Machine learning-based microseismic catalog and passive seismic tomography evaluating the effect of grouting in Zhangji coal mine, China

Qian, Jia-Wei; Anyiam, Uzonna Okenna; Wang, Kang-Dong

doi:10.1007/s11770-023-1056-5

Appl. Geophys.

2023

DOI: 10.1007/s11770-023-1056-5

|View full text |Cite

Machine learning-based microseismic catalog and passive seismic tomography evaluating the effect of grouting in Zhangji coal mine, China

Jia-Wei Qian,

Uzonna Okenna Anyiam,

Kang-Dong Wang

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Estimation of quarry blast equivalent based on seismic array: Case study in Chuzhou, Anhui Province

Qian,

Zheng,

Ning

2024

Appl. Geophys.

View full text Add to dashboard Cite

Estimation of quarry blast equivalent based on seismic array: Case study in Chuzhou, Anhui Province

Qian,

Zheng,

Ning

2024

Appl. Geophys.

View full text Add to dashboard Cite

Dynamic energy evolution and fragmentation characteristics of damaged rock under impact compression loading

Zheng,

Qian,

et al. 2024

Appl. Geophys.

View full text Add to dashboard Cite

Development of a Deep Neural Network Model for the Relocation of Mining-Induced Seismic Event

Wang,

Shen

2024

Applied Sciences

View full text Add to dashboard Cite

The precise relocation of seismic events is critical for many engineering projects. Swarms of minor or micro earthquakes typically reveal stress concentration and spots of greater seismic hazards. Particularly in the context of deep underground mining, advanced techniques that can accurately relocate microseismicities are urgently in demand. Here, we developed a neural network-based modeling training method that can precisely relocate seismicities and invert for velocities at the same time, with preconfigured receiver network locations. Our model can be iteratively improved with field recorded data. We showed that, with roughly eight iterations, we can reasonably resolve for the earthquake locations for both clusters of events, namely spatially distributed with linear pattern or randomly scattered. Our initially trained model, which only focused on events that had a linear distribution pattern, was used as the base for the training of the subsequent models which could better resolve for randomly scattered event locations. Although we stopped at the eighth iteration, the process reported here can be continued, as the model will have a better performance with more iterations.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Machine learning-based microseismic catalog and passive seismic tomography evaluating the effect of grouting in Zhangji coal mine, China

Cited by 4 publications

References 31 publications

Estimation of quarry blast equivalent based on seismic array: Case study in Chuzhou, Anhui Province

Estimation of quarry blast equivalent based on seismic array: Case study in Chuzhou, Anhui Province

Dynamic energy evolution and fragmentation characteristics of damaged rock under impact compression loading

Development of a Deep Neural Network Model for the Relocation of Mining-Induced Seismic Event

Contact Info

Product

Resources

About