Local Earthquake Tomography for Imaging Mining-Induced Changes 

Within the Overburden above a Longwall Mine 

Westman, E.C. and Luxbacher, K.D. 

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

Swanson, P.L.

National Institute for Occupational Safety and Health-Spokane Research Laboratory, Spokane, WA, USA

ABSTRACT: Three-dimensional velocity tomograms were generated on a daily basis to image mining-induced changes to the 

overburden above a longwall mine. The hypothesis was that a coherent redistribution of seismic velocity, due to the development 

of high-stress zones, could be imaged at the mine scale. Seam depth was 360 m and source location depth varied from 100 to 1000 

m. Sixteen geophones were distributed over a 600 by 600 m square area on the surface above the mine. More than 12,500 events 

were recorded over an 18 day period. The recorded seismicity provided input for the local-earthquake tomography code, 

SIMULPS. Eighteen tomograms were generated and high-velocity regions correlated well with high abutment stresses. 

Additionally, the high-velocity regions were observed to redistribute as the longwall face retreated. These results indicate that 

velocity tomography can be used to provide a better understanding of temporal changes within a rock mass, and can potentially be 

used to produce a better understanding of the mechanisms that lead to unanticipated ground failures.

1. INTRODUCTION 

The ability to forecast failure of rock is an ultimate goal 

of the geo-engineering field. Failure within a rock mass 

is often a poorly understood phenomenon which can 

have severe consequences, even where best-design 

practices are employed. Failure within a rock mass is 

currently predicted by comparing values of stress and 

strength based on estimated material properties. 

Frequently, numerical models are used to represent the 

rock structure by dividing it into elements and summing 

the behavior of the elements. Each element relates 

applied stresses to strains through a constitutive 

equation. Different material properties can be assigned 

to various portions of the model. However, the correct 

estimate of the necessary material properties is a key 

problem with numerical modeling of rock structures; 

without accurate estimates of the material properties, the 

changes induced by loading of a rock mass are 

inaccurate. 

A potential remedy to this problem is to perform highresolution imaging to monitor the true alteration of a 

rock mass as it is loaded. Because rock properties 

change under increased loads, areas of high stress can be 

identified using elastic waves [1,2,3]. Elastic waves 

include ultrasonic waves, typically used in the laboratory 

[4], and seismic waves, common in field studies [5,6,7]. 

These waves can be used to nondestructively image the 

interior of rock masses and determine changes to loading 

of the rock mass [8]. One method used to perform this 

imaging is tomography, which is a rapidly advancing 

method for imaging the interior of bodies, including rock 

masses [9,10,11]. 

Tomography has been used at the field scale as long ago 

as 20 years in the mining industry to image geologic 

features as well as stress-related features [12,13,14]. It 

was not until the last 15 years, however, that it has had 

ready acceptance within the geosciences for petroleum 

reservoir characterization and for geotechnical 

applications. 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 [15,16]. 

The objective of this study was to determine whether a 

qualitative but coherent redistribution of velocity, 

indicating change in location of induced stress, could be 

imaged as mining occurred in known locations by using 

a local earthquake tomography software code. Local 

earthquake tomography uses the many naturallyoccurring or mining-induced seismic events as sources 

to both generate a tomographic image of the velocity 

 

 
distribution within the rock mass and relocate the 

seismic events. 

2.0 BACKGROUND 

Tomographic imaging was first described by Radon [17] 

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 [18,19] and to the 

geosciences [20]. It has been shown in the lab that 

changing stress concentrations in rock can be imaged 

with tomography [21-24]. Scott et al. [25,26,27] 

indented cylindrical rock samples and imaged the 

sample as stress was increased. The tomograms showed 

a clear increase in ultrasonic wave velocity directly 

beneath the load. 

Information about the actual deformation processes in 

the rock mass can be obtained by monitoring the 

propagation of elastic waves [28-31]. These methods are 

divided into two groups, passive and active. Passive 

methods listen to the emissions generated during 

localized failure within the rock mass, for example 

microseisms and acoustic emissions [32]. With active 

methods a signal is generated on one boundary of the 

sample (e.g. with a hammer blow or explosive charge) 

and acquired on another, following a pitch-and-catch 

method [33-36]. 

Local earthquake tomography is a passive method that 

has not previously been used specifically for studying 

mining-related stress redistribution. With this method 

initial event locations are used to generate a tomogram. 

This three-dimensional velocity model is an 

improvement to the initial one-dimensional (“layer 

cake”) model used to initially locate the events, thus the 

events can be relocated more accurately. The process is 

iterated until the difference between the measured and 

calculated travel times is at an acceptable level. The 

SIMULPS local earthquake tomography code was 

developed based on Thurber‟s damped-least squares, full 

matrix inversion and allows curved rays [37-40]. 

Hudson [41] lists eleven different terms used to describe 

stress within a rock mass. Within this paper, the term 

„stress‟ will refer to „induced stress‟ in which the natural 

state of stress has been altered by human activity. 

