.

A Correlation Between Seismic Tomography, Seismic Events and Support

Pressure 

E.C. Westman

Department of Mining and Minerals Engineering, Virginia Tech, Blacksburg, Virginia, USA

K.A. Heasley

National Institute of Occupational Safety and Health, Pittsburgh, Pennsylvania, USA

P.L. Swanson

National Institute of Occupational Safety and Health, Spokane, Washington, USA

S. Peterson

RAG American Coal Corporation, USA

ABSTRACT: Coal bumps are brittle, violent failures associated with high stresses and competent host strata.

To study bump mechanisms, conditions in the vicinity of a deep longwall mining face in bump-prone strata were

monitored using three discrete systems. First, a microseismic monitoring network recorded mining-induced

seismic events throughout the mine and surrounding strata. Second, pressure variations and distributions on the

longwall shield legs across the face were recorded. Last, tomography surveys imaged seismic transmission

properties ahead of the face. Results from the three systems were correlated in an effort to increase understanding

of mining-induced stress redistribution and bump potential. An initial, one-week study showed that the

correlation between averaged tomogram values and seismically active areas had a coefficient of correlation (R)

of 0.89. Further results of these studies demonstrate that the tomography system is capable of imaging heavy

shield-leg loading and bump-prone conditions prior to them disrupting the face operations.

INTRODUCTION

The Mine

A modern longwall coal mine is a highly mechanized,

capital intensive, system, typically producing more

than 10,000 tonnes of coal per day with as few as 10

to 15 people operating the face equipment. Any

capital-intensive operation must be continuously

utilized to optimize the investment. Further, the profit

margin for a longwall face is typically five times that

of a room and pillar face. Any loss in production time,

therefore, quickly translates into lost profits for the

mine. Coal bumps, comparable to rock bursts in noncoal mines, are localized seismic events which can

rapidly and forcefully eject many tons of coal into the

mine openings. These events not only disrupt the

mining process, they can also be extremely dangerous

to underground personnel.

The longwall coal mine at which the monitoring

occurred is located in the western United States. The

seam is under approximately 800 m of massive

competent overburden with a history of miningrelated seismicity. Three systems were used to

monitor the ground conditions at this underground

longwall mine: a microseismic monitoring network,

a seismic tomography system and a longwall shieldleg pressure recording system. Results from the

monitoring systems were correlated in an effort to

determine whether the diverse information could be

used synergistically to increase understanding of

mining-induced stress redistribution and bump

potential.

Bumps

Kusznir & Farmer (1983) defined a coal bump as a

brittle failure associated with high stresses and strong

rocks. Coal bumps have resulted in numerous

fatalities historically, and continue to cause fatal

accidents. In addition to the health and safety

concerns, economic losses due to lost production are

typically tens of thousands of dollars per day that a

longwall face is inactive. Numerous factors have

been stated to influence the occurrence of bumps,

including properties of the coal, geology (joints, folds,

faults,etc.), mining induced stresses, strong sandstone

beds in the roof, pillar size and shape, mining

technique, and mining rate (Holland 1958, Whittaker

1983, Iannacchione et al. 1987, Arabasz et al. 1997).

Haramy & McDonnell (1988) emphasized that the

energy released in a bump is from accumulated strain

energy in the coal, roof, or floor. Strain energy

accumulates in the strata as a result of deflection

under abutment stresses. The roof strata deflect either

by bridging or cantilevering. Failure of the coal is
termed a “pressure” bump, while failure of the roof is

termed a “shock” bump (Mark et al. 1999).

Microseismic Monitoring Network

Mine-scale seismic networks have been used for more

than 30 years to monitor location and magnitude of

mining-induced seismicity (Mendicki 1997). Young

(1992) reported results of a study which correlated

mining-induced seismicity and tomography at

underground hardrock mines. The relationship

between rock bursts, geology, and mine layout at an

underground silver mine was described by Swanson

(1995). Wilson & Kneisley (1995) discussed the

results of studies monitoring mining-induced

seismicity at four underground coal sites identifying

potential trends leading to a high risk for bump

events. The mine and the microseismic monitoring

network used in this study are more completely

described in Ellenberger et al. (2001).

