Seismic Monitoring Strategies 

for Deep Longwall Coal Mines

RI 9700

REPORT OF INVESTIGATIONS/2016
Report of Investigations 9700

Seismic Monitoring Strategies for Deep Longwall Coal Mines

Peter Swanson, M. Shawn Boltz, and Derrick Chambers

DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Institute for Occupational Safety and Health

Office of Mine Safety and Health Research

Pittsburgh, PA • Spokane, WA

October 2016
This document is in the public domain and may be freely 

copied or reprinted.

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Suggested Citation

NIOSH [2016]. Seismic monitoring strategies for deep longwall coal mines. By Swanson P, 

Boltz MS, and Chambers D. Spokane WA: U.S. Department of Health and Human Services, 

Centers for Disease Control and Prevention, National Institute for Occupational Safety and 

Health, DHHS (NIOSH) Publication No. 2017-102, RI 9700. 

DHHS (NIOSH) Publication No. 2017–102

October 2016

Cover photo by NIOSH. 
Contents

Abstract...................................................................................................................................... 1

Introduction................................................................................................................................ 2

Background............................................................................................................................ 2

Evolution of Technology in Hardrock Mines............................................................................ 3

Bump Hazard in Coal Mines................................................................................................... 3

Seismic Monitoring Objectives ................................................................................................... 4

Need for Documentation ........................................................................................................ 5

Ground Vibration Measurements............................................................................................ 6

Rockburst Risk Management ................................................................................................. 7

Experience in Coal Mine Seismic Monitoring ............................................................................. 8

Seismic Monitoring in the United States ................................................................................. 8

Examples of International Seismic Monitoring ........................................................................ 9

Challenges of Adapting Monitoring Technology in Deep Coal Mines.........................................10

Rapid Excavation of Large Longwall Panels..........................................................................10

Accessibility ..........................................................................................................................12

Permissibility and Intrinsic Safety ..........................................................................................13

Wave Propagation Medium Effects .......................................................................................13

Seismic Source Characteristics from Waveform Measurements ...............................................14

Characteristics of Different Seismic Monitoring Systems...........................................................17

Five Seismic Monitoring Strategies for Deep Coal ....................................................................18

Single-station Option.............................................................................................................18

Regional Seismic Network Augmentation Option ..................................................................29

Temporary Network Deployment Option ...............................................................................33

District- and Mine-wide Networks Option...............................................................................37

Microseismic Monitoring Network Option ..............................................................................46

Discussion ................................................................................................................................53

Factors Guiding Strategy Selection .......................................................................................53

Pre-installation Estimates of Event Location Accuracy ..........................................................54

Effects of Heterogeneous Velocity Structure .........................................................................55

Hazard Assessment and Risk Management for Rockbursts ..................................................56

Implementation .....................................................................................................................57

Conclusion ................................................................................................................................58

Acknowledgments.....................................................................................................................59

References ...............................................................................................................................59
Appendix A: Wave Propagation Medium Effects .......................................................................67

Layer-specific Properties.......................................................................................................67

Waveform Character.............................................................................................................69

Velocity-model Development.................................................................................................70

Appendix B: Seismic Source Characteristics Derived from Waveform Measurements .............75

Waveform Measurements .....................................................................................................75

Appendix C: Single-Station Monitoring Implementation Options................................................79

Appendix D: Station Corrections and Multi-event Location Techniques.....................................81

Travel-time Station Corrections .............................................................................................81

Multi-event Location Techniques...........................................................................................82

Glossary of Seismic Terms .......................................................................................................85
Figures

Figure 1. Spatial domains of seismic monitoring ........................................................................ 5

Figure 2. Large longwall panel size and high excavation rate provide a challenge 

to maintaining high-density seismic monitoring network ............................................................12

Figure 3. Large angular coverage gaps in longwall coal mines due to inaccessible gob areas..13

Figure 4. Relationships between magnitude, seismic moment, corner frequency, and rupture 

dimension. Plotted data are from the studies listed below the graph .........................................16

Figure 5. Frequency range of six different types of seismic monitoring systems........................18

Figure 6. Helicorder-style plot of 12 hours duration from a 1-Hz short-period vertical 

seismometer .............................................................................................................................19

Figure 7. Unfiltered (A) and low-pass filtered (B) helicorder records near active longwall..........20

Figure 8. Multiseam extraction helicorder record showing large-magnitude seismic events ......20

Figure 9. Seismic body-wave particle motions...........................................................................21

Figure 10. Two sets of three-component seismograms with P and S arrivals............................22

Figure 11. Increasing S-P times with propagation distance .......................................................23

Figure 12. Observed S-P times exhibit discrete values (A, B, C)...............................................24

Figure 13. Comparison between range estimates based on S-P times and longwall locations..24

Figure 14. Seismograms from north- and east-oriented components (red and blue traces

in first two panels) used to construct a hodogram plot (third panel) illustrating P-wave 

polarization for back-azimuth measurements ............................................................................25

Figure 15. Map showing outline of a longwall coal mine with a single-station location based on 

range and back-azimuth measurements ...................................................................................27

Figure 16. Comparison of single-station location estimates from multiple stations ....................28

Figure 17. Multiple range estimates based on S-P times constrains possible event locations ...28

Figure 18. Seismic stations of the Nation's backbone network ..................................................30

Figure 19. Locations of active U.S. underground coal mines.....................................................31

Figure 20. Seismic stations currently operating in the eastern and central United States ..........32

Figure 21. Temporary exploration seismic array (magenta lines on left panel) captures

diverse seismic response at two mine sites (right panel)...........................................................35

Figure 22. Temporary array of surface geophones tracks seismic response of longwall

shearer motion..........................................................................................................................36

Figure 23. Seismic activity recorded during temporary deployment of broadband stations

(red triangles) above active longwall .........................................................................................37

Figure 24. Seismic stations of the North Fork Valley district network (triangle symbols)............38

Figure 25. Seismometer, remote power, and telemetry infrastructure of a typical district-wide 

network station..........................................................................................................................39

Figure 26. Software-automated event locations from district-wide network over 

60-day period............................................................................................................................41
Figure 27. Seismic response to progressive failure. (A) number of events per day with

M ≥ 1, (B) helicorder plot from day with four events with M ≥ 2, (C) cumulative number of

events during progressive failure, (D) map view of manually processed event locations ...........43

Figure 28. Map showing a five-panel longwall section traversed by several steeply dipping

faults (blue lines).......................................................................................................................44

Figure 29. Cumulative number of seismic events with M ≥ 1.0 during a 5-panel extraction 

sequence, showing a significant increase in activity during mining of the fifth panel.................45

Figure 30. Seismic event locations during mining of fifth panel in 5-panel sequence.................45

Figure 31. Surface and underground geophone stations of microseismic array covering one

or more adjacent longwall panels..............................................................................................46

Figure 32. Surface station of microseismic network, showing: (a) triaxial surface geophone

prior to burial and (b) station infrastructure platform ..................................................................47

Figure 33. Seven days of seismic activity (red circles) located by the microseismic network 

during 42 m (137 ft) of longwall retreat (yellow rectangle). Completed longwall panels are 

shaded gray and unmined panels are white ..............................................................................49

Figure 34. Large magnitude events during 42 m (137 ft) of longwall retreat (0 ≤ M < 1.5, blue; 

1.5 ≤ M ≤ 2.7, yellow)................................................................................................................50

Figure 35. Microseismic activity observed during 30-day period of no mining ...........................50

Figure 36. Number of seismic events detected during each hour of the day during a 30-day 

period with longwall production on both day and swing shifts. Peak activity occurs between 9 

and 10 p.m. (hours 21 and 22)..................................................................................................51

Figure 37. Seismic activity per day detected by microseismic-scale (panel-scale) and

district-wide networks and daily longwall retreat distances........................................................52

Figure A1. Snell's law governing seismic wave refraction in layered media...............................67

Figure A2. Reflection at layer boundaries controlled by acoustic impedance ............................68

Figure A3. Conversion of incident P-wave into reflected P- and S-waves in the upper layer

and refracted P- and S-waves in the lower layer .......................................................................68

Figure A4. Effect of propagation path on waveform character as observed at ten seismic 

stations at increasing distance ..................................................................................................69

Figure A5. Two sets of three-component seismograms (vertical Z, north N, and east E)

recorded on a common station comparing signals generated from a mining-induced

event (top) and a natural earthquake (bottom) ..........................................................................70

Figure A6. Sonic log in strata above the coal seam with model velocity values and layer 

thicknesses assigned to individual layers (red horizontal bars) .................................................71

Figure A7. Comparison of velocity structures in two nearby boreholes......................................72

Figure A8. Variation of P-wave velocity with overburden...........................................................73

Figure A9. Shift in calculated event locations when seismic velocity model fails to match

actual velocity. Frame A shows raypath lengths calculated for an unmined coal panel with 

known seismic velocity. Frame B shows the effect of a modest 10% reduction in seismic 

velocity. Frame C shows the resultant change of the calculated event location where

travel-time observations from both sensors can be satisfied simultaneously .............................74

Figure B1. Theoretical displacement amplitude spectrum (graph on left) for shear slip on

circular crack (illustration on right).............................................................................................75
Figure D1. Velocity model errors partially compensated by travel-time station correction

factors. Raypaths from mine B are affected by topographic features unaccounted for in the 

velocity model ...........................................................................................................................81

Figure D2. Raypaths between stations (red triangles) and event clusters (black dots) affect 

suitability of double-difference location scheme. (a) suitable array configuration, (b) unsuitable 

array configuration ....................................................................................................................83
Tables

Table 1. Back azimuths and predicted back azimuths from particle motion analysis for

temporary deployment.............................................................................................. 26

Table 2. Distances between underground coal mines and existing seismic stations...............33

Table 3. Seismic monitoring objectives matched to monitoring strategies............................ 53

Table 4. Minimum sensor densities for different scale networks......................................... 54

Table 5. Velocity model requirements for specific monitoring objectives..............................55

Table 6. Levels of complexity, staff time, and expense for different seismic monitoring

strategies...............................................................................................................58

Table B1. Relations between seismic magnitude, moment, radiated energy, rupture

dimension, and potential energy equivalent...................................................................78
Acronyms and Abbreviations

ANSS

CDC

CISN

CMI

DOE

EI

GPS

IP

M

MSHA

NAP

NIOSH

NRC

NSF

PC

PC-SUDS

PSN

P-wave

SHA

S-wave

S-P

USBM

USGS

UUSS

Advanced National Seismic System

Centers for Disease Control and Prevention

California Integrated Seismic Network

Central Mining Institute

Department of Energy

energy index

global positioning system

Internet protocol

Magnitude

Mine Safety and Health Administration

National Academies Press

National Institute for Occupational Safety and Health

National Research Council

National Science Foundation

personal computer

Personal Computer Seismic Unified Data Structures

public seismic network

compressional wave

short-term hazard assessment

shear wave

S-wave minus P-wave

United States Bureau of Mines

United States Geological Survey

University of Utah Seismic Stations
Unit of Measure Abbreviations

cm

deg

ft

GJ

Hz

J

kg

kg/m3

kHz

kJ

km

km2

km/s

m

m2

m3

m/s

mi

mi2

min

MHz

MJ

μm

mm

MPa

N

Nm

%

s

TJ

centimeter

degree

foot

gigajoule

hertz

joule

kilogram

kilogram/cubic meter

kilohertz

kilojoule

kilometer

square kilometer

kilometers/second

meter

square meter

cubic meter

meters/second

mile

square mile

minute

megahertz

megajoule

micrometer

millimeter

megapascal

newton

newton-meter

percent

second

terajoule
Seismic Monitoring Strategies for Deep Longwall Coal Mines

Peter Swanson1

, M. Shawn Boltz2, and Derrick Chambers3

Abstract

This report presents five strategies for detecting and recording seismic activity associated 

with deep longwall coal mining: 

1. Single-station monitoring

2. Augmentation of an existing regional seismic network 

3. Temporary network deployment 

4. Implementation of a mine-wide or district-wide network

5. Installation of a dense microseismic network

These strategies take into consideration: (1) the variety of potential objectives for seismic 

monitoring, (2) the notable contrast of coal mining environments with conditions in hardrock 

mines, where these techniques were originally developed, (3) the enormous range in magnitude 

and physical size of seismic events, and (4) the significant variation in expense and complexity 

of the different approaches. The strategies range from simple and inexpensive methods that can 

be implemented on the surface by a mining engineer to complex and expensive endeavors 

requiring involvement by underground mine personnel and monitoring-system suppliers. This 

report provides example monitoring results from deep longwall coal mines, and describes the 

capabilities and limitations of each of the five suggested approaches.

 

1 Geophysicist, Mine Stability Ground Control, NIOSH Spokane Mining Research Division (SMRD), 

Spokane, WA.

2 Mining Engineer, Mine Stability Ground Control, NIOSH SMRD, Spokane, WA.

3 Mining Engineer, Mine Stability Ground Control, NIOSH SMRD, Spokane, WA.
2

Introduction

Background

Following the Crandall Canyon Mine (Utah) collapse in 2007 with nine fatalities [Gates et al. 

2008], questions were raised about the potential benefits of applying seismic/microseismic 

monitoring technology to improve mine safety in deep underground coal mines, where deep is 

defined as several hundred meters or more of overburden. The underground mining industry’s 

widespread use of seismic monitoring in deep rockburst-prone hardrock mines [Durrheim et al. 

2007] indicates that it has been beneficial in these applications, suggesting that it may also prove 

useful in addressing the problem of coal bumps (also known as coal bursts or rockbursts) and 

other large-scale ground instabilities such as mine collapse. A total of 13 workers in U.S. coal 

mines were killed by such ground failures in the ten-year period 2006–2015.

Prompted by Crandall Canyon and other earlier coal mine accidents, Congress directed the 

National Institute for Occupational Safety and Health (NIOSH) to conduct studies related to the 

safety implications of retreat room-and-pillar mining practices in underground coal mines at 

depths greater than 1,500 feet (450 m). In its report to Congress [NIOSH 2010], NIOSH 

recommended “further seismic monitoring research be conducted at demonstration sites where 

the technology and interpretation techniques can be tested and refined to advance the state of 

practice of seismic monitoring for hazard assessment in deep underground coal mines.” The 

work presented in this report represents part of that effort.

As part of a NIOSH contract, the University of Utah convened an international panel of mine 

seismicity experts in 2008 to evaluate the potential benefits of seismic monitoring in U.S. coal 

mines. The purpose of the panel was to assess the current state of seismic monitoring in U.S. coal 

mines and to evaluate the costs and practical benefits of seismic monitoring at various magnitude 

scales. The panel reported on ways that routine mine seismic monitoring is being used 

internationally, particularly in hardrock mines in South Africa, Canada, and Australia, but also in 

coal mines in Germany, Poland, and China [Arabasz and McCarter 2009]. The panel concluded 

that seismic monitoring is one of the few techniques able to provide a three-dimensional 

assessment of strata conditions surrounding mining excavations. To be useful, the panel 

suggested, seismic monitoring needs to be integrated with geotechnical observations and 

modeling. The capabilities of a seismic system must enable the system to locate seismic events to 

within at least a few tens of meters, allowing the system to associate the seismic events with 

identifiable geological or mining features. A seismic system should also have the sensitivity to 

detect small events, as these can reveal the seismic character of the ground reaction to excavation 

before the mine encounters more serious problems.

Deploying and operating a seismic network capable of locating events to the accuracy

recommended by the panel, however, requires a significant investment of resources and labor. 

Also, seismic monitoring in coal mines is more difficult than monitoring in hardrock mines 

because of the larger scale of mines, much faster rate of mining, and highly variable properties of 

sedimentary layering. For these reasons, seismic systems that meet the panel’s requirements are 

rarely implemented in coal mines. For example, in the decade before the international panel 

convened, there was only one instance of a temporary in-mine system with the resolution to meet 

these objectives in an underground U.S. coal mine [Ellenberger et al. 2001]. While we 

acknowledge the need for high accuracy of event locations in order to integrate seismic 
3

monitoring into geotechnical modeling, we also present methods that allow less accurate, more 

cost-effective monitoring systems to satisfy other objectives.

Evolution of Technology in Hardrock Mines

The evaluation conducted by NIOSH’s international mine seismicity expert panel leaned 

heavily on experience from the application of seismic monitoring to deep hardrock mines, where 

monitoring has evolved to a much higher level of operational practice than in other types of

mines. South African mines have led this effort with a 120-year historical progression of the 

development and application of seismic monitoring in ground control practices in the 

Witwatersrand Basin gold mines [Riemer and Durrheim 2011]. A brief history of seismic 

monitoring in South African mines is provided here to underscore the vast difference in the 

application of seismic monitoring in U.S. coal mines compared to that in deep hardrock mines.

Seismic events associated with mining in South Africa were recognized as far back as 1908 

from recordings made on a single, standalone seismometer. Complete synchronized seismic 

networks, deployed on the surface but specifically focused on rockburst areas of mining, were 

first described in 1939, followed by underground networks in the 1960s, and real-time digital 

systems starting in the 1970s. In the 1970s, mining companies began using seismic systems, 

which were primarily developed for research, as management tools to assist in rescue operations 

and safety procedures following rockbursts. With the anticipation that rockbursts may possibly 

be predicted, real-time monitoring systems were introduced on a mine-wide basis. By the early 

1990s, digital monitoring was considered the standard; mines started hiring their own 

seismologists, and monitoring networks ultimately became fully owned by the mining companies 

[Durrheim 2010]. There are now a total of 25 systems in the Witwatersrand gold mines that 

encompass about 430 underground sensor sites, and they are estimated to locate approximately 

125,000 events each month. A similar widespread deployment of commercial systems is in place 

in deep hardrock mines in Canada and Australia, along with deployments in the rockburst-prone 

hardrock mines in the United States, where the first mine-owned digital system supplied by a 

commercial vendor was installed in 2002 [Dehn and Knoll 2013]. By the year 2000, research and 

monitoring efforts moved away from prediction and more toward hazard assessment and risk 

management. Recognizing that high-quality data are needed for these applications and that 

maintenance of a mine-wide seismic network poses significant challenges, many mining 

companies are now choosing to outsource some or all of their monitoring operations [Durrheim 

et al. 2007].

Bump Hazard in Coal Mines

One important question associated with seismic monitoring in coal mines is whether the 

approach taken in applying seismic monitoring to burst-prone hardrock mines is the same 

approach that should be taken for bump-prone deep coal mines. Only a few mining regions of the 

United States have the necessary conditions for coal bursting, and of those mines experiencing 

bumps, there are often long periods of mining where bumping is not a problem. A useful strategy

for mitigating bumps is to minimize exposure, where possible, by avoiding areas where the 

conditions for coal bumping are prevalent. For example, high stresses produced by thick 

overburden cover are one of the prime ingredients for bumps [Iannacchione and Zelanko 1995], 

and, in many coal mines, exposure to such areas can be avoided or minimized. In deep hardrock
4

mining there is not as much flexibility to mine in alternate areas; the deep cover and strong host 

rocks are typically omnipresent.

Because the coal bump hazard is not pervasive, there has not been an industry-wide 

imperative to engage in an aggressive campaign to develop and apply technology to manage coal 

bumps as has been the case in deep metal mining districts. The potential benefits of applying

seismic monitoring to ground control issues in coal will only come about through wider 

demonstration trials and research showing its value in mitigating hazards; this is the path taken in 

the hardrock mines (e.g. [Riemer and Durrheim 2011]). Additionally, seismic monitoring is just 

one tool of many that can be used in addressing geostructural challenges.

