First-Arrival Travel Times Picking through Sliding Windows and Fuzzy C-Means

Gao, Lei; Jiang, Zhen-yun; Min, Fan

doi:10.3390/math7030221

Cited by 12 publications

(3 citation statements)

References 45 publications

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“…The model's prediction was much more accurate than that of the analyst with the highest score of classification 95% in terms of precision. Gao et al studied FB picking using fuzzy C-means, where they first utilize the vertical and horizontal sliding window to determine the first-arrival range and then Particle Swarm Optimization (PSO) to locate cluster centers [19]. Unlike all studies discussed above, the authors of [20] deployed Variational Auto-Encoder (VAE) and a Generative Adversarial Network (GAN) for automatic FB picking using seismic shot gather images as input.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Models for First-break Arrival Picking

Ayub¹,

SanLinn²

2021

IJACSA

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First-break (FB) picking is an important and necessary step in seismic data processing and there is a need to develop precise and accurate auto-picking solutions. Our investigation in this study includes eight machine learning models. We use 1195 raw traces to extract several features and train for accurate picking and monitoring the performance of each model using well-defined evaluation metrics. Careful investigation of the scores shows that a single metric alone is not sufficient to evaluate the arrival picking models in real-time. Correlation analysis of predicted probabilities and predicted classes of machine learning models confirm that the performance metrics that use predicted probabilities have higher score value than those that use predicted classes. Our study which incorporates comparisons of different machine learning models based on different performance metrics, training time, and feature importance indicates that the approach we developed in this study is helpful and provides an opportunity to determine the real-time suitability of different methodologies for automatic FB arrival picking with clear deep insight. Based on performance scores, we bench-marked the Extra Tree classifier as the most efficient model for FB arrival picking with accuracy and F1score above 95%.

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Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Models for First-break Arrival Picking

Ayub¹,

SanLinn²

2021

IJACSA

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“…Sheng et al [21] developed the Shearlet Transform-Short time window/Long time window-Kurtosis (S-S/L_K) algorithm using the Shearlet transform and high-order statistics, which could accurately pick the first arrival of low-SNR microseismic signals. Gao et al [22] used a sliding window combined with the fuzzy C-means clustering algorithm to pick the first arrival of noisy and preprocessed microseismic signals. However, at a large amount of monitoring data, the above traditional firstarrival picking method cannot simultaneously meet the requirements for efficiency and accuracy of real-time microseismic monitoring.…”

Section: Introductionmentioning

confidence: 99%

First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

Guo

2021

International Journal of Geophysics

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In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms.

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“…• the "damped time window" model, where historical data weights are dynamically adjusted by fixing a rate of decay according to the number of observations assigned to it [23]; • the "sliding time window" model, where only the most recent past data observations are considered with a simple First-In-First-Out (FIFO) mechanism. as in [24]; • the "landmark time window" model, where the data stream is analysed in batches by accumulating data in a fixed width buffer before being processed; • the "tilted time window" model, where the granularity level of weights gradually decreases as data points get older.…”

Section: Introductionmentioning

confidence: 99%

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

et al. 2019

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This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses "microclusters" and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream's robustness to noise and resiliency to parameter changes.

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First-Arrival Travel Times Picking through Sliding Windows and Fuzzy C-Means

Cited by 12 publications

References 45 publications

A Comparative Analysis of Machine Learning Models for First-break Arrival Picking

A Comparative Analysis of Machine Learning Models for First-break Arrival Picking

First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation

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