Longwall Shields

The roof above a longwall face is supported by

numerous longwall shields. Each shield is typically

between 1.5 and 2 meters wide and essentially

consists of a base unit, hydraulic cylinders and a

canopy. The hydraulic cylinders (or legs) press the

canopy of the shield against the roof and control the

roof stability above the working area. Modern shields

can support up to 1000 tonnes of roof load. Hanna et

al. (1991) and Cox et al. (1995) report on a system for

monitoring roof stability conditions at a longwall face

by recording the fluid pressure in the shield legs.

Average leg pressures are reported and display

loading trends which can be attributed to changing

geologic and stress conditions at the face.

Tomography

Tomographic imaging was first described by Radon

(1917) who theorized that the interior of a body could

be imaged by analyzing energy which passed from

one boundary to another. This technology was

eventually adapted to the medical field (Hounsfield

1973, Cormack 1973) and to the geosciences (Dines

& Lytle 1979). Tomography has been used in the

mining industry for at least 20 years to image geologic

features as well as stress-related features (Buchanan

et al. 1981, Mason 1981, Kormendi et al. 1986). More

recently, the method has been adapted to image stress

concentrations ahead of a longwall face by a unique

application of the longwall mining equipment as the

seismic source (Westman et al. 1996).

METHODS AND PROCEDURES

In this paper, two data sets were analyzed, the first

consisted of seismic events and tomograms from one

week, representing approximately 90 m of face

advance. Analysis of the first data set was completed

during an on-site visit. Subsequently, a more

comprehensive analysis was completed with a second

data set. The second data set consisted of seismic

events, shield leg pressures, and tomograms from a

six-week time interval, representing approximately

240 m of face advance. The data manipulation and

analysis process was essentially the same for the two

data sets and will be described in the following

sections.

Seismic System

A three-dimensional,full-waveform, seismic network

with both surface and underground sensors was

established around the active mining panels. The

purpose of the array was to determine the timing,

exact location, magnitude, and mechanism (shear

fracture, bedding plane slip, etc.) of the failure of the

strata surrounding the mined panels. At the time of

the study described in this paper, the underground

array consisted of 13 geophones located about every

300 to 450 m around the active panel. The surface

seismic array consisted of nine geophones

strategically located above the longwall panel. In the

final seismic dataset, only the events with a minimum

of 8 total stations (with at least 3 surface stations and

3 underground stations) reporting good first-arrival

picks were kept in the database. Figure 1 shows the

location of the surface and underground geophones,

as well as the location of the longwall face during

acquisition of the two data sets.

Arabasz et al. (1997) reported that mining

seismicity is well correlated to the rate of advance of

the mining face. An comparison between the

seismicity rate and face advance rate was performed

for all seismic events within 60 m of the face and is

presented in Figure 2 which clearly shows a strong

correlation between rate of face advance and level of

seismic activity. For correlating the seismic data with

the shield leg pressures and the tomography output,

the density of the seismicity was determined on a 7.5

by 7.5 m grid by calculating the number of events

occurring per unit area in the plane of the seam.

Shield Legs 

Shield leg pressure data were gathered by using

pressure transducers attached to the legs of each

longwall shield. The transducers monitored the

hydraulic pressure in the shield legs at five- to eight-
Figure 1. Location of surface (triangle) and underground (circle)

seismic sensors and face locations during acquisition of two data

sets.

Figure 2. Mining rate and seismic event rate, for time period of February 14-21, 2000

minute intervals. These data were then reduced by

averaging to create a grid with 7.5m cell sizes so that

all the shield leg pressures as the face advanced over

7.5 m were averaged and adjacent shields were

additionally averaged to create a pressure value for

each 7.5 by 7.5-m cell. 

Tomography

Mine personnel generated three to four tomograms

each day. The longwall shearer was used as the source

of seismic energy and geophones mounted on

roofbolts in the headgate and tailgate were the

receivers. Because the receivers were mounted on

roofbolts, the tomograms are representative of

conditions in the strata above the coal seam. For this

analysis the tomograms were reduced to their raw

transmission values broken into 7.5 by 7.5 m grids.

Average tomograms were then calculated. This

averaging results in a reduction in the visibility of

stress-related features; however, the clarity of

geologic anomalies, which are stationary in their

location, is emphasized. Therefore, the average

tomograms are expected to display geologic features

across the panel, especially in the roof. The average

tomograms were calculated in 7.5 m grids to allow

correlation with the other monitored data.