As the bump hazard is not uniformly distributed throughout a coal mine property, and the 

value of the monitoring in terms of mitigating hazards has not been amply demonstrated (in 

coal), it is impractical to deploy a dense microseismic network, of the kind used in hardrock, to 

cover the entire mine. With the variability in the coal bump hazard, and the diversity of possible 

objectives for seismic monitoring, including those that go beyond the narrow coal bursting issue, 

it is recognized that no single monitoring system is suitable for satisfying all requirements. In 

this report, five strategic approaches to seismic monitoring are presented which, with a variety of 

implementation options, can be tailored to specific objectives. These five strategies, which 

encompass a wide range of cost and complexity, provide flexibility in using seismic monitoring 

to better understand and address ground control problems in coal mines.

First, potential objectives for seismic monitoring in coal mines are described. Then, a review 

of coal mine seismic monitoring applications, used for research in the United States and for

industry use in international mines, is presented. Challenges to adapting seismic-monitoring 

technology from hardrock to deep longwall coal are then described, followed by a brief review of 

quantitative measures of seismic source parameters, which place constraints on monitoring 

systems’ hardware specifications. Descriptions and applications of the five strategies are then 

presented. This report aims to identify flexible options to achieve a monitoring objective while 

minimizing cost and complexity.

Seismic Monitoring Objectives

Seismic monitoring allows for the detection, location, and estimation of the strength, or size, 

of dynamic ground movements from remote measurements. Potential objectives for seismic 

monitoring are varied and can entail measurement of “seismic events” with magnitudes (M) of 

zero (0) and above, and/or “microseismic events” with M < 0 (later in this report, the concept of 

magnitude and the various magnitude scales are discussed in more detail). Unless otherwise 

noted, the term “seismic” is used to describe both seismic and microseismic4 events and 

monitoring systems. One of the most basic objectives of monitoring is to provide an awareness 

of the dynamic rock mass response to mining. A certain amount of seismic activity is expected 

 

4 The term “microseismic” is used throughout the document to refer to small-scale seismic events. It is not

to be confused with the term “microseism” which, in conventional earthquake seismology literature, refers 

to small amplitude ambient seismic noise generated largely by wave action in the oceans.
5

with some types of mining, such as longwalling under strata with at least modest competence. In

such mining conditions, where seismicity is extremely common and rarely problematic, seismic 

monitoring may not provide any safety benefit. Pillar and strata deformation processes associated 

with subsidence produce seismic activity and its presence can, in part, be taken as a sign that the 

mine design is working as intended. Thus, the presence of seismic activity is not evidence of the 

need to develop a monitoring program; seismic activity occurs at all mining operations to some 

extent. If unusual conditions are experienced or anticipated, however, seismic monitoring can be 

used to obtain useful information on rock-mass response.

An overview of the different spatial domains for the monitoring objectives discussed in this 

report is given in Figure 1. Areas of concern vary from regions outside of a mining district down 

to specific areas in an individual mine. Potential objectives range from providing simple 

awareness to more quantitative rockburst hazard assessments.

Figure 1. Spatial domains of seismic monitoring.

Need for Documentation

External and Mining-district Activity Sources

Responsibility for documenting seismic activity on the national scale falls to the U.S. 

Geological Survey (USGS). The earthquake location resolution across much of the United States, 

outside of areas of major earthquake hazard, is the smallest locatable event of magnitude 3 and 

location uncertainties of 10 km (6.2 mi) horizontally and 10 km (6.2 mi) in depth [ANSS 2014]. 

As a result of this large uncertainty, an event’s possible location could include multiple mining 

operations as well as sources of tectonic earthquakes [Swanson et al. 2008]. As discussed in the 

Regional Seismic Network Augmentation Option section of this report, most coal mine 

monitoring objectives cannot be achieved with the Advanced National Seismic System (ANSS)

network.

A basic objective for documentation purposes would be to discriminate between seismic 

activities induced by mining activities at different closely spaced mines. Another is to

characterize nonmining related seismic activity. The presence of unusual levels of in situ stress, 
6 

derived from tectonic or gravitational forces, can have a major impact on ground control [Mark 

and Gadde 2008], and a determination of the background level of local seismicity may aid in 

assessing regional stresses for mine design evaluations. 

Activity along major geologic structures is of special concern as the largest seismic events 

associated with mining are reported to be triggered along fault systems [Gibowicz and Kijko 

1994]. A distinction is made between seismic events that are induced and triggered [e.g., McGarr 

et al. 2002]. “Induced” events result from human activities that contribute most of the local stress 

involved in a rock mass failure. Induced events typically occur in the immediate vicinity of the 

active workings and show a strong relationship to the mining cycle. “Triggered” events result 

from much smaller incremental changes in the stress state on natural earth structures (i.e. preexisting faults) that were already close to failure. Triggered events are reported to be less 

dependent on the daily mining cycles and occur after large expanses of ground have been 

excavated. The extraction causes small changes in stress to pre-existing faults, at some distance 

from the current workings, which triggers dynamic slip. Documentation of activity near faults is 

an important objective prior to, and during, mining in the vicinity of such structures. 

Another source of seismic activity that is becoming more commonplace in a number of coal 

mining states is that introduced by industrial/civil projects, such as deep borehole liquid waste 

injections [NRC 2012]. Such projects have created a tremendous interest in, and scrutiny of, 

seismic activity attending their operation. As a result, it may be important for some mines to 

distinguish between seismic events generated by oil and gas hydraulic fracturing operations and 

those induced by mining.

For all of these reasons, it may be desirable to provide documentation of seismic activity in 

the vicinity of mining operations with higher spatial resolution and magnitude sensitivity than 

that available through the USGS. 

Internal Activity Sources

Another possible monitoring objective is to document seismic activity within a mine. Mines 

are designed with standup times (the length of time before ground failure is expected to begin) 

that are considerably shorter than most civil structures [e.g., Brady and Brown 1985]; 

degradation of old mine workings may gradually occur while mining takes place in virgin 

ground. Mature longwall coal mines result in large areas of old workings that slowly succumb to 

the effect of gravity, often producing seismic events long after mining has progressed to other 

areas. Seismic monitoring can be used to identify events in older areas and to detect and track 

any unusual degradation in active workings or other critical areas of the mine. 

Ground Vibration Measurements

Measurement of ground vibration levels is another possible objective for mines generating 

large magnitude seismic events. There may be critical mine structures such as portals, steep 

slopes, waste impoundment ponds, etc., or resources in the community that could be negatively 

affected by excessive levels of ground vibration associated with mining-induced seismic activity. 

Recordings of blast vibration levels, long recognized as essential in the surface mining and 

construction communities [e.g., Siskind 1983], provide quantitative measurements to address 

these issues.
7

Rockburst Risk Management

Monitoring objectives directly related to reducing the impact of rockbursts have been 

established through decades of seismic monitoring in hardrock mines [e. g., Mendecki et al. 

1999; Durrheim et al. 2007], and could be extended to coal mining. These objectives are 

described in the following subsections:

Rescue Response

A real-time online monitoring system alerts mine management of the occurrence of 

potentially damaging events. Locating these events within the mine enables rapid response by 

rescue personnel and/or mine staff seeking to quickly evaluate safety of mine personnel and the 

degree of damage to mine infrastructure with attendant hazards. Subsequent monitoring allows 

real-time stability evaluation in areas of concern and indicates when activity has returned to 

normal levels.

Post-event Analysis of Large and/or Damaging Seismic Events

Careful analyses of the mechanics behind and conditions leading up to large and/or damaging 

seismic events can help identify causative factors so that mitigation and preventative measures 

can be developed and implemented.

Evaluation and Verification of Mine Design Performance

Mine designs are based upon an expectation of how the rock mass should behave during the 

mining process. Seismic event data provide first-order information on the seismic rock mass 

response. Comparison of observations with expectations offers the opportunity to verify that the 

response is consistent with the design and to implement design changes in response to 

unexpected behavior. The effectiveness of adjustments in the design, or mitigation measures, can 

be evaluated in a similar manner.

Hazard Assessment (following [Durrheim et al. 2007])

Seismic monitoring data is used for hazard assessment over various time scales, including:

• Long-term (months to years). Seismicity data collected over long periods of time 

allows hazardous mine and geologic structures to be recognized and mitigated through 

long-term mine planning efforts, such as minimizing exposure to the problematic

structures.

• Medium-term (weeks to months). Monthly seismic hazard assessments provide input to 

mine planning procedures and are based on quantitative seismicity measures such as 

spatial and temporal event distributions, seismic moments and energies, and derived 

parameters related to dynamic stresses and strains. These parameters are monitored to 

detect changes in seismic hazard associated with mining geometry, rate of mining, and 

geologic structures.

• Short-term (days to weeks). Analysis of various seismic parameters in relation to 

mining activity, such as face advance, can help to relate short-term changes in the 

parameters to emerging hazards.

A quantitative description of seismic events and seismicity is considered necessary, but not 

sufficient, to achieving the above rockburst hazard assessment and risk management objectives 

[Mendecki et al. 1999].
8

Experience in Coal Mine Seismic Monitoring

Seismic Monitoring in the United States

Early research applications of microseismic monitoring in U.S. underground coal mines 

focused on coal bumps and roof falls [Leighton and Steblay 1977]. In the late 1980s and early 

1990s, the U.S. Bureau of Mines (USBM) was engaged in studies directed at coal mine bump 

prediction using small-scale arrays that followed the longwall face [e.g. Rowell 1989; Rowell 

and Lemons 1991; Wilson and Lemons 1992]. Attempts to directly apply hardware-automated 

microseismic techniques, which were developed for hardrock environments, were not entirely 

successful due to much lower signal frequencies and signal-to-noise ratios found in coal mines

[Rowell 1989]. These differences were attributed to wave propagation effects, including greater 

attenuation and much larger propagation distances for events that were, on average, larger in 

magnitude than in the hardrock mines. Subsequent automation of the processing relied on 

picking P-wave arrivals, or the first arriving seismic energy, in software.

In face-centered networks, such as those in the early USBM work, geophones were placed in 

the gateroads on each side of a longwall face, and analog signals were transmitted via long 

cables to a central data acquisition and processing site in the main entries. The USBM was 

granted an experimental permit by the Mine Safety and Health Administration (MSHA) that 

allowed these sensors and preamplifiers to be operated in potentially explosive coal mine 

environments. Sensors were moved with the face as it retreated. This resulted in a twodimensional coverage area in the seam ahead of the face with little to no constraint outside this 

immediate area. A homogeneous isotropic velocity model was used to calculate seismic event 

source locations in the plane of the seam. Throughout most of this work, a full-time geophysical 

engineer was stationed by the USBM at the mine sites to install, maintain, and operate the 

networks. Closure of the USBM in 1996 resulted in the loss of the staff and equipment that 

provided this capability for both coal and hardrock environments.

In the late 1990s, NIOSH development work focused on using the PC as a data acquisition 

and processing platform, making use of public domain PC-SUDS-based modular software 

[Swanson 2001]. A PC-networked adaptation of this method was developed and applied in a 

study of the mechanical deformation behavior of the overburden above a deep bump-prone 

longwall coal mine [Ellenberger et al. 2001]. Independent surface and underground networks 

using 4.5-Hz geophones were synchronized and, in both networks, long cables were used to 

transmit analog signals back to analog-to-digital converters.

NIOSH developed a simple, layered velocity model to locate events in the initial stages of 

mining prior to extensive gob and cave development. By the time a large fraction of the first 

panel had been developed, a shift in calculated event locations with respect to the panel outline 

was observed, indicating systematic location errors [Ellenberger et al. 2001]. Attempts to deal 

with the deviation of the velocity structure from the idealized model involved the 

implementation of “station corrections,” which added or subtracted a fixed travel-time delay or 

advance to arrival times at each station.

In a joint project between NIOSH and several deep western underground coal mines 

[Swanson et al. 2008], a surface seismic network was implemented to provide district-wide

coverage and is covered later in the District- and Mine-wide Networks Option section in this 

document.
9 

The University of Utah Seismograph Stations (UUSS) has studied seismicity associated with 

the underground coal mines of Utah because of their proximity to the UUSS regional seismic 

network. In operation since 1962, the 200+ station network houses 22 sites in the vicinity of the 

coal mining districts and provides the densest regional seismic coverage of any coal mining area 

in the United States. Such coverage allows smaller-magnitude events to be detected and then

located more often than in most of the United States, other than in the major earthquake hazard 

areas. Arabasz et al. [1997] conducted an analysis of the UUSS catalog and determined that all 

events greater than or equal to magnitude 1.8 have been detected in the Utah coal mining 

districts since 1978. This threshold is considerably less than the magnitude 3 target established 

for minimum detection level across the U.S. [ANSS 2014].

The UUSS conducted detailed examinations of event locations, magnitude statistics, source

mechanisms, and acceleration levels at coal mines in Utah, using data collected from temporary 

local surface networks. These included surface deployments surrounding multiple mines in the 

Eastern Wasatch Plateau in 1984 [Williams and Arabasz 1989; Wong et al. 1989], a 12-station 

network distributed above the Trail Mountain Mine (and one underground station) within a 6-km 

radius [Arabasz et al. 2005; Fletcher and McGarr 2005; Boltz et al. 2014], and an augmentation 

of the UUSS regional network with temporary stations deployed in the vicinity of the Crandall 

Canyon Mine [Pechmann et al. 2008; Kubacki et al. 2014]. 

There has also been limited microseismic monitoring conducted by the U.S. mining industry. 

An eastern U.S. coal mine has recently reported the operation of a 5–6 station surface network 

above deep cover longwall panels [Su et al. 2014]. Seismic data were used to provide feedback 

on mine design changes following the occurrence of several large seismic events. The stations 

covered 5 to 8 panels at a time and were moved with the panels as they were excavated. Mine 

design changes, including changing the panel width from 305 to 210 m (1,000 to 700 ft), were

reported to have resulted in the reduction of one order of seismic magnitude for the largest events 

experienced in the narrower panels.

Examples of International Seismic Monitoring

Seismicity is currently being monitored at underground coal mines in Germany by 60 seismic 

stations [Fritschen 2015]. The stations are grouped into local networks surrounding two active 

underground coal mines, two abandoned coal mines, and one recently closed mine currently 

undergoing flooding. Sensors are three-component 4.5-Hz geophones sampled at 500 samples 

per second. All but one are installed on the surface. German coal mines are deep (~700 to 1,500 

m, 2,300 to 4,900 ft), and longwall faces are preconditioned to reduce burst potential [Baltz and 

Hucke 2008]. While seismic monitoring was first developed in response to coal bursting activity, 

in more recent times emphasis is on large seismic events that produce vibrations which impact 

the dense population centers in the vicinity of the mines. Hardware, software, system operation, 

and data analysis are handled by a private company [Fritschen 2015]. 

Over the last 30 years, one of the German coal mining districts, the Ruhrgebeit, has been 

routinely monitored with a regional seismic network operated by Ruhr-University Bochum. 

Recent studies have made use of its short-period and broadband stations to investigate magnitude 

statistics (b-values), relationships to mining, and source mechanisms of longwall coal mining 

events, and the 4.5-Hz geophone station network has been used to establish empirical ground 
10

motion relations [Bischoff et al. 2010a]. A temporary one-year deployment of 15 surface sensors 

above one of the Ruhr coal mines [Bischoff et al. 2010b] provided a data set used by Sen et al. 

[2013] to calculate full moment tensors using a software-automated process.

There are approximately 20 underground coal mines in Poland that monitor seismicity using 

in-mine systems developed at the Central Mining Institute (CMI) [Lurka 2015]. Mines are 

responsible for reporting events with energy of 105 joules and higher. As larger events often clip 

signals from the underground sensors, CMI operates the Upper Silesian Coal Basin regional 

seismic network, consisting of both conventional 1-Hz short period and broadband triaxial 

sensors, to provide independent confirmation of event energy. Of the ~20 underground sensors at 

a mine, two are placed on each gateroad ahead of the face, and are moved when the face is 

within 40 m (130 ft) distance, maintaining an average distance between the face and the first 

sensor of 150 m (500 ft). In recent years, CMI has moved from using 4.5-Hz geophones to 

accelerometers and now uses a current-modulation technique to transmit signals via long cables 

to the data acquisition computer providing built-in limits on current draw. Small onsite staffs 

maintain systems, analyze data, and develop hazard forecasts.

Microseismic monitoring systems in Australian coal mines have been deployed in a number 

of temporary research networks to delineate fracturing processes associated with longwall caving 

[e.g. Hatherly et al. 1997; Luo and Hatherly 1998]. Many of these short-term experiments 

utilized exploration-style triaxial geophone sensors grouted into surface-drilled boreholes in a 

tight array that straddled the face in one part of a longwall panel. Microseismic monitoring was 

also applied to a windblast problem produced by a thick resistant-to-cave conglomerate layer in 

the immediate roof above longwall panels [Hayes 2000]. Geophones were grouted into the ends 

of 10-m-long boreholes drilled into the conglomerate. The combination of hydraulic fracture 

preconditioning of the roof and warnings provided through real-time analysis of the 

microseismic data helped minimize the risk [Hayes 2000].

In a recent survey of 62 of the 142 rockburst-prone coal mines in China, approximately 80% 

of them use microseismic monitoring as one of the tools to “monitor and forewarn” the 

occurrence of rockbursts [Qi et al. 2015].

Challenges of Adapting Monitoring Technology in Deep Coal Mines

Due to inherent differences in the nature of hardrock and coal mining, a direct transfer of 

microseismic monitoring technology from hardrock to coal is met with some technical 

challenges. Issues that coal mines face in adopting these systems include: (1) rapid excavation of 

large longwall panels, (2) limited and restricted access for sensor placement, (3) hardware 

permissibility, and (4) presence of more complicated wave propagation effects. 

Rapid Excavation of Large Longwall Panels

Individual longwall coal panels in the United States are larger than the footprint of many 

hardrock mines in which seismic monitoring takes place. Hudyma and Brummer [2007] 

tabulated basic seismic monitoring network characteristics from 24 different hardrock mines. 

Although it is difficult to make a straightforward comparison between a planar coal mine and a 

more three-dimensional hardrock ore body, the report shows that the cross-sectional footprint of 
11

the area that was monitored in the surveyed hardrock mines was significantly smaller than the 

typical longwall coal mine. In fact, the areas are smaller than individual longwall panels. The 

median footprint of the monitored area of all 24 hardrock mines in this study was 600 by 400 m 

(2,000 by 1,300 ft) or 24 hectares (59 acres).

A single typical longwall panel (~2,300 by 300 m; ~7,500 by 1,000 ft), excluding gateroads, 

measures ~70 hectares (~172 acres). If one includes gateroad pillars, where large pillars (e.g., 58-

m or 190-ft) are in use in deep U.S. coal mines, the area of a single longwall panel is 4–5 times 

the footprint of the average monitored area in the hardrock mines5

. Furthermore, multiple 

adjacent longwall panels make up a section or district, and longwall coal mines consist of 

multiple sections. When the large size of the monitored area is combined with a rapid pace of 

excavation, there are significant challenges to installing and maintaining a geophone array with 

sufficient density to provide high-resolution details of fracture and deformation processes.

