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Mining Project: Advanced Mining Seismicity Processing
Keywords: Geologic conditions and events Seismic monitoring
This page is archived for historical purposes and is no longer being maintained or updated.
Principal Investigator
Derrick Chambers -  Request informationStart Date
10/1/2016End Date
9/30/2017Objective
To refine and apply advanced event detection, location, and magnitude estimation methods that can be used to increase seismic catalog quality; and to quantify the strengths and weaknesses of each method and, based on the case studies conducted, identify features that are likely to contribute to the success or failure of each for monitoring mining-induced seismicity.Topic Area
Ground Monitoring
Research Summary

Many mines produce seismic events as the ground responds to the extraction process. For these mines, monitoring seismicity can provide useful insights into mine design performance and help to identify and manage potential ground control hazards. However, the equipment, processing expertise, and software needed to monitor and locate mining-induced seismicity (MIS) can be prohibitively expensive. If the cost of monitoring MIS could be reduced, many more mines could have access to information that, when taken in the appropriate mechanical and geological contexts, can have a significant impact on worker safety.
Although many different types of seismic data can be useful (such as phase identification and polarity, moment tensors, source-time functions, waveform characteristics, etc.) the most widely used and easiest to understand is an event catalog. A catalog, at a minimum, is a list of seismic events with their corresponding calculated origin times, locations, and magnitudes. In order to produce a catalog, instruments must record the seismic energy radiated from the events. A group of instruments designed to record such energy is referred to as a network. In the context of this project, seismic networks that can monitor mining seismicity are divided into three classes:
*Class 1*: Typically an in-mine system with sensor spacing between 10 m to 500 m. Such a system is expensive to install and maintain and only a few companies in the world offer the service. Because the sensors are densely spaced and usually close to the seismic sources, the quality of seismic catalog produced by a class 1 network is optimal.
*Class 2*: A surface array above or around the mine. Typical sensor spacing is between 0.5 km and 10 km. Installation and maintenance of a class 2 network is much cheaper than a class 1 network, but produces a lower-quality catalog.
*Class 3*: A regional network with typical station spacing between 10 km and 100 km. Such a network is usually operated and maintained by a government agency or university. In the United States, the data from class 3 networks are almost always available to the public, free of charge. A class 3 network produces the lowest-quality catalog, often only detecting relatively high-magnitude events with location errors of a few kilometers.This project focused on improving seismic catalog quality by applying and refining advanced data processing methods used in regional and global seismology that are not commonly used in mining seismicity processing. The application of such methodologies may allow mine operators to produce a catalog of a given quality with lower operational and instrumentation costs. Improving the quality of seismic data collected at mines will allow more mines to utilize the information in order to aid in assessing mine design performance and identifying potential geological hazards.
Determining if seismic catalog quality has been improved is difficult without independent knowledge of the seismicity. For this reason, mines monitored by two classes of networks were invited to participate in the pilot phase of this project. Advanced techniques were applied to the higher class network (which typically produces lower-quality catalogs) and the results were compared to those obtained with traditional processing methods on the lower class network (which typically produces higher-quality catalogs).
The key accomplishments of the pilot project:
Collection of several verification datasets as described above.
Application of detection methods detailed in the paper, "Application of subspace detection on a surface seismic network monitoring a deep silver mine."
Collaboration in developing open-source software for match filter and subspace detection found at https://github.com/eqcorrscan/EQcorrscan.Work on the key concepts of the project will continue as tasks under different projects, including creating and evaluating augmented regional seismic catalogs, but a full project will not be pursued.

See Also
Detecting Strata Fracturing and Roof Failures from a Borehole Based Microseismic System
Development of an Automated PC-Network-Based Seismic Monitoring System
Evaluation of Seismic Potential in a Longwall Mine with Massive Sandstone Roof Under Deep Overburden
Local Earthquake Tomography for Imaging Mining-Induced Changes Within the Overburden above a Longwall Mine
Mapping Hazards with Microseismic Technology to Anticipate Roof Falls - A Case Study
The Relationship of Roof Movement and Strata-Induced Microseismic Emissions to Roof Falls
Safer Mine Layouts for Underground Stone Mines Subjected to Excessive Levels of Horizontal Stress
Technology News 466 - Use of Seismic Tomography to Identify Geologic Hazards in Underground Mines
Three Dimensional Microseismic Monitoring of a Utah Longwall
Time-Lapse Tomography of a Longwall Panel: A Comparison of Location Schemes
Page last reviewed: March 30, 2017
Page last updated: March 30, 2017
Content source: National Institute for Occupational Safety and Health, Mining Program
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