RESULTS

One-Week Data Set

Data collected as the face advanced 90 m during a

one-week period in February, 2000 were analyzed

initially. The goal of the analysis was to determine

whether similar trends were present within both the

seismic data set and the averaged tomograms. To

accomplish this goal, the average transmission levels

for the tomograms were summed for all cells at a

given distance along the tailgate (in 7.5 m strips

parallel to the longwall face). These results are shown
Figure 4. Number of seismic events and transmission measurement, average of all values along panel for locations along the

face.

Figure 3. Number of seismic events and transmission measurement, average of all values across panel for locations along

the tailgate

plotted against the number of seismic events

occurring in the same strip and show that both

increased seismic activity and higher tomography

transmission values occur towards the beginning of

the study period (Fig. 3). A good correlation is

observed, and a correlation coefficient (R) of 0.77

was calculated. A similar calculation was done for

locations across the face (in 7.5 m strips parallel to

the face advance) with the results shown in Figure 4

and an R of 0.62.

To smooth the trends even more, a five-point

running average of both of these calculations was

determined and is also shown in Figures 3 and 4. An

R of 0.94 was found for tailgate locations and an R of

0.89 was found for face locations using the running

average trends. The high transmission (high stress)

areas indicated by the tomograms correlate very well

with the most seismically active areas indicated by the

microseismic system. During this week, the high

stress/high activity area was in the first days of the

week and between shields 20 and 55.

Six-Week Data Set

Based on the good correlations found in analyzing the

seismicity and tomography from the one week time

period, another analysis using identical procedures

was performed on a larger dataset spanning a six
Figure 5. Seismic event location and density for six-week

data set. Panel width is 160 m.

Figure 7 Average tomographic transmission for six-week data

set. Lighter areas indicated relatively higher transmission than

darker areas. Panel width is 160 m

Figure 6. Shield-leg pressure distribution for six-week data set.

Lighter areas indicated relatively higher loading than darker

areas. Panel width is 160 m.

week period. In addition to the seismicity and

tomography, the shield-leg pressure data were also

included in this larger analysis. The data for this

analysis were collected as the face advanced from

Station 2100 (February 17, 2000) to Station 1300

(March 30, 2000), a distance of 240 m. The results of

this analysis are shown in the following figures:

seismic events and event density are shown in Figure

5, the shield-leg pressure distribution is shown in

Figure 6, and the average tomographic transmission is

shown in Figure 7. Linear regression was used to

correlate the data from the various source pairs and a

coefficient of correlation was calculated. Plots of the

correlations are as shown in Figures 8-10.

The strongest correlation was between shield-leg

pressure and the average tomogram, which produced

an R of 0.60 (Fig. 10). The correlation between

seismicity and shield-leg pressures was 0.41, and that

between seismicity and average tomogram was 0.20

(Figs. 8, 9). When the data were smoothed by

averaging strips parallel to the face as with the oneweek data set, an R of only 0.33 was obtained.

However, when averaging the data in strips

perpendicular to the face an R of 0.92 was obtained.

DISCUSSION AND CONCLUSIONS

The coefficient of correlation relating various results

of the three monitoring systems ranges from 0.20 to 

0.94. A potential explanation for the correlation is the

influence on the three monitoring systems of the roof

geology. Thicker, stronger sandstone layers in the 
Figure 9. Correlation between seismic event density and average tomogram transmission for six-week data set.

Figure 8. Correlation between seismic event density and average shield leg pressure for six-week data set.
Figure 10. Correlation between average shield leg pressure and average tomogram transmission for six-week data set.

immediate roof appear on the tomograms as higher

transmission levels than thinner, weaker beds. These

thicker layers load the longwall shields more than

thinner layers because the stronger layers tend to

cantilever further over the shields before collapsing

into the gob. Further, these more competent layers fail

at higher stresses and generate more and/or larger

seismic events in the abutment zone.

Regardless of the cause of the correlation, the fact

that a correlation is observed is beneficial to the

mining community. By implementing the tomography

system, an indication of potential seismicity and

shield-leg loading can be obtained for the region in

advance of the mining face. If heavy loading or

seismicity is projected, appropriate plans can be

made, such as adjusting mining rates or minimizing

personnel exposure until mining through the zone of

concern is complete.

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