In a hardrock mine, a sensor array can be established and left in place while mining takes 

place within the array bounds for several years, if not for decades, with only minor periodic 

adjustments in the array geometry. At longwall coal mines, in contrast, a face that is 335 m 

(1,100 ft) wide may sweep through a section of panel that is 500 m (1,600 ft) long in just one 

month (> 16 hectares; > 40 acres) (shown in Figure 2). In less than six weeks, the typical 

longwall coal panel will have completely mined out the median footprint of the hardrock mines 

studied by Hudyma and Brummer. The effort required to continually move the array in a coal 

operation to maintain similar high-sensitivity event detections and high-resolution locations as 

mining progresses is significant. Nevertheless, while difficult to achieve, certain monitoring 

objectives may require continuous monitoring with an underground microseismic network.

 

5 South African hardrock mines targeting deep tabular deposits are much larger than the typical mines 

surveyed in Hudyma and Brummer [2007], but rates of excavation are still at least an order of magnitude 

slower than U.S. longwall coal mines. Multiple monitoring systems are also implemented within individual 

mines [Durrheim 2010] to maintain a smaller fixed footprint.
12

Figure 2. Large longwall panel size and high excavation rate provide a 

challenge to maintaining high-density seismic monitoring network.

Accessibility

One unique challenge of seismic monitoring in longwall coal mines is the difficulty in the 

placement and maintenance of sensors near the active face. In hardrock mines, sensors can be 

placed as near to the working face as protection from blast flyrock allows, or sensors can be 

placed on a different level above or below the working face. In longwall coal mines, there are 

typically no accessible levels above or below the active seam. The working face has a length of 

~250–365 m (800–1,200 ft) and all of the available rock/coal surfaces make contact with moving 

parts of the longwall machinery, leaving no surface undisturbed for mounting sensors. Instead, 

sensors must be placed some distance away in the gateroads, along mains, or on the surface above 

the mine. Sensors along the tailgate do not survive long once the longwall face passes and the 

resultant caving produces gob, leaving a very large gap angle with no sensors at all (Figure 3). 

Such a large gap in coverage provides little constraint on event location solutions in the direction 

of the gap, resulting in systematic amplification of errors and biases in the calculated locations. 

Boreholes from the surface provide an option for placing sensors near the working face, and even 

for surrounding the seismic sources. However, this is an expensive and difficult option for deep 

coal mines and is only beneficial while the face is in the vicinity of the boreholes.
13

Figure 3. Large angular coverage gaps in longwall coal mines due to 

inaccessible gob areas.

Permissibility and Intrinsic Safety

A general strategy for measuring seismic deformation response is to place sensors as close as 

possible to areas of interest. In gassy mines, placing electronic equipment close to active mining 

areas is problematic unless the equipment is intrinsically safe or can be made permissible, or 

considered safe to operate in a potentially explosive environment. No microseismic monitoring 

system in use today is considered intrinsically safe under U.S. regulations, although some 

systems have achieved this rating in other countries. A permissible system can be developed 

through the use of electrical barriers and by housing digitizers and electronics in explosion-proof 

boxes. Before use in the United States, however, such a system must receive MSHA 

certification, which involves a lengthy approval process. Placing sensors and electronics in freshair entries, where permitted, may provide acceptable but more limited coverage.

Wave Propagation Medium Effects

Layered sedimentary structures in coal mines introduce wave propagation effects that are 

absent, or marginal, in hardrock mines. The geology in most hardrock mines is not necessarily 

uniform, but the spatial variation in the seismic velocity across the extent of a typical hardrock 

seismic network is small, on the order of 5%–10%. The small variation in seismic velocity 

allows for the use of a homogeneous isotropic velocity model to calculate reasonably accurate 

event locations. Periodic adjustment of the single P-wave velocity in the model for a hardrock

mine is achieved with additional calibration or production blasts. A big advantage of a 

homogeneous isotropic velocity model, in terms of seismic event location schemes, is that 

seismic raypaths (paths the energy travels from the source to the sensors) closely approximate

straight lines. 

The sedimentary layers found at coal mines, in contrast, have large (10%–90%; [Jones 

1994]), sudden changes in elastic properties, resulting in raypaths that are not straight but

reflected, refracted, and mode converted. Seismic energy can also get trapped in low-velocity 

layers, such as coal seams. As the distance between the source and the sensor increases, the first 

arriving signal is no longer the direct, or straight, raypath from the source but is instead energy 
14 

that travels along longer paths through higher-velocity layers. Thus, in order to use arrival times 

of the first arriving phase to locate events, one must be able to reconstruct the path of the seismic 

wave. Determining the raypath is only possible with a realistic model of the seismic velocity 

structure. An additional important complication is the effect of the excavations and attendant 

longwall caving on the seismic velocity. The three-dimensional alterations of the seismic 

velocity from the excavation accumulate through time and result in the need for a continually 

updated seismic velocity model. Advancements in this area are needed to reduce systematic 

errors in event locations that arise when such complications are not accounted for in the velocity 

model. A summary of the effects of wave propagation in layered media, the influence of lowvelocity gob regions on event locations, and the development of layered velocity models is 

provided in Appendix A. 

Seismic Source Characteristics from Waveform Measurements 

The approaches to seismic monitoring described in this report are designed to capture 

transient vibrations produced by dynamic adjustments in the rock mass. Such a rapid adjustment, 

termed a “seismic event,” produces elastic waves that originate at the “source location.” Analysis 

of the emitted seismic waves allows certain characteristics of the source to be identified. These 

seismic source parameters are briefly introduced in the paragraphs below with further elaboration 

in Appendix B. 

Dynamic failures in the mining environment, ranging from minute rock fractures to largescale triggered fault slip, cover an enormous span in magnitude and energy release. Seismic 

magnitudes, such as the Richter magnitude [Stein and Wysession 2009], are empirical 

instrumental measures of earthquake size based on maximum amplitudes of specific wave types. 

Although they are useful, and widely known through communications with the public about the 

relative sizes of earthquakes, such measures do not provide a direct link with the mechanical 

process that produced the seismic signals. As a more informative alternative, seismologists use 

seismic moment, or moment magnitude, to describe the size of an event [Stein and Wysession 

2009]. Moment magnitude is the preferred magnitude for all earthquakes listed in the USGS 

earthquake catalogs [USGS 2016]. Instead of a single measurement, the current trend is to use 

two independent parameters that are measured on the seismic waveforms to describe the size of 

an earthquake: seismic moment and radiated seismic energy. Seismic moment provides a 

measure of the physical size, and radiated energy provides a measure of its capability to do 

damage to infrastructure.

Seismic moment, Mo, describes the forces required to produce a given amount of fault slip 

and is based on the physical attributes of rupture area, A, averaged shear slip, dd̅, and shear 

modulus, μ, of the source rocks, 

Mo = μ A dd̅ (1)

An advantage of Mo is that it gauges the mechanical energy needed to produce the seismic 

event, whereas earlier magnitude measures lack a one-to-one relationship with radiated 

earthquake energy, Es. Both seismic moment and radiated energy are obtained through 
15 

independent measurements on the seismic waveforms. Mo and Es, and their combinations, can be 

used to estimate source parameters, such as: stress drop, apparent stress, rupture area, average 

slip distance, orientation of the slip vector, etc. [e.g. Gibowicz and Kijko 1994]. 

The simple two-dimensional shear-slip source embodied in Equation 1, also known as a 

double-couple source, involves no volume change. It is a very good model to describe many 

natural earthquakes. The shear-slip model can be shown [Gibowicz and Kijko 1994] to be a 

special case of a more general moment tensor description of a seismic source that includes 

volumetric components. A source that contains a nonshear implosional component is often 

needed to describe mining-induced seismic events that accompany the closure of mine openings

[Fletcher and McGarr 2005]. 

Another important parameter derived from seismic waveform measurements is the corner 

frequency, fc, (Appendix B). The corner frequency is inversely proportional to seismic source 

dimension and is of particular importance to specification of monitoring system characteristics. 

Measurements of seismic moment and corner frequency for a number of cases of mining-induced 

seismicity in hardrock mines6

 are shown in Figure 4. Event magnitude is the moment magnitude 

based on the logarithm of seismic moment (Appendix B). The events span a moment magnitude 

range of -3 < M < +4, which is the magnitude range pertinent to the monitoring strategies 

discussed herein. 

6 Data from hardrock mines are shown because of insufficient measurements of suitable source 

parameters from coal mines to span a similar range in moment magnitude.
16

Figure 4. Relationships between magnitude, seismic moment, corner frequency, and rupture 

dimension. Plotted data are from the studies listed below the graph.

The largest mining-related seismic events ever recorded were produced by triggered slip on 

faults in deep South African mines [Durrheim et al. 2006] and large-scale mine collapse in 

evaporate deposits in Germany and the United States [Whyatt and Varley 2008]. These events 

are in the magnitude 5+ range with corner frequencies less than 1 Hz and would require 

characterization by a larger-scale network than those considered in this document. The smallest 

microseismic events reported from production-oriented, in-mine monitoring systems have 

magnitudes of approximately -3 with source dimensions of tens of centimeters and corner 

frequencies in the kilohertz range (Figure 4). Although not shown in Figure 4, this plot could 

have been extended to higher frequencies and smaller source dimensions. Quantitative analysis 

of laboratory rock fracture experiments with grain-scale and sub-grain scale fracturing produces

magnitudes into the -8 to -6 range [McLaskey and Lockner 2014; Goodfellow and Young 2014]. 

“Acoustic emission” is often used to describe these events but ultrasonic, or “ultrasonic acoustic 

emission,” is more apt for these high-frequency events.
17

Characteristics of Different Seismic Monitoring Systems

A monitoring system must be selected such that the response characteristics of the system

match the expected frequency range (or size) of the seismic events of interest. The system 

response includes the response of both sensor and recording system, as well as the propagation 

medium. In order to resolve corner frequencies from seismic waveforms, Mendecki et al. [1999]

cite the necessity of measuring frequencies that are five times above and below the corner 

frequency. The span of four orders of magnitude in corner frequency in Figure 4 asserts that a 

single type of sensor is inadequate to accurately measure events over the size scale observed for 

mining-induced events.

Array configuration, especially station spacing, is another monitoring system feature that 

must be matched to the event size of interest. Station spacing becomes important because of 

attenuation—low frequencies propagate long distances, but high frequencies are quickly 

attenuated. This provides a type of spatial filtering in that small events with high frequencies 

may be detected when occurring in close proximity to sensors with high-frequency range but 

would not be detected by stations that are farther away. In order to detect and locate small 

events, it is necessary to install a dense network near the sources. For example, if the objective is 

to detect minute events (e.g. M = -3 to -4) associated with a developing roof fall, the network

must be close by and of a sufficiently high-frequency range to capture small-scale events.

The different types of seismic systems needed to span nine orders of magnitude in frequency 

are shown in Figure 5. Piezoelectric sensors used in acoustic emission networks (-8 ≤ M ≤ -3) 

have a response in the tens of kHz to MHz range. Sensors in microseismic systems (-4 ≤ M ≤ 3) 

include piezoelectric-based accelerometers at the high end of the frequency range and movingcoil geophones (seismometers) for the remainder of the range. Inexpensive geophones are 

primarily developed for the exploration geophysics market and have a frequency range that is 

largely 5–10 Hz and above. Circuitry to boost the low-frequency range of geophones so that they 

help resolve the low corner frequencies of the largest mining-induced seismic events provide a 

low-cost alternative to the more expensive seismometers commonly used in earthquake 

seismology. Low-noise, strong-motion accelerometers (0 to 200 Hz response) or seismometers 

with responses down to 1 Hz provide suitable response for coal-mine-oriented district-wide (0 ≤ 

M ≤ 4) and mine-wide (-1 ≤ M ≤ 4) networks.
18

Figure 5. Frequency range of six different types of seismic monitoring systems.

Regional seismic networks (M ≥ 1.5) have used the standard 1-Hz short-period velocity 

seismometers for decades, but are now gradually replacing them with broadband sensors. 

Broadband networks (M ≥ 1.0) now cover the ranges that used to be covered by specialized 

ultra-low frequency sensors (down to a period of ~1,000 s) for worldwide earthquake monitoring 

(M ≥ 4) and the short-period seismometers in regional networks.

Five Seismic Monitoring Strategies for Deep Coal

In this section, five strategies for monitoring seismicity in deep coal mines are presented: 

(1) a single station, (2) a regional seismic network augmentation, (3) a temporary network 

deployment, (4) a district-wide or mine-wide seismic network, and (5) a microseismic network. 

Examples of the use of each of these strategies are provided.

Single-station Option

The deployment of a single seismometer, with suitable data recording and display capability, 

offers an effective way to provide awareness and rudimentary documentation of local seismic 

activity. Of the various options for providing seismic monitoring coverage at an operating mine, 

the use of a single station is the simplest and least expensive method. Additionally, 

mining/geology engineering staff should have the technical skills (i.e., selecting a suitable site, 

connecting the electrical components, and running data collection software) to easily deploy a 

single station as a do-it-yourself project. One of the key features of deploying a single station is 

the ability to provide continually updated visual records of ground motions that come from all 

sources of vibration, including: local mining-related seismic events, earthquakes, local and 

regional blasting, and vehicular traffic and earthwork with heavy machinery. Rough constraint 

can, in many cases, be placed on the origin of the local mining-related seismic events with a little 

effort in manipulating and measuring waveforms recorded by a three-component, or triaxial, 

sensor. Such analysis provides estimates of the distance between the seismometer and the origin 

of typical mining-related seismic events, and estimates of the direction (bearing or back azimuth) 
19

from which the seismic waves originated. Combining range and bearing measurements provides 

a method to estimate source locations using a single triaxial station.

Helicorder Display

A continually updated visual record of ground motion, or helicorder plot, is shown in Figure 

6 for a vertically oriented short-period (1-Hz) uniaxial seismometer deployed in a coal mining 

district. A helicorder display is the digital equivalent of the old pen and ink, or smoked paper, 

drum recordings. They show vibration output from a single seismometer component as a 

function of time—typically a 12- or 24-hour duration.

Through daily examination of helicorder plots, the ability to identify different types of 

seismic sources is rapidly mastered, with suitable initial guidance, so that local mining-related 

events can be quickly recognized and separated from other sources. Sensors should be placed 

away from the immediate vicinity of large local noise sources (e.g., vehicle traffic, conveyor 

belts, wash plants, stockpiles) and closer to the workings of interest. The 12-hour display of 

Figure 6 provides examples of signals from a magnitude 3.3 coal burst at a distance of 12 km 

(7.3 mi), coal trains at a distance of 700 m (2,300 ft), a small local mining-related seismic event 

(M~0.4) at a distance of 1 km (3,300 ft), and background noise from a coal mine wash plant at 

700 m (2,300 ft). 

Figure 6. Helicorder-style plot of 12 hours duration from a 1-Hz short-period vertical seismometer.

A helicorder plot for a single station deployed away from the mine surface infrastructure and 

nearer to an active 270-m-wide (900-ft-wide) longwall panel is shown in Figure 7. A 

conventional short-period (1-Hz) seismometer was placed on the surface 460 m (1,500 ft) above 

the seam at a horizontal distance of 440 m (1,440 ft) from the tailgate edge of the face. Frequent,

brief, spike-like signals associated with small seismic sources (fracturing) in the immediate 

vicinity of the panel are typical for a sensor this close to an active longwall at many western U.S. 

coal mines. The longwall face was actively cutting, accompanied by periods of elevated 

background noise, for approximately half of the time displayed in Figure 7A.

When background seismic event activity is elevated, it can be difficult to readily identify 

notable events. Applying a low-pass filter to the seismic signals before plotting allows larger 

seismic sources to be accentuated. For example, Figure 7B shows a low-pass filtered version of 

the record in Figure 7A. The low-pass filtered trace (Figure 7B) attenuates the numerous short 
20

high-frequency spikes, and highlights the few lower-frequency signals from the largest mining 

events as well as regional and teleseismic earthquakes when present (signals from regional 

earthquakes are on blue and black traces at 9:36 am and 11:09 am local time, on the left axis).

Figure 7. Unfiltered (A) and low-pass filtered (B) helicorder records near active longwall.

As a final example, Figure 8 shows frequent large-magnitude seismic events up to M2.8 

recorded during multiseam panel extraction under 520 m (1,700 ft) of cover. The 200-m-wide

(650-ft-wide) longwall panel retreated approximately 8.5 m (28 ft) per day at a level that was 

~90 m (300 ft) beneath a previously mined seam. The panel, with gateroads that were offset from 

the overlying gateroads, was the fourth panel in a district that undermined the more extensive 

excavations of the upper seam. The single-station, low-pass filtered data was recorded with a 

1-Hz, short-period seismometer that was positioned 3.3 km (2.1 mi) away from the panel. The 

elevated activity during this period indicates significant dynamic deformation of the strata in the 

vicinity of the active and overlying previously mined seams. Although elevated seismic activity 

does not always signal the onset of ground control problems, a damaging event did occur two 

days later, producing extensive floor heave ahead of the face along the tailgate. In this manner, 

the simple visual display of the helicorder record provides a quick assessment, and rudimentary 

documentation, of the seismic activity from that day. 

Figure 8. Multiseam extraction helicorder record showing large-magnitude seismic events.
21

Range Determination

There are two different types of waves that travel through a solid body (called body waves): 

P, or compressional, and S, or shear, waves. P-waves are the seismic equivalent of sound 

(acoustic) waves in air and propagate as an alternating sequence of compression and expansion 

pulses produced by particles in the ground vibrating in the direction of wave propagation (Figure 

9). S-waves, which require a medium with rigidity to propagate, consist of an oscillating shear 

movement with particle motions perpendicular to the direction of wave propagation. A single 

station may be used to estimate the distance between a seismic source and a receiving 

seismometer by estimating the separation between these two body waves, because S-waves 

propagate at roughly 0.5 to 0.7 of the velocity of P-waves.

Figure 9. Seismic body-wave particle motions.

Examples of P and S phases, produced by an M = -0.5 event at seam level, are shown in 

Figure 10 for two different stations. The 4.5-Hz triaxial seismometers were located on the 

surface 600 m (2,000 ft) above the coal seam. Figure 10A shows a P arrival with abrupt onset 

and large amplitude on the Z (vertical) component, but with comparatively little energy at the 

time of the S-wave. Conversely, the two horizontal components (N, E) show higher amplitudes 

for the shear wave than the P-wave.

While the S-wave arrival can be estimated from Figure 10A by direct inspection, the arrival 

time in Figure 10B is not as clear. The difference is directly related to the influence of the 

propagation path of the seismic energy. While the straight-line path lengths between the source 

and the stations are similar at 834 m (2,736 ft) and 1,063 m (3,488 ft), respectively, for the 

stations in Figure 10A and 10B, the signal in Figure 10A traverses intact sedimentary strata, and 

that of Figure 10B traverses disrupted strata above the longwall gob. Several avenues are 

available for improving S-wave arrival time estimations. Instead of viewing the data in a 

coordinate system tied to the fixed E-W, N-S orientation of the sensor components, rotating the 

signal components into a coordinate system aligned with the incoming raypaths (radial and 

tangential components) maximizes separation of the signal into P- and S-waves to facilitate 

picking the S-wave arrival. Nongraphical techniques for estimating S-wave arrivals make use of 

quantitative wave polarization analyses [Cichowicz 1993].
22 

Figure 10. Two sets of three-component seismograms with P and S arrivals.

