zhang ◽  
Huaimin Xu ◽  
Man Lu
Keyword(s):  
Euclidean Distance ◽  
Well Logs ◽  
Stratigraphic Correlation ◽  
Time Warping ◽  
Error Matrix ◽  
Stratigraphic Model ◽  
Wireline Logs ◽  
Well Correlation ◽  
Dynamic Time ◽  
Error Computation

Stratigraphic correlation of well logs is based on interactive, interpreter-based pattern recognition. A skilled interpreter identifies similar patterns (such as upward fining and coarsening) in user-defined well sections and links them using either a conscious or subconscious stratigraphic model. This manual stratigraphic correlation of numerous wells in mature fields can be both time consuming and error prone. To expedite the process of stratigraphic correlation, we perform the semi-automatic stratigraphic correlation of wireline logs from multiple wells using the Improved Dynamic Time Warping (IDTW). The IDTW employs semblance, which compares the shape of the well logs, to replace the Euclidean distance in the pairwise error computation. The resulting error matrix is compatible with the lateral nonstationary variation of well logs in the same formation. The workflow begins with interpreting stratigraphic well tops on user-defined well sections that is similar to current process of stratigraphy analysis. The interpreted wells are then treated as reference wells to aid in interpreting well tops for other wells. Necessary manual interventions are incorporated during the process of the semi-automatic stratigraphic correlation. We applied the proposed method to two experimental fields: a sand-rich reservoir and a mud-rich reservoir. The applications illustrate that the proposed method performs well in aggradational strata and successfully predicts the discontinuities with manual interventions.

A semiautomatic method to tie well logs to seismic data

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. V47-V54 ◽  
Author(s):  
Roberto H. Herrera ◽  
Mirko van der Baan
Keyword(s):  
Seismic Data ◽  
Recognition Task ◽  
Original Data ◽  
Well Logs ◽  
Time Shift ◽  
Time Warping ◽  
Analysis Window ◽  
Dynamic Time ◽  
Improve Correlation ◽  
Pattern Recognition Task

We evaluated a semiautomatic method for well-to-seismic tying to improve correlation results and reproducibility of the procedure. In the manual procedure, the interpreter first creates a synthetic trace from edited well logs, determines the most appropriate bulk time shift and polarity, and then applies a minimum amount of stretching and squeezing to best match the observed data. The last step resembles a visual pattern recognition task, which often requires some experience. We replaced the last step with a constrained dynamic time-warping technique, to help guide the interpreter. The method automatically determined the appropriate amount of local stretching and squeezing to produce the highest correlation between the original data and the created synthetic trace. The constraint ensured that stretching and squeezing were kept within reasonable bounds, as determined by the interpreter. Results compared well with the manual method, leading to ties along the entire trace length in contrast to the shorter analysis window in the conventional method. Yet, we advise against unsupervised applications because the method is intended as a guide instead of a fully automated blind approach.

scholarly journals The Effectiveness of Dynamic Features of Finger Based Gestures on Smartphones’ Touchscreens for User Identification

2017 ◽  
Vol 11 (1) ◽  
pp. 133
Author(s):  
Suleyman AlShowarah
Keyword(s):  
Dynamic Time Warping ◽  
Euclidean Distance ◽  
Movement Time ◽  
Identification Accuracy ◽  
Dynamic Features ◽  
Time Warping ◽  
User Identification ◽  
Nearest Neighbour Classifier ◽  
Dynamic Time ◽  
Finger Pressure

<strong>This paper presents methodology for user identification on smartphone and mini-tablet using finger based gestures. In this paper, a set of four features, namely Signature Precision (SP), Finger Pressure (FP), Movement Time (MT), and Speed were extracted from each gesture of eight using dynamic time warping and Euclidean distance. The features are then used individually and combined for the purpose of user identification based on the Euclidean distance and the k-nearest neighbour classifier. We concluded that the best identification accuracy results from the combinations of FP and MT features where 78.46% and 78.33% were achieved on small smartphone and Mini-tablet respectively using a dataset of 50 users.</strong>

Seismic well tie by aligning impedance log with inverted impedance from seismic data

2020 ◽  
Vol 8 (4) ◽  
pp. T917-T925
Author(s):  
Bo Zhang ◽  
Yahua Yang ◽  
Yong Pan ◽  
Hao Wu ◽  
Danping Cao
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Real Data ◽  
Seismic Inversion ◽  
Synthetic Seismogram ◽  
Well Logs ◽  
Time Warping ◽  
Seismic Wavelet ◽  
Dynamic Time ◽  
Depth Relationship

The accuracy of seismic inversion is affected by the seismic wavelet and time-depth relationship generated by the process of the seismic well tie. The seismic well tie is implemented by comparing the synthetic seismogram computed from well logs and the poststack seismogram at or nearby the borehole location. However, precise waveform matching between the synthetic seismogram and the seismic trace does not guarantee an accurate tie between the elastic properties contained represented by the seismic data and well logs. We have performed the seismic well tie using the impedance log and the impedance inverted from poststack seismic data. We use an improved dynamic time warping to align the impedance log and impedance inverted from seismic data. Our workflow is similar to the current procedure of the seismic well tie except that the matching is implemented between the impedance log and the inverted impedance. The current seismic well-tie converges if there is no visible changes for the wavelets and time-depth relationship in the previous and current tying loops. Similarly, our seismic well tie converges if there are no visible changes for the wavelets, inverted impedance, and time-depth relationship in the previous and current tying loops. The real data example illustrates that more accurate inverted impedance is obtained by using the new wavelet and time-depth relationship.

