Microseismic events enhancement and detection in sensor arrays using autocorrelation‐based filtering

Liu, Entao; Zhu, Lijun; Raj, Anupama Govinda; McClellan, J.H.; Al-Shuhail, Abdullatif A.; Kaka, SanLinn I.; Iqbal, Naveed

doi:10.1111/1365-2478.12491

Cited by 19 publications

(2 citation statements)

References 23 publications

(24 reference statements)

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“…4.2. Moreover, our approach lends itself to the extension to coloured noise, which is most likely in realistic seismic data (Liu et al, 2017).…”

Section: Inferencementioning

confidence: 99%

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events

Piras,

Mancini,

Joachimi

et al. 2021

Preprint

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Bayesian inference applied to microseismic activity monitoring allows for principled estimation of the coordinates of microseismic events from recorded seismograms, and their associated uncertainties. However, forward modelling of these microseismic events, necessary to perform Bayesian source inversion, can be prohibitively expensive in terms of computational resources. A viable solution is to train a surrogate model based on machine learning techniques, to emulate the forward model and thus accelerate Bayesian inference. In this paper, we improve on previous work, which considered only sources with isotropic moment tensor. We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows for the complete and fast retrieval of the event coordinates for any source mechanism. Moreover, we show that our approach is computationally inexpensive, as it can be run in less than 1 hour on a commercial laptop, while yielding accurate results using less than 10 4 training seismograms. We additionally demonstrate how the trained emulators can be used to identify the source mechanism through the estimation of the Bayesian evidence. This work lays the foundations for the efficient localisation and characterisation of any recorded seismogram, thus helping to quantify human impact on seismic activity and mitigate seismic hazard.

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“…4.2. Moreover, our approach lends itself to the extension to coloured noise, which is most likely in realistic seismic data (Liu et al, 2017).…”

Section: Inferencementioning

confidence: 99%

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events

Piras,

Mancini,

Joachimi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…[14] Kai et al (2014) utilized from particle swarm optimization algorithm (an algorithm of data mining) to forecast seismic events. They applied forecasting process on the seismic database and created a significant improvement (Cai and et al, 2014) [15], Landerb et al (2015) investigated the relationship between sea depth, volcano and earthquake using geophysical approaches of data mining and proposed a method to forecast the occurrence of these seismic events [16]. Mark Last et al (2016) emphasized on forecasting seismic events in Israel and its neighbors.…”

Section: Micro Seismicitymentioning

confidence: 99%

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

Ghorbani

Barari

Hosseini

2017

cej

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Various natural disasters such as floods, fires, earthquakes, etc. have affected human life. Detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to Earth scientist. One of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. Based on latest achievements of Data Mining international institutions such as Rexer-KDnugget-Gartner and also newest authentic articles, SVM, KNN, C4.5, MLP are from most important and popular and leading classifiers in data world. Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ،C4.5 decision tree and KNN near neighbourhood have been utilized in a combined form to detect and categorize micro seismic events. In general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting pre-processing and separating training and testing samples, 4) generating relevant models with training samples and detecting and clustering test samples and 5) extracting a cluster with the maximum candidate using boost learning. After simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. Accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively.

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Advanced Topics

Al-Shuhail¹,

Al-Dossary²

2019

Attenuation of Incoherent Seismic Noise

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Microseismic events enhancement and detection in sensor arrays using autocorrelation‐based filtering

Cited by 19 publications

References 23 publications

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events

Towards fast machine-learning-assisted Bayesian posterior inference of realistic microseismic events

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

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