Because P- and S-waves travel at different velocities, the propagation distance is directly 

related to the difference in arrival times of these two phases. Assuming a homogeneous isotropic 

velocity structure and that the P- and S-velocities Vp, and Vs are known, then D, the range, or 

distance, in meters between the seismometer and the seismic source can be easily derived. 

Arrival times for P and S phases traveling a distance, D, are, respectively:

tp = to + D/ Vp (2)

and

 ts = to + D/ Vs (3)

where event origin time is to. 

Rearranging and substituting Equation 3 into Equation 2 yields: 

D = (ts – tp)/(1/Vs − 1/Vp) (4)

In most cases, independent measurements of both P and S velocities are not available, and 

the S velocity is estimated from an assumed ratio of Vp / Vs. While a common rule of thumb is 

Vp / Vs = √3 [Shearer 2011], values measured for specific sedimentary rock types range from 

1.5 to greater than 2 [Castagna et al. 1985], with this ratio generally increasing to values of 2 

and higher near the Earth’s surface [Nicholson and Simpson 1985]. 
23 

Folding the velocity dependent term in Equation 4 into a single parameter, c, yields: 

D = (ts – tp)*c (5)

where the assumption of a constant Vp / Vs ratio is implicit. The parameter c typically ranges from 

2 to 8 km/s (6,000 to 26,000 ft/s) depending upon whether raypaths traverse short distances under 

shallow cover (low velocity, high P/S velocity ratio) or longer distances through deeper layers 

(high velocity, low P/S velocity ratio). For a particular situation, the value of c is best determined 

through calibration by observing seismic signals from a known source location. Equation 5 

represents the distance to the seismic source regardless of the direction of wave propagation. A set 

of possible solutions to Equation 5 yields a sphere of radius D centered on the seismometer 

(assuming a homogeneous isotropic velocity). In cases where a two-dimensional geometry is 

appropriate, the solution is a circle of radius D centered on the seismometer (Figure 11). 

Figure 11. Increasing S-P times with propagation distance.

As an example of the single-station analysis (Equation 5), we present data collected at an 

isolated station as part of a data-gathering exercise performed prior to setting up a seismic 

network in a mining district. Both a triaxial, strong-motion accelerometer and a triaxial 2-Hz 

seismometer were deployed at the temporary station to collect baseline data over a two-month 

period. Helicorder plots were generated and used to identify the larger events, all of which had 

been saved in separate triggered-event files by the data acquisition software. Measurements of Pand S-wave arrival times were used to tabulate S-P times, which strongly clustered into three 

groups (Figure 12). As three different longwall coal mines were operating in the vicinity of this 

station, the tight groupings suggest that the events likely originated at the three different mines.
24

Figure 12. Observed S-P times exhibit discrete values (A, B, C).

Applying the S-P values to Equation 5 to estimate the distance to the events yielded 

unrealistically large distances when using standard values of c from earthquake seismology 

(8–10 km/s (e.g., [Stein and Wysession 2009]). As the location of one of the operating longwalls 

was known at the time these measurements were made, Equation 5 was used to solve for c for 

that specific distance, giving c = 3.3 km/s. Using this value of c produced range values, or circles 

of potential event locations, as shown in Figure 13. Locations of the longwalls in operation at the 

three mines at the time of the measurements are also shown, indicating fair agreement with range 

measurements estimated simply on the basis of S-P times.

Figure 13. Comparison between range estimates based on S-P times and longwall locations.

Back-azimuth Determination

The back azimuth, or direction from which an event originates, is commonly estimated by 

measuring the recorded seismic waves along three perpendicular axes and fitting a line to the 

polarized P- and/or S-phases. A 180-degree ambiguity is inherent to this method, however,

because the line conveys no information about propagation direction. Back azimuth can be 

estimated graphically using particle-motion diagrams (hodograms/Lissajou figures) or through 

more rigorous polarization analyses such as orthogonal regression and principal component 

analysis.
25

An example particle-motion diagram is provided in Figure 14. The left-most plots show the 

north (N) and east (E) outputs from a single 4.5-Hz triaxial geophone that recorded an M0.8 

event. The middle figures show a magnified section of the dashed line segments in the first 

panel. The plot on the far right shows the output from the north component plotted against the 

output from the east component for the time period shaded in the middle panels. A NW-SE 

polarization of the initial P-wave is evident in the quasi-linear NW-SE trends of the plot. This 

orientation is consistent with the bearing between this station and the source location (black 

arrow) as determined through triangulation, using arrival times from a dense array of geophones. 

Later in the signal, beyond the initial P-wave arrival, energy starts arriving from various 

directions due to wave scattering, reflection, refraction, and general multipathing, which 

seriously degrades the linear polarization. Thus, this technique works best when the raypaths 

between the sources and the seismometer are devoid of complicated structures. Additionally, it is 

important to apply the analysis to only the beginning of the desired phase, and to filter out 

higher-frequency energy, which tends to be more sensitive to local variations in structure. 

Eisermann et al. [2015] found that it is also useful to apply several polarization methods and rate 

the quality of the predicted back azimuth by how well each of the methods agree. 

Figure 14. Seismograms from north- and east-oriented components (red and blue traces in first 

two panels) used to construct a hodogram plot (third panel) illustrating P-wave polarization for 

back-azimuth measurements.

In a temporary network deployment, NIOSH installed a number of broadband seismometers 

over an active longwall panel in the western United States. Triggered data were recorded on 

three channels at a 500-Hz sampling rate. Orthogonal regression was applied to estimate P-wave 

polarization on several events using the first 100 milliseconds of data after the onset of the P 

phase. The data were filtered between 1.0 to 2.0 Hz. Results for a typical event are shown in 

Table 1, where back azimuths predicted from orthogonal regression are compared to those 

calculated from arrival-time inversion. The predicted back azimuths are reasonably accurate,

considering the instruments were manually oriented in the field with a hand-held compass.
26

Table 1. Back azimuths and predicted back azimuths from particle motion analysis 

for temporary deployment

Station

Back 

Azimuth 

(deg)

Predicted 

Back 

Azimuth 

(deg)

Residual 

(deg)

GS 155 157 2

GNW 335 344 9

GSW 190 205 15

GSE 111 119 8

GNE 75 67 -8

GRN 262 266 4

GN 40 56 16

Similar graphical and polarization analyses can be used for the more complex S-wave motion 

by searching for maximum particle motion perpendicular to the wave propagation direction [e.g., 

Cichowicz 1993].

Single-station Location

Combining range and back azimuth estimates results in two possible solutions due to the 180-

degree ambiguity in the back azimuth. The ambiguity can be resolved with knowledge of existing 

workings or measurements with additional stations. In the example of Figure 15, range 

measurements based on S-P times indicate a distance of approximately 1 km (3,300 ft) from the 

underground 14-Hz triaxial geophone (red triangle). A N66°W back azimuth measurement from a 

particle-motion diagram intersects the circular line of constant S-P time at two widely separated 

locations. In this case, because one solution is in the current longwall panel and the other is in old 

workings, the event likely occurred in the current panel. However, events in old workings are 

commonplace, so the ambiguity remains unless additional measurements are available.
27

Figure 15. Map showing outline of a longwall coal mine with a single-station 

location based on range and back-azimuth measurements

Multiple Single Stations

Figure 16 illustrates how additional single-station measurements can constrain the location of 

the same event. Signals from three additional triaxial 14-Hz geophones (red triangles) deployed 

at the level of the coal seam were analyzed to estimate single-station locations using the 

combination of range and back azimuth measurements. An independent determination of the 

source location (cyan symbol) was made through triangulation using inversion of arrival-time 

measurements from 17 stations of a panel-scale microseismic network. A comparison of the four 

independent single-station event location estimates (yellow symbols) with the location 

constrained by the 17-station network (cyan symbol) shows that the level of error associated with 

any one single-station location estimate is, in this example, several hundred meters. Depending 

upon monitoring objectives, this error may be perfectly satisfactory for locating events within the 

confines of the overall 5–6 km (3.5 mi) dimensions of the mine.
28

Figure 16. Comparison of single-station location estimates

from multiple stations.

A common geoscience lesson is to apply Equation 5 to earthquake signals from multiple 

seismometers and then have students graphically solve for an earthquake’s location. When S-P 

times from the same seismic source are measured at two different seismometers and circles of 

appropriate range (diameter) are plotted for each, the solution circles intersect at two different 

locations (assuming a two-dimensional problem). The addition of the second sensor reduces the 

number of possible source locations from infinity to just two. Addition of a third seismometer, 

with its circle of possible source location solutions, identifies the correct solution in the twodimensional, error-free example in Figure 17.

Figure 17. Multiple range estimates based on S-P times constrains

possible event locations.

A few seismometers reporting S-P arrival-time differences can place significant constraint on 

a source location. In some applications, this may be sufficient to meet the monitoring objective. 

For example, this could be the case if the objective was to discriminate between events occurring 

in old workings versus current workings, at the longwall face versus development headings, or in 

adjacent mine properties, etc.
29

As shown in the above examples, the single-station seismic method offers a way to provide 

awareness of the seismic activity in the vicinity of the sensor at a range of distances from tens of 

meters to tens of kilometers. When paired with appropriate digitizing hardware and display 

software, it is a simple, low-cost method that can provide a continually updated visual record of 

ground motion. With a little extra analysis, through manipulation and measurement of the 

waveforms, arrival times of P- and S-phases and phase polarization directions can be used to 

estimate the distance to the seismic event, the back azimuth to the event and, when combined, an 

approximate location.

Another advantage of the single-station approach is that a synchronized timing device (e.g., 

GPS) is not required. Thus, estimation of range, back azimuth, or a single-station location may 

be possible through deployment of suitable blast-recorder type monitoring devices commonly 

available in many coal mining regions.

There are a variety of options for implementing single-station monitoring capability. These 

options range from user-assembled systems based on low-cost geophones, digitizing hardware,

and open-source software, to high-quality fully digital broadband seismometers and proprietary 

software. Appendix C describes possible options for setting up single-station monitoring.

Regional Seismic Network Augmentation Option

The concept of network augmentation is to add one or more seismic stations to an existing 

network to improve coverage in the vicinity of mining operations. Instead of devoting resources 

and effort to develop the required expertise and monitoring network, a mining company could 

purchase the instrumentation, remote power, and telemetry hardware for any number of stations

and partner with a participating network operator (typically a local university). A small amount 

of financial support may be required for telemetry and maintenance costs, but the additional 

station or stations would improve documentation of local seismic activity. The model for 

augmenting a regional network originates from the UUSS practice of placing strong-motion 

seismometers near underground coal mining operations in Utah and integrating the new stations 

with the rest of the network [Arabasz and Pechmann 2001]. However, there must be an existing 

network in the area to implement this option.

The USGS accomplishes its mission to monitor earthquakes across the country by supporting

national and regional seismic networks. The Advanced National Seismic System (ANSS) 

consists of a backbone national network (Figure 18) along with additional coverage by 15 

regional seismic networks [ANSS 2014]. The regional seismic networks are largely operated by 

university groups. The density of seismic network coverage across the country varies 

significantly, with a much higher density of seismometers in recognized areas of frequent 

earthquake occurrence. Much of the rest of the country is covered by the low-density ANSS 

backbone network. Both the epicentral location and depth uncertainty are ~10 km (6.3 mi) across 

much of the United States for events with M ≥ 3, the minimum target magnitude for event 

detection and location in all areas [ANSS 2014]. Many significant mining ground control failures

produce events below this magnitude threshold as well as nondamaging, but notable, dynamic 

adjustments of caving/subsiding ground, making the current ANSS backbone network 

insufficient for most mining operations.
30

Figure 18. Seismic stations of the Nation's backbone network (after [ANSS 2016]).

The ANSS backbone network has recently been updated and expanded to a total of 95 

stations [ANSS 2016]. The 15 regional networks under the ANSS umbrella are relied upon to 

provide additional coverage where earthquake hazards are of concern; in addition, there are 

other stations outside ANSS. In total, ANSS lists almost 50 network codes that have been 

assigned to seismic monitoring networks, ranging from individual stations and small networks 

hosted by universities to the major regional university/government networks of the Nation’s 

monitoring capability. 

Many U.S. coal mining regions (Figure 19) have never had very good coverage by seismic 

networks. However, improvements to the Nation’s seismic monitoring capabilities, beyond the 

ANSS backbone, are occurring in and near some coal mining states because of an earth science 

initiative named Earthscope funded by the National Science Foundation (NSF). Earthscope is 

an NSF-funded “big science” experiment to characterize the geologic and geophysical 

structure of the North American continent, with a special emphasis on processes related to 

earthquakes and volcanoes [NAP 2001]. As part of this initiative, a temporary network of 400 

high-quality broadband stations with ~70-km (~44-mi) spacing moved from west to east across 

the United States. Recognizing the benefit of a high-quality network, and the sparsity of station 

coverage across much of the eastern and central United States, a multi-agency collaboration 

(NSF, USGS, Nuclear Regulatory Commission, DOE) came together to keep 159 of the 400 

stations in place as the Earthscope experiment continues on to Alaska [USARRAY 2015]. The 

159 new stations are shown in Figure 20 as the N4 network. The addition of numerous seismic 

stations in this region increases the likelihood of detecting mining-related events in the area 

and locating them at the correct mine, but still only for the largest of events. As a standalone 

network, the ANSS is inadequate to achieve the main objectives of coal mine seismic 

monitoring in most coal mining regions.
31

Figure 19. Locations of active U.S. underground coal mines (after [CDC 2012]).

One underground mine from each state with coal mining was randomly selected (sample 

coal mines black-outlined diamonds in Figure 20 for eastern-central U.S. mines), and the 

distance to the five nearest seismic stations was determined for each of the selected mines. 

Table 2 lists the distance to the closest station as well as the average distance to the nearest five 

stations. The mines with the best coverage are those in Utah, with a minimum source-receiver 

distance as low as 5 km and an average distance of 31 km for the selected mine. The sourcestation distances are much greater in other states with mines in the areas of West Virginia, 

Kentucky, and Virginia that have previously experienced bumps (polygon in Figure 20),

having a closest station distance of 26–45 km (16–28 mi). However, there are a few seismic 

networks operated by universities near coal mining regions which are undergoing expansions 

in response to concerns related to waste disposal in deep boreholes and hydrofracking. Adding 

one or more stations in the vicinity of one of the mines, and feeding the data into a regional 

network covering the area, would enable smaller events to be detected and properly located. 

Additionally, there are nonconventional event detection methods (i.e., matched filters) that 

have been shown to increase the detection and location capabilities of regional networks that 

cover mining districts to the point of being comparable with deploying a local network over the 

district [Kubacki et al. 2014; Chambers et al. 2015].
32

Figure 20. Seismic stations currently operating in the eastern and central 

United States (after [USARRAY 2015]).
33

Table 2. Distances between underground coal mines and existing seismic stations

Mine State

Distance to 

Closest Station

km (mi)

Average Distance to 

Nearest 5 Stations

km (mi)

Mine A Alabama 45 (28) 105 (65)

Mine B Colorado 57 (35) 73 (46)

Mine C Indiana 56 (35) 60 (37)

Mine D Illinois 48 (30) 64 (40)

Mine E Kentucky 26 (16) 52 (32)

Mine F Ohio 49 (31) 77 (48)

Mine G Oklahoma 70 (43) 119 (74)

Mine H Pennsylvania 33 (20) 74 (46)

Mine I Utah 5 (3) 31 (19)

Mine J Virginia 45 (28) 79 (49)

Mine K West Virginia 41 (25) 98 (61)

Mine L Wyoming 91 (57) 133 (83)

Temporary Network Deployment Option

Temporary deployments have often been used to investigate seismicity associated with 

longwall coal mining at higher sensitivity and resolutions than are possible with regional seismic 

networks [Hatherly et al. 1997; Bischoff et al. 2010b; Arabasz et al. 2005]. Deployment of a 

temporary seismic network is an option that avoids the personnel and financial investments 

associated with a permanent system. With this method, the hardware and software of each of the 

four other types of monitoring options can be implemented on a temporary basis. Reasons to 

adopt this monitoring strategy include: (1) determination of whether or not the technique is 

suitable to the problem at hand, (2) monitoring a ground control issue that is not expected to be a 

recurring problem, or (3) a need to collect premining data as part of the permitting procedure or a 

company’s exploration process.

Some specialized types of equipment that are well-suited to the rapid installation of 

temporary deployments include: certain conventional, cabled, exploration seismograph arrays;

arrays of self-contained independent geophone recording systems used in exploration 

geophysics; and portable broadband seismometers used in earthquake aftershock studies.

A conventional engineering exploration seismograph system consists of one or more strings 

of geophones connected with spread cables to a small portable, battery-operated data acquisition 

unit (some specific exploration systems only record for a very short period of time for refraction 

work and require modification for continuous recording). A laptop receives and displays the 

waveform data and controls the data acquisition process. As an example, a 48-channel 

exploration seismograph was deployed on the surface directly above the longwall face at two

separate coal mines. The deployments were similar at both mines with two 24-channel spread 
34

cables with 14-Hz vertical geophones on 12-m (40-ft) spacing laid out along two perpendicular 

lines (Figure 21). One line was deployed parallel to the headgate, and the second line was

perpendicular to the first and across the active panel. The second line was positioned ahead of 

the face so that, as mining progressed, the face would pass beneath it. Overburden was 275 m 

(900 ft) and 425 m (1,400 ft), respectively, for the two mines. The terrain above the first mine 

was flat with light vegetation, and the network was easily deployed by three people in a couple 

of hours. The terrain for the second mine was much steeper with heavy vegetation, requiring six 

hours to deploy the sensors.

This example illustrates not only the temporary network deployment concept but also the 

vast difference in possible longwall seismic response. A comparison of the signals recorded at 

each mine, while the shearer was actively cutting, is given in Figure 21. Nine geophone signals, 

out of a total of 48, are shown for each mine in 10-minute-long recordings. These signals are 

typical of those observed while the shearer was in operation at the two mines during the severaldays test at each site. Traces from Mine A show very few transient seismic events, which is 

consistent with the general absence of the snapping and cracking sounds when underground in 

the vicinity of the longwall at Mine A. Although there are some sandstone channels in the roof at 

this mine, none were in the vicinity of the longwall face or excavated parts of the panel at the 

time of the measurements. Much of the overburden strata are comprised of layers that largely 

deform plastically, thereby producing very little seismicity. Mine B contains numerous modestly

strong layers, including sandstones, in the roof and exhibits a steady stream of typical mininginduced transient seismic events; these signals coincide with the audible cracking sounds heard

when underground in the vicinity of the active longwall at this mine. The maximum event 

magnitude during the day of monitoring at this mine was M2.3. Neither mine experienced any 

difficulties related to the seismicity accompanying extraction of these longwall panels.
35

Figure 21. Temporary exploration seismic array (magenta lines on left panel) captures 

diverse seismic response at two mine sites (right panel).

The stark difference in seismic response for the two mines underscores the need for

conducting preliminary measurements prior to the selection and design of a seismic monitoring 

approach at any given mine. A single-station measurement is the fastest, easiest way to gauge the 

type of seismic response to be expected.

While a temporary deployment can involve any combination of surface and underground 

sensors, a surface-only deployment is much simpler. It may seem that measurements from the 

surface would be much less sensitive than underground measurements, but this is not necessarily 

so. Longwall panels that are over 300 m (1,000 ft) wide, with large inaccessible gob areas, can 

result in shorter raypaths to surface stations than to available locations for underground stations. 