scholarly journals Time Series Clustering Model based on DTW for Classifying Car Parks

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 57 ◽  
Author(s):  
Taoying Li ◽  
Xu Wu ◽  
Junhe Zhang
Keyword(s):  
Time Series ◽  
Euclidean Distance ◽  
Supply And Demand ◽  
Time Warping ◽  
Time Series Clustering ◽  
Model Based ◽  
Clustering Model ◽  
Proposed Model ◽  
Car Park ◽  
Dynamic Time

An increasing number of automobiles have led to a serious shortage of parking spaces and a serious imbalance of parking supply and demand. The best way to solve these problems is to achieve the reasonable planning and classify management of car parks, guide the intelligent parking, and then promote its marketization and industrialization. Therefore, we aim to adopt clustering method to classify car parks. Owing to the time series characteristics of car park data, a time series clustering framework, including preprocessing, distance measurement, clustering and evaluation, is first developed for classifying car parks. Then, in view of the randomness of existing clustering models, a new time series clustering model based on dynamic time warping (DTW) is proposed, which contains distance radius calculation, obtaining density of the neighbor area, k centers initialization, and clustering. Finally, some UCR datasets and data of 27 car parks are employed to evaluate the performance of the models and results show that the proposed model performs obviously better results than those clustering models based on Euclidean distance (ED) and traditional clustering models based on DTW.

Research on Seasonal Index Based on Dynamic Clustering of the Daily Railway Passenger Flow Title

2014 ◽  
Vol 981 ◽  
pp. 966-971
Author(s):  
Zheng Zheng Wei ◽  
Fu Zhang Wang
Keyword(s):  
Time Series ◽  
Euclidean Distance ◽  
Hierarchical Cluster ◽  
Time Warping ◽  
Programming Technique ◽  
Passenger Flow ◽  
Railway Passenger ◽  
Dynamic Time ◽  
The Impact ◽  
Seasonal Index

The railway passenger flow is greatly impacted by different months and weeks of the season, and the impact is periodic. Accurate evaluation of the seasonal index for predicting the railway passenger flow is of key importance. Based on this background, the paper proposes an algorithm for calculating the seasonal index which is impacted by both months and weeks. The railway passenger flow between different OD(Origination Destination) is affected by months and weeks quite different. Therefore the paper focuses on the method for effective calculation of the month index and week index on the basis of time series clustering. When adopting hierarchical cluster, general Euclidean distance and its expansion used as a similarity metric is widely applied in time series comparison, however, this distance measurement is not robust enough for the processed data. Dynamic time warping is a pattern matching algorithm based on nonlinear dynamic programming technique. It is applied to calculate month and week index to get seasonal index that defined in this paper, which has good application value for predicting the passenger flow.

scholarly journals Pattern matching under dynamic time warping for time series prediction

2019 ◽  
Vol 15 (3) ◽  
pp. 148
Author(s):  
Nguyen Thanh Son
Keyword(s):  
Time Series ◽  
Pattern Matching ◽  
Dynamic Time Warping ◽  
Prediction Accuracy ◽  
Euclidean Distance ◽  
Time Series Prediction ◽  
Nonlinear Behavior ◽  
Time Series Forecasting ◽  
Time Warping ◽  
Dynamic Time

Time series forecasting based on pattern matching has received a lot of interest in the recent years due to its simplicity and the ability to predict complex nonlinear behavior. In this paper, we investigate into the predictive potential of the method using k-NN algorithm based on R*-tree under dynamic time warping (DTW) measure. The experimental results on four real datasets showed that this approach could produce promising results in terms of prediction accuracy on time series forecasting when comparing to the similar method under Euclidean distance.

scholarly journals SPEED-INDEPENDENT GAIT IDENTIFICATION FOR MOBILE DEVICES

2012 ◽  
Vol 26 (08) ◽  
pp. 1260013 ◽  
Author(s):  
GONZALO BAILADOR DEL POZO ◽  
CARMEN SÁNCHEZ-ÁVILA ◽  
ALBERTO DE-SANTOS-SIERRA ◽  
JAVIER GUERRA-CASANOVA
Keyword(s):  
Dynamic Time Warping ◽  
Euclidean Distance ◽  
Identification Problem ◽  
Time Warping ◽  
Classical Dynamic ◽  
Intensive Use ◽  
Dynamic Time ◽  
Gait Identification ◽  
New Generation ◽  
Acceleration Signals

Due to the intensive use of mobile phones for different purposes, these devices usually contain confidential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner's gait. Nowadays, gait identification in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identification problem, they show difficulties when dealing with gaits at different speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at different velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at different motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.

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