RFloc3D: A Machine-Learning Method for 3-D Microseismic Source Location Using P- and S-Wave Arrivals

Chen, Yangkang; Savvaidis, Alexandros; Fomel, Sergey; Saad, Omar M.; Chen, Yunfeng

doi:10.1109/tgrs.2023.3236572

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…The action area of vibration stress wave generated by source explosion is wide with short duration, the shallow underground medium has complex constitutive characteristics, and the propagation path of wave-field ray is not clear. But the number of test nodes is limited and can only provide incomplete available data with low signal-to-noise ratio (SNR) [6]. Therefore, in order to improve the localization accuracy, one of the solutions is to design a distributed wireless sensor network (WSN) composed of a large number of sensor nodes (groups) deployed in the monitoring area.…”

Section: Introductionmentioning

confidence: 99%

Securing Fast and High-Precision Localization for Shallow Underground Explosive Source: A Curiosity-Driven Deep Reinforcement Learning Approach

Wu,

Wang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Shallow underground explosive source localization technology is a key technology in the field of underground space localization. The existing approaches mainly aim to improve the localization accuracy, but need to deploy enormous sensors in the monitoring area, and rely on a large number of back-end workstations to solve. These methods have the defects of considerable calculation and high time cost, and are hard to satisfy the precise and real-time requirements of onsite testing, ultimately resulting in slow localization speed and accurate localization failure. Fortunately, emerging deep reinforcement learning (DRL) can effectively solve the problem of slow search policy by modeling the source localication as a Markov decision process (MDP). Therefore, a Curiosity-Driven Deep Dueling Double Q-learning Network (C-D3QN) is subsequently proposed to solve the above MDP. The overestimation problem is solved by decoupling selection and evaluation of the bootstrap action, and the action difference is effectively increased by introducing the dueling network that separately represents state values and action advantages. Meanwhile, the exploration is jointly reinforced by an intrinsic reward outputted from the curiosity module and an extrinsic reward supplied by the environment, guaranteeing the convergence to global optimal. Finally, extensive simulation results based on the outfield experiment data show that compared with other algorithms, the proposed scheme can significantly improve exploration ability and learning speed as well as generalization and robustness. Additionally, compared to the baseline algorithm DQN, the C-D3QN algorithm can offer an improved localization accuracy as high as 99.62% and an increased localization speed of 66.23%.

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Section: Introductionmentioning

confidence: 99%

Securing Fast and High-Precision Localization for Shallow Underground Explosive Source: A Curiosity-Driven Deep Reinforcement Learning Approach

Wu,

Wang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Shallow underground explosive source localization technology is the key to realize shallow underground explosion, microseismic safety monitoring in shallow mining, and health monitoring of composite material structures [1,2], and is also often applied to earthquake disaster assessment and emergency rescue response. At present, the localization method based on vibration signals is the most promising method for detecting the source location.…”

Section: Introductionmentioning

confidence: 99%

An Energy Focusing-Based Scanning and Localization Method for Shallow Underground Explosive Sources

Wu,

Wang,

2023

Electronics

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To address the problem of slow speed and low accuracy for recognizing and locating the explosive source in complex shallow underground blind spaces, this paper proposes an energy-focusing-based scanning and localization method. First, the three-dimensional (3D) energy field formed by the source explosion is reconstructed using the energy-focusing properties of the steered response power (SRP) localization model, and the velocity field is calculated based on a multilayered stochastic medium model by considering the random statistical characteristics of the medium. Then, a power function factor is introduced to quantum particle swarm optimization (QPSO) to search for and solve the above energy field and to approach the real location of the energy focus point. Additionally, the initial population is constructed based on the logistic chaos model to realize global traversal. Finally, extensive simulation results based on the real-world dataset show that compared to the baseline algorithm, the focusing accuracy of the energy field of the proposed scheme is improved by 117.20%, the root mean square error (RMSE) is less than 0.0551 m, the triaxial relative error (RE) is within 0.2595%, and the average time cost is reduced by 98.40%. It has strong advantages in global search capability and fast convergence, as well as robustness and generalization.

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Identifying the source types of the seismic events using discriminant functions and tree-based machine learning algorithms at Soma Region, Turkey

2023

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RFloc3D: A Machine-Learning Method for 3-D Microseismic Source Location Using P- and S-Wave Arrivals

Cited by 6 publications

References 19 publications

Securing Fast and High-Precision Localization for Shallow Underground Explosive Source: A Curiosity-Driven Deep Reinforcement Learning Approach

Securing Fast and High-Precision Localization for Shallow Underground Explosive Source: A Curiosity-Driven Deep Reinforcement Learning Approach

An Energy Focusing-Based Scanning and Localization Method for Shallow Underground Explosive Sources

Identifying the source types of the seismic events using discriminant functions and tree-based machine learning algorithms at Soma Region, Turkey

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