Because there is generally greater attenuation on longer paths, signals from underground sensors 

may have smaller signal-to-noise ratios. Also, the greatest motion from the closure of a 

horizontal tabular void created by longwall mining is in the vertical direction. Therefore, a 

vertical sensor on the surface will be most sensitive to motion parallel to the expected direction 

of movement, producing maximum signal amplitude. Another factor that degrades the signal-tonoise ratio underground is the greater noise in an underground environment. Direct mechanical 

excavation processes in the seam, ground vibrations in the entries from ventilation currents, and 

the waveguide effect of low-impedance coal seams—which traps vibrations and channels them 

through the medium where the underground sensors are deployed—all negatively impact the 

signal-to-noise ratio. For these reasons, surface sensors may be as effective as underground

sensors, especially in cases where the distance to bedrock is small.

Evidence that surface sensors can detect seismicity associated with individual cuts of the 

longwall face is shown in Figure 22. Seismic activity was recorded on the surface by an array of 

eight 4.5-Hz vertically sensitive geophones distributed in a 425-m (1,400-ft) diameter circle 

above the face. Depth of cover under the array ranged from 180 to 300 m (600 to 1,000 ft). 
36

Figure 22 shows clear evidence of the cycle of increasing and decreasing rates of seismic activity 

as the shearer moves back and forth across the face, with maximum seismicity rates attending 

cutting near the tailgate. Overburden strata in this area consists of interbedded layers of

sandstones, mudstones (shales), and siltstones.

Figure 22. Temporary array of surface geophones tracks seismic response of 

longwall shearer motion.

Large-scale deployments of dense arrays of continuously recording, autonomous geophones 

are becoming commonplace in oil and gas exploration, as well as in research applications [Lin et 

al. 2013]. Up to several thousand self-contained, cordless geophone units incorporating GPS and 

digitizer hardware, measuring roughly 15 cm (6 in) in diameter and 20 cm (8 in) in length, can be 

deployed for continuous recordings of two weeks or longer while operating from an internal or 

external rechargeable battery. The instruments are housed in hardened plastic with spikes 

attached to the bottom of the units to facilitate coupling to the ground. Installation consists of

simply scooping out a small pocket of dirt and pushing the spikes on the bottom of the unit into 

the ground. The internal GPS determines both the physical location coordinates and provides a 

data-synchronizing time stamp.

Another approach to temporary network deployments involves the use of high-quality 

broadband instruments. While broadband instruments require considerably more care in 

installation than geophones, they provide high signal-to-noise ratio signals over a frequency band 

necessary to extract source parameters of some of the larger events. A temporary deployment of 

broadband stations was described in the Single-station Option section. Each seismometer was 

placed in a 30-cm-deep (12-in-deep) hole, carefully leveled, covered, and powered with highcapacity batteries for one month of operation. Analysis of a 6-hour recording period from this 

data set is offered as an example of the temporary deployment of a network of broadband 

stations. Uninterrupted longwall cutting operations took place during this time period.

Continuous GPS-synchronized data streams were locally recorded on each unit and searched 

for time-coincident transient events that could be associated into separate event files. Event files 

were then processed to extract arrival times and event locations. Station locations (blue 

triangles), longwall infrastructure, and event locations for the 60 largest events that occurred 
37

during this time interval are shown in Figure 23. All but one of these events were too small to be 

located by the district-wide network. While the majority of the events cluster about the retreating 

longwall front, three small groups of events fall outside the bounds of the longwall gateroads. 

The two northernmost groups coincide with continuous miner development in the north and east

mains. Events from the southern group coincide with a steeply dipping, NW-trending fault 

structure (shown in Figure 23 as maroon lines).

Figure 23. Seismic activity recorded during temporary deployment of 

broadband stations (red triangles) above active longwall.

District- and Mine-wide Networks Option

A mining district-wide network has dimensions large enough to encompass more than a 

single mining operation. The example described here is jointly owned and operated by coal 

mines of the North Fork Valley, Colorado, and NIOSH [Swanson et al. 2008]. As originally 

conceived, the network was designed to cover a single mine to investigate relative amounts of 

natural seismicity versus activity generated in the longwall caving process. Developing empirical 

relations between seismic event magnitude and levels of shaking measured at various distances 

was another research goal. Over time, the network grew to cover three separate operating 

longwall mines, all of which have a history of coal bursting [Mark et al. 2012]. Additional 

objectives were developed to implement a real-time event monitoring and notification tool, and 

to collect data for use in research studies aimed at quantifying impacts from mining-related 

seismicity. The potential impacts include dynamic rock mass failures, or bursts, as well as strong 

shaking in the vicinity of critical structures such as impoundment dams, reservoirs, mine seals, 

mine openings, and steep slopes.

Most longwall mining in this district takes place in three different coal seams under the 

variable topography of mountain-canyon terrain. Depth of cover extends up to 800 m (2,600 ft). 

Competent sandstone units are present with variable thickness and distance above and below the 

minable seams.
38

Stations

Historical reports of seismic activity in this district described events up to M3 and higher; 

therefore, strong-motion accelerometers were selected. Strong-motion seismographs are 

specifically designed to measure ground motions near seismic sources that are strong enough to 

potentially impact the integrity of engineered structures. An additional 1-Hz moving-coil 

seismometer was also included at the same location to provide higher sensitivity to smaller 

and/or more distant seismic events. The data recorders are configured to continuously stream 

data at 100 samples per second, and to trigger and save event data to local digital storage media. 

Time synchronization was provided by GPS receivers at each site.

A total of 17 stations was ultimately installed in the district (Figure 24), providing a coverage 

area of approximately 300 km2 (120 mi2). Outlines in Figure 24 show lease boundaries for the 

three district mines. The average distance between seismic sources and the nearest five stations 

of the network is ~3.0 km (1.8 mi) with the largest distance averaging ~19 km (12 mi). This 

station spacing provides suitable coverage as a district-wide network. However, if only one mine 

was present and the array had fewer stations but similar station density, it could effectively serve 

as a coarse mine-wide network. In both district- and mine-wide networks, stations are confined 

to the surface because few places in an underground coal mine remain accessible for very long 

since longwall sections are continually excavated and subsequently sealed. 

Figure 24. Seismic stations of the North Fork Valley district network (triangle symbols).

Station sites were selected to provide suitable azimuthal coverage of the mining areas, a 

workable telemetry path, concealment from view, and, wherever possible, access to bedrock. 

Separate instrument and power vaults were installed by digging shallow holes and installing 

vertically oriented, corrugated steel culverts with locking lids (Figure 25). A flat concrete floor 

was poured onto exposed bedrock in the instrument vault, onto which the strong-motion 

seismometer was bolted. Remote power sources were constructed with photovoltaic panels and 

deep-cycle batteries. 
39

Figure 25. Seismometer, remote power, and telemetry infrastructure

of a typical district-wide network station.

Data Communications

A variety of license-free radio networks are used to communicate with and transfer data from 

the seismic stations. Continuous digital seismic waveform data is sent from each station to a 

central site equipped with an Internet connection. The serial/IP data network allows complete 

control over the instruments from distant Internet-connected user locations.

Data Processing

A large portion of the data collection, processing, analysis, and display components uses the 

Earthworm software [Earle et al. 2003; Friberg et al. 2010]. The freely available, open-source 

software is configured to automatically detect, process, archive, and analyze seismic events from 

numerous types of seismic sensors and to make the raw data and processed results available over 

a computer network and the Internet. Earthworm has been in use since the late 1990s and, while 

initially receiving major support from the USGS, it is now supported and further developed 

through an active international network of users and operators of seismic networks, in addition to 

contractors hired by the USGS.

One of the powerful features of Earthworm is its distribution of data acquisition and 

processing across computers of mixed operating systems that are connected via a network. In the

mining-district-wide network example described here, the main data acquisition, 

communications, and processing computers are located in a secure location with Internet access 

in the field. Further processing, data archiving, web access, and email notifications are handled 

by computers in the NIOSH Spokane office.

After arrival times are automatically picked for each station by Earthworm, event locations 

are calculated using the Hypoinverse software [Klein 2014] with a layered velocity model.

Empirical station correction factors are developed to help counteract the effects of spatial 

variations in seismic velocity. A discussion of how both station correction factors and multipleevent location methods can be used to improve event locations is given in Appendix D. 
40 

Magnitude estimates are obtained from Earthworm modules using both the duration of 

seismic signals (coda magnitude) and their amplitudes (local/Richter magnitude). A moment 

magnitude Earthworm module is currently in beta testing.

Data Access

Raw and processed data are made available to users in several ways. Data can be accessed 

via a password-protected web page. The web page provides access to helicorder plots, triggeredevent waveform files, event location and magnitude calculations from the automated processing, 

and summary peak ground motion data for each station. Time-critical information can also be 

distributed via email, pager, and/or text messages. 

Event Location Display

Outside of Earthworm, a web server provides continuous reporting of seismic activity in the 

vicinity of the network. Client software provides near real-time maps of event locations, 

magnitudes, and times of occurrence based on the automated processing results with different 

audible notifications based on selected magnitude thresholds. Both server and client software use 

the Java-based California Integrated Seismic Network (CISN) model, designed for California 

earthquake monitoring and emergency management 24/7 operations centers 

(http://www.cisn.org/). 

Custom modifications were made to the CISN software to provide additional control of the 

quality of the displayed events. Such quality control is desirable when results from wholly 

automated processing are accessed by non-seismologist users. Some event locations and 

magnitude assignments can be erroneously calculated because software currently cannot entirely 

replace the judgment of a human analyst. Such erroneous results occur, for example, with low 

signal-to-noise ratios or when multiple seismic events occur close together in time. These 

additional display control parameters increase the robustness of the event reporting and reduce 

the display of poorly constrained events when operating under the standard wholly automated 

processing conditions. 

Email, Text, and Pager Notifications

Automated emails are used to notify users about the occurrence of events and highacceleration levels using two separate Earthworm modules. Email recipients can tailor the 

conditions required for email issuance by specifying values for acceleration threshold, station 

names, minimum event magnitude, and distance to the event from individual stations. When 

strong ground shaking in the vicinity of critical structures is of concern, emails can be 

automatically issued to a list of users when threshold accelerations are met or exceeded. Emails 

may also be issued upon the occurrence of an event, independent of the observed acceleration 

levels, which include a map showing the event’s location and waveforms with automated phase 

picks. 

Example Results

The software-automated results can generally be used to identify the specific mine, or section 

of a mine, in which particular events (M>0) occur. Figure 26 is based on the output from the 

CISN (also known as Quakewatch) client, showing a 60-day period in 2015 where all of the 

events (0 ≤ M ≤ 3.0) were processed in software without any human intervention. Three clusters 

of activity are apparent in the northern section. The westernmost cluster is associated with active 
41

longwall mining in a panel with overburden ranging from 335 m to 600 m (1,100 to 2,000 ft). 

The two clusters to the east of this longwall originate in old mined-out areas where mining is no 

longer taking place. The middle cluster centers on an area where there was multi-seam longwall 

mining under 500 m (1,700 ft) of overburden; mining ceased in this area in 2010. The 

easternmost cluster is centered on a longwall district under cover ranging from 500 m to 800 m 

(1,700 to 2,600 ft) where mining stopped in 2012. The diffuse activity to the southeast occurs in 

areas containing both previous longwall mining under depths of cover up to 700 m (2,300 ft) and 

an active longwall mining under ~200 to 335 m (700 to 1,100 ft) of cover.

The distribution of general seismic activity across this district shows that there is a significant 

variety in the seismic response of both the active and formerly active mining areas. The 

awareness of the activity and the ability to discriminate between events occurring at the different 

mines, and within specific areas of the same mine, comes directly from the automatically 

processed results.

Figure 26. Software-automated event locations from district-wide network over 60-day period.

District-wide network data can, with the additional effort of making manual adjustments in 

arrival time picks and reprocessing, resolve further detail on deformation processes operating on 

the scale of individual longwall panels and smaller areas. Two examples are presented below to 

illustrate this point.
42

Seismic Event Sequence in a Working Area

A dynamic failure of pillars and floor occurred during room-and-pillar retreat operations 

under 150–210 m (500–700 ft) of cover. The progressive failure was accompanied in rapid 

succession by four seismic events with M ≥ 2.0. Seismic activity (M ≥ 1) as a function of time is 

shown in Figure 27A. A spike in seismic activity occurs on the first day with additional events 

tapering off with time. An unfiltered helicorder record from a station 3 km (2 mi) away is shown 

in Figure 27B. A closer look at the timing of the large-event seismic activity (large events on red 

trace) reveals that the period of increased seismic activity did not initiate with the occurrence of a 

large seismic event, as observed for some rockbursts, but rather the four large (M ≥ 2) seismic 

events occurred over the span of 3.5 minutes approximately 8 hours after the start of the 

accelerating seismic activity. The large events were apparently more of a compounding effect 

than a cause of the initiation of the instability. An area of burnt coal was encountered adjacent to 

the workings, indicating possible enhancement of overburden loading in the area of mining due 

to volume reduction of the adjacent oxidized coal. Figures 27C and 27D illustrate the temporal 

and spatial progression of the seismic activity, which generally coincides with the damaged area. 

While the time history plot in Figure 27C includes events with M ≥ 1 that were automatically 

processed, the plot in Figure 27D is limited to manually processed events with M ≥ 1.5. 

The unusual occurrence of four large seismic events within a few minutes presented some

difficulty for the automated processing. As the software had difficulty recognizing where one 

event started and the next one ended, the system inferred fewer events but with larger 

magnitudes than obtained with manual processing.
43

Figure 27. Seismic response to progressive failure. (A) number of events per day with M ≥ 1, (B) 

helicorder plot from day with four events with M ≥ 2, (C) cumulative number of events during 

progressive failure, (D) map view of manually processed event locations.

Monitoring the seismic response of the progressive failure with the district-wide system 

provided documentation of the: (1) extent of the disturbed area generating seismic activity, 

(2) magnitudes of the events, and (3) times at which activity accelerated and then abated. The 

latter provides information to help identify the return of the ground to a stable state, which is 

useful for making decisions regarding the progress of an accident investigation and plans for 

mine recovery.

Seismic Event Sequence in an Inaccessible Area

The second example illustrates a period of elevated seismic activity that coincided with the 

extraction of the last panel in a five-panel longwall sequence. Depth of cover over panel 5 

ranged from 350 to 575 m (1,150 to 1,900 ft) and had steadily increased from 150 m (500 ft) as 

panels 1 through 5 were mined (Figure 28). Numerous steeply dipping fault structures cut 

across the panels.
44

Figure 28. Map showing a five-panel longwall section traversed 

by several steeply dipping faults (blue lines).

As extraction began on the fifth panel, the seismicity rate, reflected in the slope of the 

cumulative number of events as a function of time (Figure 29), increased substantially (months 

31–39) and remained quite constant as the panel was mined at a nearly constant rate—the 

average retreat rate over all panels was 7.2 ± 0.5 m/day (24 ± 2 ft/day). Observations made in an 

investigation of manually processed events with M ≥ 1.5 include: (1) rapid increase in the 

seismicity rate with the mining of the fifth panel was not accompanied by any notable, or 

unusual, change in ground response in the working areas, (2) most of the activity (Figure 30) was 

confined to inaccessible areas behind the longwall face, occurring up to 500 m (1,600 ft) back, 

(3) events along the tailgate had a larger average magnitude than the events along the headgate, 

and (4) seismic activity did not appear to be dominantly affected by the presence of faulting. 

These observations are consistent with our qualitative understanding of how the rock mass 

should respond to a growing number of adjacent panels under increasing overburden conditions. 

The increasing rate of seismicity (56, 63, 340, 728, and 2,608 events with M > 1.0, respectively, 

for panels 1–5) is a reflection of the combination of increasing span of the overall excavation—

which reduces the stiffness of the global rock mass loading environment—and increasing depth 

of cover. Studies that use such data sets to evaluate how well numerical modeling predictions 

match field observations are currently underway. 
45

Figure 29. Cumulative number of seismic events with M ≥ 1.0 during a

5-panel extraction sequence, showing a significant increase in 

activity during mining of the fifth panel (months 31–39).

Figure 30. Seismic event locations during mining of 

fifth panel in 5-panel sequence.

Summary

A district-wide seismic network with software-automated processing provides: (1) a real-time 

monitoring tool to bring awareness and documentation of seismic activity, (2) automated event 

locations that differentiate sources between mines or locations within an individual mine, and (3) 

an automated communication tool for distributing information on the occurrence of events and 

levels of observed peak ground motion. With additional effort expended in manual processing 

and analysis, dynamic failure processes on the scale of individual longwall panels, or smaller 

areas, can be better scrutinized.
46

Microseismic Monitoring Network Option

A microseismic monitoring network uses a focused, dense sensor array to target a limited 

area of mine workings. As described herein, our emphasis is on active longwall panels or a series 

of adjacent panels under deep cover. These types of networks are known variously as panelscale, face-centered, or close-in networks [Arabasz and McCarter 2009]. They are intended to 

provide detailed coverage of seismically related deformation in active mine workings as opposed 

to a mine-wide or district-wide system that monitors only the largest events over a broad region 

without focusing in any one area. Compared to the latter systems, a microseismic network offers 

higher event detection sensitivity and better spatial resolution of event locations.

In a production environment, the only practical way for a mine operator to implement this 

scale of a network is to secure the services of a commercial vendor that provides microseismic 

monitoring in underground mines. With the voluminous amount of event data possible, the 

additional advantage of the commercial vendor is the availability of 24/7/365 support and the 

possibility of conducting all processing, analysis, and report generation externally via a service 

contract. A microseismic network also requires the greatest amount of upfront, and annual, cost 

and participation by mine personnel. Only a few highly specialized companies provide these 

networks to the mining industry as a day-to-day production tool.

A commercial microseismic system was evaluated by NIOSH in a joint effort with a coal 

operator in one of the North Fork Valley coal mines. The system was deployed while extracting 

longwall panels under depths of cover ranging from 460 to 800 m (1,500 to 2,600 ft) and is 

described in the sections that follow.

Stations

To minimize the distance between the expected event locations and the seismic stations, eight 

geophones were installed underground and 10 to 12 were placed on the surface (Figure 31). Due 

to the limited areas where underground stations could be placed, raypaths to many of the surface 

stations were often shorter than those to the more distant underground stations; signals to surface 

stations frequently appeared among the first five P-wave arrivals, despite the thick overburden.

Figure 31. Surface and underground geophone stations of microseismic array

covering one or more adjacent longwall panels.
47

Stations installed in the mains and headgate were confined to fresh-air entries. Geophones 

were periodically removed in the headgate and relocated ahead of the face to remain in fresh air. 

Although recommended use in hardrock mines is to install the 14-Hz triaxial geophones in 10-mdeep boreholes, in this example the geophones were mounted on the roof to facilitate easy 

removal and relocation. Circuitry to extend the low-frequency range was used with all geophones 

to enhance measurements of corner frequencies of the large magnitude events. Low-power 

digitizing units were located adjacent to the geophones and, at the end of long cables, the 

current-limiting receiving electronics were located in fresh air with other mine control and 

communications electronics. A fiber-optic cable provided timing and network communications 

with the surface where the system server merged surface and underground data streams into the 

processing system.

