A Novel Method of Seismic Signal Detection Using Waveform Features

Li, Jian; He, Mengmin; Cui, Gaofeng; Wang, Xiaoming; Wei-dong, Wang; Wang, Juan

doi:10.3390/app10082919

Cited by 9 publications

(4 citation statements)

References 32 publications

(40 reference statements)

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“…Furthermore, GP was exploited for seismic signal detection as well. According to [27], the rate of detection when using GP was higher compared to other methods and the rate of false alarm was lower especially for the weak seismic signals. Researchers in [28] proved that GP was successfully capable of being exploited in processing EM responses by calibrating the stochastic and the computer experiment models for magnitude versus offset (MVO) analysis.…”

Section: Gaussian Processmentioning

confidence: 99%

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

et al. 2021

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Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source–receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at “untried” depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and “untried” computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL.

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Section: Gaussian Processmentioning

confidence: 99%

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

et al. 2021

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“…In geophysics field, researchers in Reference [29] stated that GP can provide reliable porosity and permeability information to the well analysis. Besides that, GP was also fully utilized by researchers in Reference [30] in the seismic signal detection. The study inferred that the GP was able to produce higher rate of detection and lower rate of false alarm, especially for the weak seismic signals.…”

Section: Stochastic Process-gaussian Processmentioning

confidence: 99%

Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging

et al. 2021

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This work proposes a stochastic process-based inversion to estimate hydrocarbon resistivity based on multifrequency electromagnetic (EM) data. Currently, mesh-based algorithms are used for processing the EM responses which cause high time-consuming and unable to quantify uncertainty. Gaussian process (GP) is utilized as the alternative forward modeling approach to evaluate the EM profiles with uncertainty quantification. For the optimization, gradient descent is used to find the optimum by minimizing its loss function. The prior EM profiles are evaluated using finite element (FE) through computer simulation technology (CST) software. For validation purposes, mean squared deviation and its root between EM profiles evaluated by the GP and FE at the unobserved resistivities are computed. Time taken for the GP and CST to evaluate the EM profiles is compared, and absolute error between the estimate and its simulation input is also computed. All the resulting deviations were significantly small, and the GP took lesser time to evaluate the EM profiles compared to the software. The observational datasets also lied within the 95% confidence interval (CI) where the resistivity inputs were estimated by the proposed inversion. This indicates the stochastic process-based inversion can effectively estimate the hydrocarbon resistivity in the seabed logging.

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“…Short-Time Average over Long-Time Average (STA/LTA) [4] is a broadly used algorithm that allows the detection of an event through its energy, and it is typically applied to data that undergo bandpass filtering within a frequency range where the signal prevails over the background noise [5]. Nevertheless, for weaker events, the background noise could have a higher amplitude than that of the event itself [6,7]. Additionally, in environments with dynamic noise, no single filter will be universally optimal for a diverse range of signals when using energy detectors [8].…”

Section: Introductionmentioning

confidence: 99%

Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

Sosa,

Molina,

Spagnotto

et al. 2024

Electronics

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This study focused on seismic event detection in a volcano using machine learning by leveraging the advantages of software/hardware co-design for a system on a chip (SoC) based on field-programmable gate array (FPGA) devices. A case study was conducted on the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcanic seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA into the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for the development of an early alert system.

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A Novel Method of Seismic Signal Detection Using Waveform Features

Cited by 9 publications

References 32 publications

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging

Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

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