Surface geophones were located 450 to 800 m (1,500 to 2,600 ft) above the coal seam. The 

4.5-Hz triaxial sensors were mounted on concrete pads (Figure 32A), positioned to take 

advantage of any near-surface bedrock, avoiding where possible direct placement over 

underground areas that were to be fully excavated. Remote photovoltaic systems (Figure 32B) 

powered each station’s digitizing and communications electronics with a digital radio telemetry 

link back to the Internet-connected data acquisition and processing server. Station timing is 

provided through GPS receivers. A temporary mounting platform for the power, data acquisition, 

and communications infrastructure allows for periodic relocation of the surface stations to 

maintain an appropriate distance to the active panels as panels are excavated, and as gob-vent 

borehole access roads are reclaimed.

Figure 32. Surface station of microseismic network, showing: (a) triaxial surface geophone 

prior to burial and (b) station infrastructure platform.

During monitoring of the first two panels, the array footprint measured approximately 2,600 

by 1,500 m (8,500 by 5,000 ft), which then expanded northward as additional panels were mined.

The average three-dimensional distance between a seismic source and a geophone for the first 

five arrivals was ~550 m (~1,800 ft) with a maximum distance of ~1,800 m (~5,900 ft). Such 

sensor spacing would qualify this system as a mine-wide system under the sensor density 

specifications outlined by Mendecki et al. [1999] for hardrock mines (5 sensors within 1 km 

(3,300 ft)) instead of a microseismic monitoring system (5 sensors within 300 m (1,000 ft)). 

Nevertheless, we retain the term “microseismic” with our emphasis on focused monitoring in the 

vicinity of a rapidly moving face over a region that is considerably smaller than the overall areal 

extent of the mine.
48

Data Processing

Sampling rates for underground and surface stations were 5,000 and 1,000 samples per 

second, respectively. Local data screening takes place at each station to determine candidate 

events rather than transmitting a continuous data stream to a main system computer. Apparent 

event triggers from each station are associated into coherent events at the main data collection 

computer where further processing occurs (event locations and source parameter estimates). 

Event files are then saved to disk and manually processed. Moment magnitudes are calculated 

based on a combination of event moment and energy measures. Magnitude ranges observed 

during the monitoring period extended from 3.4 down to -3, with as many as 3,500 triggered 

event files recorded each day during active periods. As the smallest events were not of great 

interest in this study, a threshold of 10 station triggers was set as the minimum required for 

processing an event. Such a large number of reporting stations ensured that the event was big 

enough to provide adequate signal-to-noise ratio to place reasonably good constraint on solutions 

of event locations.

The main data collection and processing PC was located in a deeply incised canyon where 

there was no conventional commercial Internet access available. Consequently, a satellite 

receiver was used to provide connectivity. In addition to remote access and control, triggered 

event files, summary event parameters, etc., were continually copied to a remote data analysis 

platform at NIOSH for viewing, archiving, analysis, and manual reprocessing.

Event locations were calculated using a dipping, layered velocity model. Two- and threelayer simplified models were developed through a simultaneous inversion of arrival times for 

both event locations and velocity model [King 2012]. This procedure was developed to provide 

the ability to periodically update the velocity model as mining progressed. A two-layer model 

with the interface between the layers positioned at the coal seam was used in the event locations 

presented herein. The P-wave velocities of the upper and lower layers were 2,771 m/s (9,091 ft/s) 

and 3,880 m/s (12,467 ft/s), respectively. While the two-layer model provided a more realistic 

event distribution than a homogeneous isotropic model, the results shown in the next section, 

Example Results, indicate the need for a three-dimensional velocity model to adequately account 

for the impact of voids, gob, and fracture zones resulting from the excavation.

Resolution of the vertical location coordinate is difficult with the sensor geometry, as in 

Figure 31, coupled with the uncertainties in the velocity model. Additional processing effort is 

required to place confidence in the vertical positions of the activity, as small perturbations in the 

rapidly changing velocity in the vertical direction can have significant changes in the calculated 

vertical location coordinate.

S-phases are ordinarily used to increase the constraint on event locations in hardrock mines 

with homogeneous isotropic velocity structure, especially for systems with few stations. Using Sphases in the location process in this coal mine environment, however, had the opposite effect. 

Several factors are thought to contribute to this behavior: (1) anisotropy in the layers between the 

coal seam and the surface, (2) violation of assumption of constant Vp / Vs ratio, (3) shear-wave 

splitting, and (4) the presence of high-amplitude trapped and interface phases, which are

mistaken for the S-phase on underground stations. The inconsistent, or misinterpreted, picking of 

the S-wave arrival resulted in event locations that were generally degraded; thus, use of the 

shear-wave arrival for locations was minimized.
49

Example Results

Microseismic activity observed over a seven-day period while mining the second panel of a 

longwall section is shown in Figure 33. The 4,331 events occurred as the face retreated 42 m 

(137 ft) during nine longwall production shifts (excavated area shown in yellow rectangle). 

Several features in this example highlight the types of information that are potentially available 

through scrutiny of seismic activity recorded by this scale of a network. The heaviest 

concentration of activity occurs in a broad band along the 250-m-wide (825-ft-wide) longwall 

face. Activity also concentrates along the tailgate (gateroad 2) behind the face with a light

concentration of events ahead of the immediate face activity on the tailgate side. Similarly, there 

is a light concentration of seismicity on the pillars to the south of the first excavated panel 

(vicinity of gateroad 1). Both of these areas of activity might be a response to the increases in 

abutment stress from the extraction of the second panel, which are superimposed on stresses 

emplaced after the first panel is mined. There is also a notable concentration of activity behind 

the face on the headgate side (gateroad 3), but instead of concentrating on the headgate pillars 

themselves, they locate to the north of the headgate pillars in the solid coal of the next panel. The 

absence of activity in the headgate pillars and the presence, at a greater distance, of significant 

activity in the solid coal panel suggests systematic mislocation of the headgate pillar events to 

the north. Such systematic offsets are commonly observed in close-in network data with the 

mislocations generally occurring in a direction away from a low-velocity zone such as a longwall 

gob area. This effect arises because travel times are slower through the gob than are assumed 

with a uniform layered velocity model (e.g., Figure A46 in Appendix A).

Figure 33. Seven days of seismic activity (red circles) located by the microseismic network

during 42 m (137 ft) of longwall retreat (yellow rectangle). Completed longwall panels are 

shaded gray and unmined panels are white.

Despite the current uncertainties in event locations, it is clear that the data can be used to 

delineate temporal and spatial trends of deformation processes in a way that is not possible with 

visual observations. The pattern of deformation illuminated by the larger events is somewhat 

different from that inferred from the smaller-magnitude events of the microseismic data. To 

emphasize the larger events (shown in Figure 34), events with magnitudes in the ranges 0 ≤ M < 

1.5 and 1.5 ≤ M ≤ 2.7 (as confirmed with the district-wide network) are highlighted with blue 

and yellow symbols, respectively. Although the heaviest concentration of events located by the 

microseismic system cluster around the longwall face, the larger events occur more in the 
50

gateroads and panel edge at the tailgate. The pattern of large-event seismic deformation is similar 

to the pattern observed with the district-wide network at the mine in Figure 30, where events 

with M ≥ 1.5 were distributed along both gateroads behind the face.

Figure 34. Large magnitude events during 42 m (137 ft) of longwall retreat 

(0 ≤ M < 1.5, blue; 1.5 ≤ M ≤ 2.7, yellow).

One of the clear benefits of the microseismic system is its ability to delineate finer-scale 

details of mine response not evident with arrays that are less dense and less sensitive. Even in the 

absence of active longwall mining, a time-dependent rock-mass deformation response becomes 

highlighted when tracking small-magnitude activity through time. Figure 35 shows events (-2.7 ≤ 

M ≤ 1.2) occurring over a one-month period several weeks after the first panel was completed. 

This one-month period of time coincided with the delayed resumption of longwall retreat as 

development of the second-panel headgate (gateroad 3) was finished. The concentration of 

activity midway along gateroad 3 in Figure 35 was produced during completion of this headgate.

When viewed over similar periods of time, concentrations of low-level activity in areas without 

active mining were sometimes reported by the mine operator to be areas that ultimately required 

additional support. The concentrated activity along gateroad 2 is an example of an area that had 

such a time-dependent deformation response.

Figure 35. Microseismic activity observed during 30-day period of no mining.
51

On the time scale of individual mining shifts, or days of mining, a clear time-dependent 

response in microseismic activity was observed. Figure 36 shows the number of events located in 

each hour of the day over the course of one month of mining during two shifts a day (day and 

swing shifts). Longwall operation starts around 8 a.m. and generally continues at a steady rate 

until an hour or so before midnight (hour 24). Following the peak of activity between 9 and 10 

p.m. (hours 21 and 22), the rate declines until the day shift resumes the next day.

Figure 36. Number of seismic events detected during each hour of the day during a 

30-day period with longwall production on both day and swing shifts. Peak activity 

occurs between 9 and 10 p.m. (hours 21 and 22).

Finally, in order to contrast the difference between the microseismic network and that of a 

mine-, or district-wide network, we compare the event detection sensitivity of each over a 

common recording period in Figure 37. The number of events recorded, processed, and located 

by each network, as well as longwall face-retreat distance, are shown for each day in this 10-day 

period. Approximately 32 times more events were detected and located by the microseismic 

(panel-scale) network than the district-wide network. An even larger number of microseismic 

events could have been processed with a reduction of the minimum number of station triggers 

specified for manual processing, which was purposefully set high to guarantee a high signal-tonoise ratio. On average, four triggered event files were recorded for every event that was 

manually processed with the minimum 10-station triggers criterion. If activity rates continued as 

observed in this 10-day period for one year, the district-wide network would have reported 972 

events for this mine, while the microseismic network would have located 30,945 events and 

recorded 118,100 waveform files.
52

Figure 37. Seismic activity per day detected by microseismic-scale (panel-scale) and 

district-wide networks and daily longwall retreat distances.
53

Discussion

Many of the objectives for seismic monitoring in deep coal mines can be met by more than 

one monitoring strategy. Monitoring objectives and appropriate strategies to meet various 

objectives are identified in Table 3.

Table 3. Seismic monitoring objectives matched to monitoring strategies

Factors Guiding Strategy Selection

Required location accuracy and anticipated event magnitude guide the selection of which 

monitoring strategy is most appropriate. In general, event location errors are reduced and 

smaller events are detected by increasing sensor density as in the progression from regional- to 

district- to mine-wide to microseismic-scale networks. If the goal is to evaluate the progression 

of failure that culminates in periodic caving of a massive cantilevering roof layer above a 

longwall face, a microseismic network may be desired. The high sensitivity and sensor density 

of a microseismic network provides the ability to detect and locate small-scale fractures 

attending macroscopic failure progression (e.g., [Hayes 2000]). If the objective is to provide 

documentation of the absence of activity in a mining operation that expects none, a single 

station with continuous monitoring can provide information to satisfy this objective. 

Alternatively, if it is desired to be able to differentiate between modest-sized events occurring in 

old workings and the adjacent current active workings, then a system with appropriate event 

location accuracy must be selected.

Objective

Single 

station

monitoring

Augment 

regional 

network 

monitoring

Temporary 

deployment 

monitoring

District-/ 

mine-wide 

monitoring

Microseismic 

monitoring

Awareness X X X X X

Documentation for 

external sources Blank cell X X X Blank cell

Documentation for 

in-mine sources Blank cell Blank cell X X X

Vibration levels X Blank cell X X X

Rescue response, 

damage assessment Blank cell Blank cell X X X

Resolve mine and 

geologic structures X X X

Post-event analysis Blank cell Blank cell X X X

Evaluation/verificati

on of mine design Blank cell Blank cell X X X

Hazard assessment Blank cell Blank cell X X X
54

Pre-installation Estimates of Event Location Accuracy

Based on experience in hardrock mines, recommendations have been made for minimum 

source-to-receiver distances in different types of monitoring networks (e.g., [Mendecki et al. 

1999]). Table 4 identifies recommended minimum source-to-receiver distances, or sensor 

densities, associated with district-wide, mine-wide (or shaft-wide), and microseismic-scale 

networks in deep South African hardrock mines. The networks are assumed to contain five 

triaxial sensors, yielding 15 total components reporting either P or S phase arrivals. If shear-wave 

arrivals are not used, a higher sensor density than is shown in Table 4 is required to provide the 

same constraint on event locations. Sensor densities are specified as average source-to-receiver 

distances for the first five sensors reporting arrivals. Sensor densities listed in Table 4 are 

suggested minimum values, and much higher sensor densities are found in many hardrock mines. 

Table 4. Minimum sensor densities for different scale networks (after [Mendecki et al. 

1999])

Network Scale (km) Minimum 

Magnitude Minimum sensor density

District/Regional 1–30 0 5 sites within 5 km of source

Mine-wide 0.3–5 -1 5 sites within 1 km of source

Microseismic 0.1–1 -3 5 sites within 0.30 km 

(300 m) of source

For comparison, the coal mine district-wide and microseismic-scale network examples have 

sensor density distances of five sites within 3.0 km (1.9 mi) and 550 m (1,800 ft), respectively. 

Although network labels do not directly carry over between hardrock and coal mines, the 

important parameter guiding implementation of seismic monitoring is event location accuracy, so 

that station density is of first-order concern.

One rule-of-thumb estimate for event location accuracy is that, with a suitable velocity 

model, the uncertainty in the location can approach as small as ~3% of the average source-toreceiver distance. Thus, the shorter the source-to-receiver distances are, the smaller the location 

uncertainties. Implicit in this estimate is that errors in station coordinates, arrival-time 

measurements, and velocity represent random fluctuations. For velocity variations of 3% or 

higher, Durrheim et al. [2007] recommend either omitting arrival picks from certain stations or 

implementing a more detailed velocity model (i.e., something other than homogeneous 

isotropic). Applying the 3% rule-of-thumb to the average source-to-receiver distances of Table 4 

yields location errors of 150 m (500 ft) for district-wide, 30 m (100 ft) for mine-wide, and 9 m 

(30 ft) for microseismic networks. Even for hardrock mines, these values should be considered to 

be minimum location uncertainty values obtained under idealized conditions. 

In coal mining environments, the spatial variation in seismic velocity clearly varies with the 

lithology, and to a far greater degree than 3%. As a result, a much greater effort is needed to 

establish realistic layered velocity models to achieve the same constraint on event locations.
55

Effects of Heterogeneous Velocity Structure

Standard location methods using homogeneous layered velocity models appear satisfactory 

for accomplishing objectives where the demands on location accuracy are not stringent, 

including: awareness, documentation, vibration measurement, and rescue response (Table 5). 

Improvements in event locations do appear necessary when the objective is to resolve 

microseismic activity on particular mining or geological structures. Such is the case with postevent analysis, mine design evaluation, and hazard assessment. Generally, in order to achieve the 

necessary location improvements to adequately meet these objectives, a 3D heterogeneous model 

is required. However, there are some nontrivial techniques that may increase event location 

accuracy sufficiently (two such techniques are presented in Appendix D) to allow for some of 

these objectives to be at least partially met with a homogenous layered model. Moreover, 

progress could be made in some areas with slightly less stringent location accuracy requirements 

(e.g., a mine-wide system can detect and locate large magnitude events to aid in long-term coal 

burst risk mitigation).

Table 5. Velocity model requirements for specific monitoring objectives

Objective Homogenous layered 

velocity model

Heterogeneous 

velocity model

Awareness X X

Documentation for 

external sources X X

Documentation for inmine sources X X

Vibration levels X X

Rescue response, damage 

assessment X X

Resolve mine and geologic 

structures Blank cell X

Post-event analysis Blank cell X

Evaluation/verification of 

mine design Blank cell X

Hazard assessment Blank cell X

As examples relevant to using microseismic networks to mitigate coal burst hazards, recent 

field measurements and modeling work have focused on load-transfer distance as an important 

measure of deep longwall mine performance [Larson et al. 2015]. Stress and deformation 

distributions revealed through three-dimensional spatial and temporal trends in microseismic 

event locations and source parameters can provide a much higher degree of constraint in 

measuring load-transfer distance than static rock mechanics measurements taken in limited, 

accessible areas of a mine. Similarly, assessment of the performance of different combinations of 

yield, abutment, and interpanel barrier pillars in mitigating burst hazards [Gilbride and Hardy 

2004] could also benefit by comparing model projections with observations of seismic response. 
56

Unfortunately, systematic errors in event locations (i.e., Figure 33) have the potential to skew 

such results. Therefore, improvements are necessary before these techniques can effectively

satisfy these objectives.

There are two obvious approaches to increasing event location accuracy—increasing sensor 

density and implementing a more realistic velocity model. As described earlier, deploying 

sensors in the working areas at a much higher sensor density is not considered practical for many 

applications relevant over the scale of deep longwall coal panels. In addition, increasing sensor 

density does not eliminate the need for a more realistic velocity model. Use of three-dimensional 

velocity models to account for the influence of voids, pillar deformation, stress heterogeneity,

and gob and fracture zone formation attending excavation can reduce systematic errors that stand 

in the way of accomplishing these objectives.

To aid in the construction of 3D velocity models, NIOSH has developed a prototype seismic 

calibration source for use in return-air entries [Swanson et al. 2014]. Initial tests under 600 m 

(2,000 ft) of cover indicate that the prototype has the range to provide repeatable P-wave firstarrival measurements to stations on the surface out to a horizontal distance of 800 m (2,600 ft) or 

more. Controlled sources provide the opportunity to refine, and gain confidence in, the velocity 

model and to reduce the disparity between known and calculated source locations. Efforts to 

incorporate three-dimensional velocity structures that evolve with longwall mining into 

microseismic event location algorithms are in progress [Boltz et al. 2016].

Hazard Assessment and Risk Management for Rockbursts

Seismic monitoring, analysis, and interpretation are key components of the hazard 

assessment and risk management system used in rockburst-prone hardrock mines. As we 

consider the application of these methods to address issues related to coal bursts, it is useful to 

reflect on the experience gained in hardrock mines. In 2007, Durrheim et al. reported on a major 

effort to review the seismic monitoring component of rockburst risk management practice in 

South African mines, and “to formulate standards, guidelines, and descriptions of best practice.”

Although it was possible to set standards for some areas of mine seismology practice such as 

network design and emergency response, in other areas of practice, such as seismic hazard 

assessment and risk management, it was acknowledged by Durrheim et al. [2007] that “most 

methods currently in use have not been rigorously evaluated and validated.” To a greater or 

lesser extent, “many methods were found to rely on local experience and subjective judgment.” 

As a result, it was “deemed premature to set standards, or even firm guidelines,” because this 

knowledge and experience had not yet been translated into explicit guidelines.

Thus, although the monitoring technology has been widely implemented for satisfying 

numerous objectives, including hazard assessment and risk management, the knowledge and 

experience in these areas have not been readily translated into explicit guidelines. This was 

particularly so for short-term hazard assessments (SHA). As concluded by Durrheim et al. 

[2007]:

“Long- and medium-term seismic hazard assessments are widely used when 

designing mine layouts and sequences. However, there is unanimous agreement that 

the short-term SHA is not reliable enough to be used for routine rockburst risk 

management, despite considerable research effort. Although this has proven to be a 

difficult, and perhaps intractable problem, it is believed to be worthwhile to persevere 
57

with efforts to improve SHA procedures. There are several promising avenues of 

research. For example, the forecasting of rockbursts might be improved through realtime integration of seismic and rock deformation data . . . mining data, and numerical 

models of rock mass behaviour.”

Implementation

Estimates of the levels of complexity, staff time, and expense associated with implementing 

each of the five different monitoring strategies are shown in Table 6. The deployment of a single 

station can readily be accomplished by tech-savvy members of the engineering/geology staff. In

contemplating applying seismic monitoring to any ground control problem, it is strongly

advisable to acquire some preliminary, or sample, data to inform the network selection and 

design process. For the small amount of expense and effort, deployment of a single station offers 

a wealth of information concerning the character of signals, and the frequency and amplitude of 

vibrations. If the mine is located within, or near, the coverage area of a regional seismic 

network—which are mostly operated by universities—mine staff are likely to find a welcome 

response to an offer to host a station of the network. Adding stations to such networks improves 

the event detection sensitivity and location accuracy all across the array. Deployment of a 

temporary network can involve installation of stations in any of the configurations and station 

densities of the other approaches, with more staff time required for underground installations. 

Although it may be possible to find a university or other research group to team up with for 

development of a mine-wide or district-wide network, one is more likely to find a vendor that 

specializes in conventional earthquake or mining seismology. Microseismic monitoring in 

hardrock mines is a mature business, and at least two vendors with worldwide experience in 

installing, servicing, and providing data processing services for underground mining provide 

services in the United States.
58

Table 6. Levels of complexity, staff time, and expense for different seismic 

monitoring strategies

Seismic Monitoring 

Strategy

Level of 

Complexity

Mine Staff 

Involvement Expense

Single station Low Medium Low ($1–10k)

Augment regional 

network Low Low Moderately low

Temporary network 

deployment

Low to 

medium 

(contract out)

Low to medium Moderate (no ongoing expense)

District- and minewide networks High Low to medium Moderately high

Microseismic network High High High ($100k+)

Each monitoring option is amenable to providing real-time data display and distribution over 

a computer network, although this can be difficult with some forms of temporary networks. All 

options allow the collected seismic data to remain private. The only exception may be

augmenting a regional seismic network, where data are often made public, but this would depend 

on the particular agreement with, and requirements of, the regional network.

It is evident that seismic monitoring can play a role in addressing coal mine ground control 

issues and other monitoring objectives. Adoption by industry, though, can only come through 

monitoring experience with attendant demonstration of its utility. The monitoring strategies 

reported herein provide several avenues for acquiring knowledge and experience that can be 

applied toward meeting varied objectives.

Conclusion

Deployment of seismic monitoring systems in the vicinity of deep longwall coal mines 

reveals a variety of seismic rock mass responses. These responses range from nearly aseismic to 

high-magnitude yet non-damaging activity to coal bursting. This report identifies numerous 

potential objectives for seismic monitoring, ranging from providing simple awareness of the 

ground response to mapping out the details of seismic deformation at the face on a real-time 

basis and over a wide range of magnitude scales. Several strategies for accomplishing desired

objectives are described, along with examples of their deployments and potential avenues for 

their implementation. 

Mapping spatial and temporal distributions of microseismic activity has the potential to 

improve mine safety through recognition of atypical rock mass response, evaluation of mine 

stability, and improved understanding of dynamic failures. Variations in the seismic velocity 

structure accompanying coal extraction pose a challenge for accurately locating seismic events. 
59 

In order to relate deformation on specific mine and geologic structures to microseismic activity,

improvements in event location methodologies are needed. Significant progress in using 

microseismic data to achieve some of the ground control hazard assessment and risk 

management objectives will likely ensue with the implementation of realistic time-variable, 

three-dimensional velocity models. 

Demonstration and independent validation of the utility of seismic monitoring in improving

safety in deep U.S. coal mines is a prerequisite to acceptance and use of the technology by the 

mining industry. It is hoped that by offering five different approaches with a wide range in 

expense and complexity, we have identified several possible paths to explore and adopt the use 

of seismic monitoring as one of the tools in a mine’s safety toolbox. 

Acknowledgments

Special acknowledgment is given to the staff of all of the mining companies that participated

in the field studies described in this report, and the numerous employees of the National Institute

for Occupational Safety and Health (NIOSH), both in the field and office, all of whom 

contributed to the success of the field studies. 

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Appendix A: Wave Propagation Medium Effects

Seismic rays, or raypaths, describe trajectories of seismic waves that are similar to light rays 

in optics. The geometrical construct of a raypath arrow that is perpendicular to and pointing in 

the direction of the moving wavefront provides a mathematical simplification, allowing ready 

solutions for many problems in seismology. In the context of geometric ray theory, this appendix 

explores several complications related to waveform interaction with common elements of coal 

mine geology, and discusses strategies that can partially account for geological deviations from 

simple velocity models.

Layer-specific Properties

The sudden variation in elastic properties across sedimentary layers results in raypaths that 

are not straight but are reflected, refracted, and mode converted. Seismic energy can also get 

trapped in low-velocity layers such as coal seams. Refracted rays are deflected at boundaries 

between layers with different seismic velocities, Vi, according to Snell’s law (Figure A1). The 

general trend is for seismic velocities to increase with depth. Critically refracted rays produce 

head waves that travel along the interface between layers where there is a sudden increase in 

seismic velocity. Ultimately, these waves, travelling at the velocity of the faster layer, propagate 

upward on a refracted wavepath that leads to the surface. At a certain distance from the source, 

the refracted waves arrive ahead of the slower direct wave. 

Figure A1. Snell's law governing seismic wave refraction in layered media.

In order to use the arrival times of the first arriving phase to locate events, one must be able 

to reconstruct the path that the seismic energy traveled. This is only possible with a realistic 

model of the seismic velocity structure. Locating events in a hardrock mine with a uniform 

seismic velocity and, consequently, straight raypaths is quite simple by comparison.

Wave reflections at interface boundaries are governed by the mismatch in the acoustic 

impedance (velocity times density, V*ρ) of the layers comprising the boundary. Amplitudes of

reflections increase with higher contrasts in the bounding layers’ acoustic impedance (Figure 

A2). The combination of coal’s low velocity and low density gives it a low impedance and large 

contrast (up to a factor of ~4) with contacting layers. This produces an environment where 
68

seismic energy can undergo complete internal reflection at each boundary of the coal seam, 

which essentially traps the energy within the seam [Dresen and Rüter 1994]. Large contrasts in 

acoustic impedance can also cause mode conversion—the conversion of one seismic phase into 

another [Stein and Wysession 2009]. For example, in addition to reflected and refracted P-waves, 

a P-wave incident on a high-impedance contrast boundary partially converts to reflected and 

refracted S-phases (Figure A3). Similar mode conversions occur with incident shear waves.

Figure A2. Reflection at layer boundaries controlled by acoustic impedance.

Figure A3. Conversion of incident P-wave into reflected P- and S-waves in the upper layer and 

refracted P- and S-waves in the lower layer.

As noted by King [2005], processing algorithms used in hardrock mines for source parameter 

inversion are often not directly applicable to the coal environment. The presence of strong 

reflections from coal seams, near-field effects, and anisotropy needs to be taken into account in 

order to be able to determine accurate source parameters of seismic events.

In addition to the P and S body waves, surface waves are generated when body waves 

interact with a free surface. Surface waves have large amplitudes, propagate with speeds slightly 

less than shear waves, and are dispersive—meaning that waves travel at a speed dependent upon 

the wave frequency.
69

Waveform Character

The complexities associated with wave propagation through stratified layers can significantly 

influence the waveform character in comparison to many hardrock mines where propagation 

conditions are more uniform and isotropic. As an example, waveforms generated by a M2.9 coal 

bump and observed at distances ranging from 0.5 to 16.9 km (0.3 to 10.5 mi) are shown in Figure 

A4. The surface sensors were vertical 1-Hz seismometers. As the distance between source and 

receiver grows, there is an increasing number of distinct paths of variable lengths that waves can 

travel to reach the receiver; the varied travel times along the paths is one reason why the signal 

duration increases with distance traveled. Another factor is the existence of multiple wave types

(P, S, surface waves, trapped modes, etc.) that travel at different speeds, so they arrive 

increasingly separated in time at farther distances. 

Figure A4. Effect of propagation path on waveform character as 

observed at ten seismic stations at increasing distance.

The impact of near-surface layers on waveform character becomes more evident when 

contrasted with signals spending a shorter amount of time in the near-surface layers. Signatures 

from two different events with similar magnitudes and total propagation distances, but different 

paths, are compared in Figure A5. The traces are three-component acceleration waveforms

(vertical, north, and east) recorded at one station for both a mining-related event and a natural 

earthquake (after [Swanson et al. 2008]). By comparing signals at the same seismic station, the 

influences of differing instrument characteristics and local site conditions are eliminated. The 

upper three traces were produced by a magnitude 2.2 nondamaging event in the vicinity of an 
70

active longwall under 600 m (2,000 ft) of cover. The lower three traces, recorded at the same 

station, were produced by a natural earthquake with magnitude 2.4. The distances between the 

sources and station for these two events are approximately 14 and 19 km (8.7 and 11.8 mi), 

respectively.

There are several notable differences in waveform character of the two events in Figure A5. 

The natural event exhibits a classic earthquake-like signature with abrupt P-wave arrival on the 

vertical component Z, followed by a sharp S-wave pulse on the horizontal components N and E. 

The waveform signature of the mining-related event appears quite different. The demarcation of 

P- and S-phases is not nearly as well defined, and there appear to be several additional phases 

present, or complexity that is absent in the earthquake seismogram. In addition, the duration of 

shaking of the mining event appears to be longer despite the slightly smaller magnitude (2.2

versus 2.4), and the frequency content across the entire waveform is lower for the mining event 

than for the natural earthquake. 

Figure A5. Two sets of three-component seismograms (vertical Z, north N, and east E) 

recorded on a common station comparing signals generated from a mining-induced 

event (top) and a natural earthquake (bottom).

The calculated depth of the natural earthquake is approximately 4 km (2.5 mi), whereas the 

depth of the mining-related event is less than ~1 km (0.6 mi). Consequently, waves from the 

natural event spent only a small fraction of their propagation time in the complex, attenuating, 

low-velocity structure of the upper 1 km. Waves from the mining-induced event spent much, if 

not all, of their propagation time within this near-surface region, largely traveling parallel to the 

layering. The propagation path effects account for much of the difference in waveform 

appearance and result in complications when processing the data.

Velocity-model Development

Accurate seismic event locations are not only important in their own right, but they are also 

the starting point for determination of other seismic source parameters. While a simple 

homogeneous isotropic velocity model is adequate for routine event locations in the vast 
71

majority of hardrock mines, a layered model is a minimum requirement to capture the first-order 

velocity characteristics in a coal mine. Calibration blasts are typically used to measure travel 

times in hardrock mines, and velocities from different stations are averaged with the assumption 

of straight raypaths. As most U.S. coal mines do not blast as part of day-to-day operations, 

calibration blasts are not always possible, and when they are, the propagation distances achieved 

with a few sticks of powder are not sufficient to deliver adequate signals across the larger-sized 

arrays in attenuating coal measure rocks. 

In the case of a district-wide network with propagation distances exceeding 10 km (6.3 mi), 

the fastest raypaths between source and receiver are refracted waves that travel via the faster 

deeper layers. For the deeper layers, it is necessary to seek out seismic velocity models that have 

been developed for regional seismic networks or for other geophysical efforts. These models can 

serve as a base model for the lower layers, and then upper layers can be estimated on the basis of 

any borehole sonic logs available from local exploration work and/or knowledge of strata 

properties. Unfortunately, exploration geophysics efforts often concentrate only in the vicinity of 

the coal seam or seams, so sonic logs are not always available to characterize the velocity 

structure between the seam or seams and the surface.

An example sonic log collected from a borehole penetrating just below a coal seam at 500 m 

(1,600 ft) depth is shown in Figure A6. Seismic velocities are calculated from measurements of 

the travel time along the borehole wall between points on the sonic probe spaced 60 cm (24 in) 

apart. An attempt is then made to fit a suitable number of layers with variable widths to the data 

as an estimation for a velocity model. Where available, information on geologic structures may

help constrain the model layers. Geologic information may also provide a starting point for the 

model in the absence of any sonic logs or direct measurements. Results from multiple boreholes 

provide a sense of spatial variation in seismic velocity. Sonic logs from two boreholes offset by 

900 m (3,000 ft) are shown in Figure A7. There can be significant uncertainty in assigning layer 

position, thickness, and velocity values to the entire mining region when large variability exists.

Figure A6. Sonic log in strata above the coal seam with model velocity values 

and layer thicknesses assigned to individual layers (red horizontal bars).
72

Figure A7. Comparison of velocity structures in two nearby boreholes.

In addition, layered media often have anisotropic mechanical properties. Field measurements 

indicate that wave speeds can be as much as 20%–40% higher in the horizontal than vertical 

direction [Uhrig and van Melle 1955]. Accounting for such anisotropy in velocity models 

improves event locations in sedimentary environments (e.g. [King and Talebi 2007]).

Another effect on seismic velocity that is relevant to certain coal mining districts is variation 

in overburden thickness. Calibration blasts were used in one deep western coal mine to place 

constraint on the seismic velocity in the layers immediately above the coal seam (Figure A8). 

Blasts were performed at the edges of the panel near both gateroads and mains containing roofmounted geophones. While the geology of near-seam strata was nominally the same in each 

location, a repeatable systematic variation in velocity was found to be associated with depth of 

cover. An increase of P-wave velocity of approximately 10% was found as depth of cover varied 

from 300 to 750 m (1,000 to 2,500 ft). Assuming a density of 2,500 kg/m3

, the pressure increases 

from 7.4 MPa to 18.4 MPa. Similar velocity changes and greater are often observed in laboratory 

measurements under similar pressure conditions (e.g. [King 1966; Bourbié et al. 1987]). 

Overburden stress thus represents another factor contributing to systematic variations in local 

seismic velocity structure. Although pressure certainly affects P-velocity measurements, the 

magnitude of the effect observed in Figure A8 is less than the variation observed in sonic logs 

from one hole to another within a similar span of distance (Figure A7). Nonetheless, it is a 

source of variability, especially for near-surface sedimentary rocks in canyon-mesa terrain. 

Additionally, fluids, such as ground water, affect P-wave velocity significantly (e.g. [Gregory 

1976]), especially at the (comparatively) low confining conditions of underground coal 

environments. Seasonal ground water fluctuations can also impose a potentially time-variable 

influence on the velocity structure. 
73

Figure A8. Variation of P-wave velocity with overburden.

An initial model of the seismic velocity structure is constructed on the basis of undisturbed 

geologic layers. Once excavation proceeds, especially where large rock volumes are perturbed 

with subsidence attending full-extraction mining, changes in seismic velocity occur. The effect 

of neglecting systematic discrepancies between actual and modeled velocities is illustrated by 

considering raypaths traversing a low-velocity zone such as a longwall gob area. As mentioned 

in reference to the systematic event mislocations observed in Figure 33, such offsets are 

commonly observed in close-in network data with mislocations generally occurring in a direction 

away from regions of low velocity. This effect arises because travel times are slower through the 

gob than are assumed with a uniform isotropic velocity model. A simple graphical depiction of 

travel-time vectors in Figure A9 illustrates the principle. In frame A, travel-time vectors 

emanating from two seismometers (red triangles) have lengths that connect with the actual 

source location (circle symbol) when the seismic velocity is known and uniform (i.e., the 

longwall panel has not yet been mined). In frame B, a modest 10% reduction in seismic velocity 

is imposed upon the extracted longwall panel to simulate the effect of mining, resulting in travel 

times that are 10% longer than in the unmined panel. Travel-time vectors, as shown, no longer 

meet at the actual event location. Frame C shows the resultant change of the position where 

travel-time observations from both sensors can be satisfied simultaneously. This is the effect of 

event mislocation that can happen when raypaths encounter a low-velocity zone that is not 

incorporated into the seismic velocity model used in the location calculations. The magnitude 

and direction of the event mislocation depends strongly upon the position(s) of the low-velocity 

zones with respect to the raypaths that participate in the solution [Boltz et al. 2016].
74

Figure A9. Shift in calculated event locations when seismic velocity model fails

to match actual velocity. Frame A shows raypath lengths calculated for an unmined coal panel 

with known seismic velocity. Frame B shows the effect of a modest 10% reduction in seismic 

velocity. Frame C shows the resultant change of the calculated event location where travel-time

observations from both sensors can be satisfied simultaneously.
75 

Appendix B: Seismic Source Characteristics 

Derived from Waveform Measurements

Waveform Measurements

Analysis of emitted seismic waves allows certain characteristics of the source to be 

estimated. Measurements made on seismic waveforms that have been corrected for the 

influences of the propagation medium, geometric spreading, and instrument response allow 

determination of seismic moment, Mo, corner frequency fc, and radiated seismic energy, Es. From 

these basic measurements other source parameters can be derived: rupture area A, source 

dimension r, stress drop, Δσ, apparent stress, σa, averaged shear slip, dd̅, apparent volume, Va, 

energy index, EI, etc. [e.g., Gibowicz and Kijko 1994]. 

In the frequency domain, Mo can be obtained from the wave displacement amplitude at the 

low-frequency limit, Ωo, and r is inversely proportional to the corner frequency, fc. fc marks the 

point of departure of the amplitude spectrum from the flat low-frequency limit to smaller values 

at higher frequencies (Figure B1).

Figure B1. Theoretical displacement amplitude spectrum (graph on left) 

for shear slip on circular crack (illustration on right).

Seismic wave energy, Es, is computed by integrating the square of the particle velocity 

measured in the far field over all frequencies and incidence angles.

Source Parameters

In this section, seismic moment and the other source parameters are defined. Seismic 

moment is defined as follows: 

Mo = μ A dd 

̅ (B1) 

where μ is the shear modulus of the source rocks, A is rupture area, and dd̅is the averaged shear 

displacement across the rupture surface (Figure B1).

Moment magnitude is based on the logarithm of the seismic moment [Hanks and Kanamori 

1979] with moment specified in units of dyne cm. 
76 

M = (log Mo)/1.5 −10.7 (B2) 

The characteristic source dimension, 

r ~ 1/fc (B3) 

is inversely proportional to the corner frequency, fc, (for P- and/or S-waves).

Values of stress drop, Δσ, are calculated for specific geometrical rupture models. For a 

circular crack model [Brune 1970] of radius r: 

 Δσ = (7/16)*Mo/r3 ~ Mo (fc)

3

 (B4)

A more model-independent measure of stress is given by the apparent stress, 

 σa = ησσ� = η(σf + σi)/2 = μ(Es/Mo) (B5) 

where σσ� is the mean stress acting during the dynamic stress drop, σi and σf are the initial and 

final static stresses in the source zone, respectively, η is the seismic efficiency (< 10%), and Es is 

the radiated seismic energy.

This last equation indicates the direct relation between stress in the source region and 

radiated energy. Examining how the ratio of Es/Mo varies throughout the rock mass for events 

with similar moments gives a probe of the local stresses acting in the vicinity of those events 

[e.g., Mendecki et al. 1999]. In other words, if a map is created of the locations of events with 

similar moments and the event symbol is scaled according to radiated energy, then this map is 

equivalent to a map showing the relative values of stress. Areas of high radiated energies are 

associated with areas of high stress.

Apparent volume, 

Va = Mo/2σa = (Mo)

2

/2μEs (B6)

is a measure of the rock volume undergoing dynamic inelastic strain [Mendecki 1993]. 

Energy index is a ratio that identifies how energetic a particular event is, given that radiated 

energy for a set moment has been observed to range over a factor of 1,000 [van Aswegen and 

Butler 1993]. The energy index is given by: 

EI = E(Mo)/E(Mo)ave (B7)

where the radiated seismic energy measured for one event E(Mo) is normalized to the energy 

expected for an average event of the same moment E(Mo)ave. E(Mo)ave is the value of energy 

expected for an event of moment Mo on the basis of a fit to log Es versus log Mo of all events in 

the region of evaluation. Values of energy index greater than and less than 1 indicate, 

respectively, higher or lower values of energy radiated than the average for that value of 

moment.
77 

Energy Index and Cumulative Apparent Volume Plots

A short- to medium-term hazard assessment method used in some hardrock mines involves 

plotting the time histories of the energy index and cumulative apparent volume. Field data has 

been presented to suggest that when a mine or geologic structure is taking on increased loads, the 

energy index for events starts to increase, meaning that events are getting more energetic than

they were previously. After such a structure starts to fail, or soften, the average radiated energy

drops, putting the energy index below 1. Similarly, a plot of cumulative apparent volume 

[Minney et al. 1997] reflects the volume of inelastic strain, or relaxation, in the source region. 

Plotting the cumulative volume allows one to discern changes in the rate of volume changes 

from the slope of the curve. A number of examples in the literature [Lynch and Mendecki 2001] 

suggest that, in certain cases, the energy index drops and the cumulative apparent volume rises in 

advance of larger seismic events.

Seismic Moment Tensor

The source parameters presented above describe a simple two-dimensional, shear-slip source, 

also known as a double-couple source, which involves no volume change. This can be shown to 

be a special case of a more general moment tensor description of a seismic source that includes 

volumetric components [Gibowicz and Kijko 1994]. Implosional components are often needed 

to describe the closure of mine openings that attend mining-induced seismic events [Fletcher and 

McGarr 2005]. The procedure for extracting the full moment tensor requires clean, high-quality 

seismic waveforms and is not generally as well-suited to software-automated processing as are 

some of the other source parameters, although progress is apparently being made in this area 

[Sen et al. 2013]. 

Seismic Magnitudes, Moments, and Energies 

As a way to attach physical significance to seismic events of different magnitudes, the 

seismic energy radiated from events of a given magnitude/moment is converted to the change in 

the potential energy of falling objects in Table B1 below. Self-similar earthquake energy scaling 

is assumed. In this manner the difference, for example, between a magnitude 0 and -2 event can 

be placed into context. For clarification, the energy associated with the falling objects would not 

produce a seismic event of the given magnitude. It merely represents the amount of radiated 

seismic energy for an event of that magnitude. The radiated energy is only a small fraction of the 

total energy associated with earthquake sources [e.g., Fletcher and McGarr 2005] with estimates 

ranging from less than 1% to 10%. Ranges of rupture dimensions and fault slip, for stress drops 

ranging from 0.1 to 10 MPa, are also given in Table B1 for comparison. 
78

Table B1. Relations between seismic magnitude, moment, radiated energy, rupture 

dimension, and potential energy equivalent

Moment 

magnitude

Seismic 

moment 

(J)

Seismic 

energy

Rupture 

dimension

Amount of 

slip

Potential energy equivalent 

to seismic energy

6 1.1 x 1018 63 TJ 20–80 km 0.05–1 m

5 3.5 x 1016 2 TJ 2–10 km Blank cell

4 1.1 x 1015 63 GJ 0.7–4 km 0.5–10 cm

3 3.5 x 1013 2 GJ 0.2–1 km Blank cell

2 1.1 x 1012 63 MJ 70–300 m 0.05–1 cm 1-m-thick football field size 

chunk of coal falling 1.6 m

1 3.5 x 1010 2 MJ 20–100 m Blank cell D9 dozer falling 4.5 m

0 1.1 x 109 63 kJ 7–30 m 0.05–1 mm 4x4 pickup falling 3 m

-1 3.5 x 107 2 kJ 2–10 m Blank cell 100-kg person falling 2 m

-2 1.1 x 106 63 J 0.8–4 m 5–100 mm six pack of soda falling 3 m

-3 3.5 x 104 2 J 0.2–1 m Blank cell 1 can of soda falling 0.5 m

-4 1.1 x 103 0.06 J 0.08–0.4 m 0.5–10 mm 1 penny falling 2.5 m
79 

Appendix C: Single-Station Monitoring Implementation Options

As implementing a single-station monitoring strategy is often a prudent step before moving 

to more complicated strategies, a number of options are discussed in this appendix. The first 

option is especially tailored as a do-it-yourself project for individuals without direct experience 

in seismology and is described in some detail here. 

Low-cost hardware and software are available through the Public Seismic Network (PSN). 

PSN develops hardware and software to facilitate the recording of earthquakes for the worldwide 

amateur seismology community. Two basic data acquisition platforms are available. The 

4-channel, strong-motion accelerometer unit has a built-in three-component accelerometer and

provision for using an optional 4.5-Hz or 1-Hz geophone for the fourth channel. The second

4-channel digitizing board is oriented toward seismometers but does not come with any sensors.

New and used geophones are often available through PSN. In both data acquisition units, the

maximum sample rate is 200 samples per second (i.e., suitable for far-field awareness monitoring

in district- and mine-wide scale use), there is a choice between a USB or RS-232 interface, there

is an option for GPS timing, and the data can be streamed into an Earthworm monitoring system.

The WinSDR software for instrument configuration and data acquisition comes with the 

hardware (and is capable of running on Raspberry Pi in order to stream data to Earthworm). The 

WinQuake software is the free Windows software package for displaying and manipulating the 

waveform data from the hardware with provision for picking phase arrivals, filtering, calculating 

spectra, and outputting time series data into a few different formats. Additionally, for the more 

advanced users, there is a popular open-source python package called ObsPy [Megies et al. 

2011] which is a very powerful tool for performing data processing and visualization tasks.

The following are possible hardware and software options for implementing a single-station 

approach: 

• PSN hardware and software (http://psn.quake.net/)

• Swarm—open-source software allowing virtual seismometers to produce desktop

helicorder records from on-line local/regional stations

(http://volcanoes.usgs.gov/software/swarm/index.php)

• Earthworm open-source software with Earthworm-compatible hardware

(http://www.earthwormcentral.org/)

• Microseismic system vendors

• Blast vibration monitor equipment

• Broadband seismometer (commercial sources with hardware-specific software)

81

Appendix D: Station Corrections and Multi-event Location Techniques

Travel-time Station Corrections

Event location errors are produced by both random and systematic factors. Random errors are 

associated with slight mispicks of arrival times and errors in the measurement of station 

locations. Random variations in the seismic velocity along raypaths to each station, even in a 

medium well characterized by an isotropic homogeneous velocity model, also contribute random 

fluctuations in arrival times. Most estimates of event location error derived from a location 

calculation assume that these are the sources of error and that they are random, not systematic. 

Station correction factors and multi-event location techniques are calculational methods to 

address small systematic errors in the velocity model.

A fundamental error statistic is the travel-time residual, which is the difference between the 

arrival time predicted by the velocity model and the observed arrival time. Most location 

algorithms seek to minimize the sum, or the sum of the squares, of the individual stations’ traveltime residuals. At a given station, truly random errors should produce travel-time residuals 

approaching zero when averaging over a large number of events. In practice, when viewing 

results from locating many events, a trend toward a consistent residual value, either positive or 

negative, is often observed for each station. This is frequently assumed to be associated with 

systematic variations of velocity near the stations due to local differences in the weathering of 

near-surface rocks. A station-correction factor is a fixed travel-time delay, or advance, that is 

added to the arrival times at each station in order to bring the resulting travel-time residuals 

closer, on average, to zero. Small travel-time residuals are usually interpreted to mean lower 

location error, provided a sufficient number of stations were used in the event’s location.

If the monitored region encompasses a range of geological and geographical conditions, 

multiple velocity models can be incorporated into the location algorithm. Alternatively, if the 

velocity changes are not too major, a different set of station correction factors may be applied 

when locating events in different regions (e.g., the different mines in Figure 24). In this manner, 

systematic variations in the seismic velocity that are not accounted for in the model can be 

ameliorated somewhat by applying these station-correction factors, producing event locations 

relative to those events used to determine the corrections. An example of how a station-correction 

factor can mitigate the impact of a systematic error in velocity is shown in Figure D1.

Figure D1. Velocity model errors partially compensated by travel-time station correction factors.

Raypaths from mine B are affected by topographic features unaccounted for in the velocity model.
82

One station in a network of stations is shown in the vicinity of two mines in Figure D1. 

Raypaths from seismic events generated in the mine on the left travel directly, or straight, to the 

surface station above. For purposes of illustration, it is assumed that the velocity is constant 

between the coal seam and the surface. For an event occurring in the vicinity of Mine B, the 

location procedure, with its homogeneous isotropic velocity model, calculates the travel time as 

if there were no mesa-canyon topography present as indicated by the straight dashed-line 

raypath. The actual, longer raypath takes more time than that assumed by the model. As a result, 

all event locations at Mine B are biased by this systematic deviation from the assumed velocity 

model. Applying a station correction factor, equal to the average residual value, which 

compensates for the additional travel time effectively, but only partially, counteracts the 

systematic error in the velocity model. A more accurate model would be a heterogeneous, threedimensional model, with a P-wave velocity of the speed of sound in air in the canyon.

Since many deep coal mines occur under steep rugged terrain, situations like that depicted in 

Figure D1 can be expected at various positions throughout the extensive underground workings 

of a single mine. Thus, when there is a particular area experiencing a notable event or sequence 

of activity in an area that fails to match the assumed velocity model, (e.g., at the edge of a steep 

side of a canyon in Figure D1), systematic shifts in event locations will occur. Developing a new 

set of station-correction factors for this particular region and this set of events is one way of 

forcing the systematically mislocated events back into an area of known activity without having 

to deal with the complexity of a three-dimensional heterogeneous model.

Multi-event Location Techniques

Master-event location techniques [Gibowicz and Kijko 1994] and double-difference 

techniques [Waldhauser and Ellsworth 2000] provide a way of achieving event locations that are 

accurate relative to the locations of other events. These techniques work by cancelling out the 

effect of spatial variation in velocity along raypaths to a given station and require that all of the 

events that are being located share a largely common path between the source region and each 

sensor. In these methods, travel-time residuals between pairs of events are minimized while 

linking together all other event-station pairs. This only works effectively for certain geometrical 

configurations of stations and events, specifically when the distance between pairs of events is 

small relative to both the distance to the stations and the spatial variations in velocity. Figure D2

shows two identical 6-station networks with two different distributions of activity. In the lefthand side of the figure, raypaths from all of the events back to each of the stations share common 

paths where the unknown deviations from the velocity model are the same. In this geometrical 

configuration, the double-difference location technique cancels out the deviations of the velocity 

model from the actual velocity structure along each raypath [Waldhauser and Ellsworth 2000]. 

The result is a tight clustering of event locations that is often obscured without such processing.

The absolute location of the cluster, however, is still subject to systematic errors resulting from 

inadequacies of the velocity model.
83

With the distribution of activity in the network on the right-hand side of Figure D2, the range 

of azimuths at all of the stations is far greater than in the left-hand side. As the raypaths to the 

stations are not the same for all of the events throughout the mine, the double-differencing 

strategy does not work for the area of activity as a whole. Use of the double-difference technique 

in this situation is limited to those sub-volumes producing similar raypaths back to the stations.

Figure D2. Raypaths between stations (red triangles) and event clusters (black dots) 

affect suitability of double-difference location scheme. (a) suitable array configuration, 

(b) unsuitable array configuration.

Thus, this technique is better suited to improving relative locations when the area of activity 

is small and unchanging in comparison to the scale of the array. Its application to a 

microseismic-scale network, as in the network on the right, is not as straightforward. Although it 

could be used to tighten up multiple clusters of activity—e.g., activity associated within limited 

portions of the longwall face, headgate, and tailgate—the positions of clusters relative to each 

other will still exhibit systematic mislocations. A calibration event would be needed to anchor 

the calculated location of each cluster group. Master-event location techniques work by 

examining differences in arrival times for pairs of events where one of the events is a calibration 

blast or other event of known location.

Boltz et al. [2014] used double differencing to account for the temporal-spatial variations in 

the velocity as a result of the gob formation as mining advanced in a single longwall panel. Prior 

to applying the double-difference technique, the depths of the events migrated toward the surface 

and located above the topography at the end of the panel in an unrealistic pattern. Tightly linking 

the events pulled the resulting event depths down to where they followed the approximate dip of 

the coal seam.
84
85

Glossary of Seismic Terms

Acceleration. The second derivative of displacement and common measure of ground motion.

Acoustic impedance. A property that describes the opposition of a material to acoustic wave 

transmission, which is determined by multiplying the density of the material and the velocity of 

the wave traveling through the material.

Apparent stress. A failure-model-independent estimate of the average stress state around a 

seismic source region.

Apparent volume. The volume of the failed rock mass near the source of a seismic event.

Arrival time. The time at which seismic energy of an event reaches a station.

Attenuation. The decrease in seismic energy intensity observed over time and distance. 

Averaged shear slip. The particle displacement averaged over a fault-slip failure area.

Back azimuth. The angle from north to a line originating at a station and terminating at a 

seismic event.

Body waves. The elastic energy radiated from a seismic event that does not require a freesurface to persist (i.e., travels through the interior of the earth). Consists of P-waves (pressure) 

and S-waves (shear).

Broadband seismometer. An instrument for recording seismic events that is able to adequately 

record a wide range of frequencies (e.g., 1/30 Hz to 100 Hz).

Coda-wave magnitude. A magnitude scale based on the duration of an event waveform. Larger 

events tend to produce longer duration waveforms for a given source-receiver distance.

Corner frequency. The frequency (in Hz) at which the spectra amplitudes of an observed 

waveform begin to drop off.

Critically refracted wave. A raypath with an incidence angle to a material boundary that will 

cause the wave to travel, as a head wave, along the boundary and eventually reflect to the 

surface.

Direct wave. The energy that travels from the seismic source to the receiver without reflection.

Displacement amplitude spectrum. Amplitudes as a function of frequency for seismic 

waveforms recorded as, or transformed to, displacement. 

District-wide network. A seismic network which monitors a group of mines (a district).

Double-difference location method. A multi-event relocation method that locates events 

relative to other events based on the differences in their travel times.

Energy index. The ratio of radiated seismic energy for a given event over the average radiated 

energy for events of comparable seismic moments.

Exploration seismic array. The typical instrumentation used for reflection and/or refraction 

surveys, which consists of several geophones connected via cables to an acquisition system that 

are deployed on a temporary basis.
86

Frequency range. The range of frequencies that are best recorded by a given seismic instrument.

Geophone. A common instrument for measuring ground motion (in velocity).

Head wave. Raypath that travels along the interface between two materials at the speed of the 

material with the faster seismic velocity.

Helicorder record. A plot of continuous data recorded by a single seismic instrument, which 

usually shows multiple hours of data. 

Hodogram. A two- or three-dimensional plot of ground motion over time.

Homogeneous isotropic velocity model. A seismic velocity model characterizing the velocity as 

a single value that does not vary based on the propagation direction of the seismic energy.

Incidence angle. Angle between a raypath and a direction perpendicular to the material into 

which the seismic energy is being transmitted.

Induced seismic event. A seismic event that is caused by human activity.

Local magnitude. A magnitude scale based on the amplitude of seismic energy at a station and 

its distance from the event. A subset of local magnitude is the Richter magnitude, which requires 

amplitudes to be measured on a real, or simulated, Wood-Anderson seismograph.

Low-pass filter. A filter for attenuating energy above the cutoff frequency.

Magnitude. A measure of the size of a seismic event, which can be based on a number of 

observed features of the event waveforms. Because there are several magnitude scales, care must 

be taken when comparing magnitudes calculated by different sources.

Microseismic event. A small, typically induced, seismic event—not to be confused with 

microseism, which refers to faint earth tremors caused by natural phenomena (e.g., ocean 

waves).

Microseismic network. A group of seismic instruments designed to record small, typically 

induced, events originating from a concentrated region. Generally, station density is much 

smaller than regional networks designed to monitor tectonic earthquakes.

Mine-wide network. A group of seismic instruments monitoring seismic activity at a single 

mine.

Mode conversion. A waveform phenomena wherein seismic energy is converted from one phase 

to another (e.g., P-waves to S-waves on a material boundary).

Moment magnitude. The generally preferred magnitude scale based on seismic moment, which 

relates to physical properties of the seismic source.

Particle-motion diagram. See Hodogram.

Particle velocity. The velocity (or speed) of a single particle in a media.

Polarization. The direction of a wave’s oscillation with respect to the wave’s direction of 

propagation.

P-wave. Pressure or compressional wave. Particle motion occurs in a direction parallel to the 

direction of wave propagation. Typically, the first phase that is observed in a waveform for a 

seismic event.
87

Radiated seismic energy. The seismic energy released by an event. Radiated seismic energy

only accounts for a small amount of the total energy released at the event source region (< 10%).

Raypath. The physical path taken by seismic energy traveling from the source to the receiver.

Reflection. A change of propagation direction for a raypath upon reaching a material boundary 

such that it returns to the material from which it originated.

Reflection coefficient. The ratio of the amplitudes of a reflected and an incident wave.

Refraction. A change of propagation direction for a raypath as it passes through a material 

boundary into a material with a different acoustic impedance.

Rockburst. A seismic event that causes damage to active mine workings.

Rupture area. The failed area at the source of a seismic event, often idealized with simple 

geometries.

Seismic efficiency. The percentage of energy released by a seismic event that is radiated by 

seismic waves.

Seismic event. A dynamic failure that radiates seismic energy.

Seismic moment. A quantity used to measure the size of an earthquake.

Seismic network. A group of instruments used to record ground motion induced by seismic 

events.

Seismic phase. A characterization of seismic energy based on particle motion and raypath.

Seismic velocity. A material property that describes the speed at which seismic energy travels 

through the material.

Seismometer. An instrument for recording ground motion caused by seismic events.

Short-period seismometer. A seismic instrument designed to record high-frequency energy 

(e.g., 1 to 100 Hz).

Signal-to-noise ratio. The ratio of the amplitude of a seismic signal to the amplitude of 

background noise.

Single-station location. A calculated location of a seismic event that is obtained using data from 

only one station.

Snell’s law. The law governing the bending of seismic (and other) waves traveling through 

material boundaries.

Sonic log. A log detailing the seismic velocity along a borehole perimeter.

Source dimension. The physical dimensions of a seismic source.

S-P time. The arrival time of the first observed S-wave minus the arrival time of the first 

observed P-wave. Can be used to estimate the source-receiver distance.

Station correction. An empirical correction applied to travel times at a seismic station to help 

account for un-modeled heterogeneity in the velocity structure.

Stress drop. The decrease in stress experienced around a seismic source region after an event 

occurs.
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Strong-motion seismometer. A seismic instrument designed to record high-amplitude ground 

motions without exceeding the instrument’s dynamic range.

Surface waves. Seismic waves that require a free surface to exist.

S-wave. Shear wave. Particle motion occurs in a direction perpendicular to the direction of wave 

propagation. Arrives at a station after the P-wave.

Travel time. The time required for seismic energy to travel from the source to a receiver.

Triaxial sensor. Seismic instrument with three perpendicular sensors that record ground motion. 

The sensors are often oriented in the vertical, north-south, and east-west directions.

Uniaxial sensor. Seismic instrument with a single sensor that records ground motion in a single 

direction.
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