3.0 STUDY SITE 

Data for this study were collected between July 7, 1997 

and August 8, 1997, at an underground coal mine in the 

western United States. The mine employs longwall 

mining, and the coal seam ranges in thickness from 2.6 

to 3.0m with a depth of approximately 360m. The 

operation mines longwall panels that are approximately 

5500m long and 250m wide. Over the course of the 

study, the face advanced 431m, averaging about 24m per 

day. Sixteen geophones were assembled on the surface 

to monitor and locate microseismic events. The 

geophones provided adequate spatial coverage of the 

entire area of study. 

A unique feature of the mining area was that three 

entries were cut through the panel to provide an alternate 

escapeway due to the length of the panels. Shortly 

before mining through, the cross-panel entries were 

backfilled with light-weight cement. This cement 

carried only a portion of the induced stress because its 

stiffness was less than the pillars. The backfilled entries, 

cross-panel pillars and the panel were instrumented. 

Analysis of this data showed that nothing unusual 

occurred as they mined through the backfill [42]. 

4.0 DATA COLLECTION AND ANALYSIS 

Seismic data were collected at the site over an 18 day 

period. As mining progressed, the void left by removal 

of the ore created a stress concentration ahead of the 

mining face, resulting in higher stresses and mininginduced seismicity as the rock failed. An individual 

seismic event was logged to the database if it triggered at 

least ten geophones. The events typically had a 

frequency around 30 Hz. The number of events 

recorded per day was related to the amount of mining 

that was done and varied from about 100 to more than 

800 events per day, with a mean of 650. 

Input for tomography consists of many sets of the 

combination of receiver location, source location and 

travel time of the seismic wave between the source and 

receiver. The receiver locations were surveyed using the 

same coordinate system as the mine. The source 

locations were based on an automated arrival time 

determination and a one-dimensional velocity model, as 

described in Luxbacher et al. [43]. 

Several parameters within the SIMULPS program are 

important to note. The program allowed a grid of voxels 

30m on each side. Event locations were adjusted a 

maximum of 15m per iteration with a maximum of 20 

iterations per event to minimize the residual between 

measured and calculated travel times. A damping value 

of 500 was selected empirically following the procedure 

recommended by Evans et al. [37]. Damping values that 

are too low result in a highly variant, „noisy‟ model 

while too much damping reduces the ability of the model 
to fit the data well. The final velocity model was 

compared to the initial velocity model in MATLAB and 

the difference plotted using the default trilinear 

interpolation. A voxel size of 15m was used in 

MATLAB to smooth the images. 

5.0 RESULTS AND DISCUSSION 

Six plan-view tomograms at seam level are shown as 

Figure 1, including the face location and the related mine 

geometry. Two features are clearly apparent. One is 

that a high-velocity zone, indicating high stress, extends 

along the tailgate pillar area. The other is that as the face 

advances, a parallel high-velocity zone ahead of the face 

also advances. Figure 1 shows this zone to lose its 

coherency as the face approaches and passes through the 

backfilled entries (Figure 1a-d) and is then clearly 

reestablished in subsequent days (Figure 1e,f). 

Results of the study include event relocation as well as 

the tomograms. Histograms of initial event locations 

and the relocated events (Figure 2) show that the 

locations based on the SIMULPS-generated velocity 

model are closer to the seam, as indicated by the 

narrowing of the depth distribution. This is as would be 

expected and is attributed to the improved velocity 

model used for event location. 

There are important parameters that must be clearly 

understood when applying tomographic methods in the 

field. The resolution of the tomographic image is a 

function of several factors. The number of sources and 

receivers used in the study determines the number of 

raypaths traversing the body, influencing the resolution 

of the image. The number of sensors is determined, in 

part, by the size of the body and the data acquisition 

system capabilities. The frequency characteristics of the 

elastic waves also influence the resolution with which 

features can be identified. Laboratory studies are 

typically conducted using ultrasonic waves with 

frequencies near 1 MHz, resulting in wavelengths of 

approximately 0.4 cm (assuming propagation velocity of 

3500 m/s). Large-scale field studies, on the other hand, 

must use lower frequency waves (typically < 200 Hz) to 

allow greater propagation distances. These waves in the 

same material have a wavelength of nearly 18 m. 

Because features will only be well resolved if they are of 

diameter greater than one-half the wavelength, the 

difference in observable features is substantial. Given an 

average velocity of 3600 m/sec and a typical frequency 

of 30 Hz, a wavelength of 120 m is typical for the 

dataset. 

Major stress features resulting from abutment stress 

were expected to be imaged through velocity 

tomography. Abutment stress is a result of stress 

redistribution due to the extraction of ore, and occurs 

along or near the boundary where material has been 

removed [44]. An undisturbed coal seam with competent 

roof and floor strata will have a fairly uniform stress 

distribution. As coal is removed this distribution is 

disrupted and the load shifts to another intact area. In 

longwall mining, this stress is transferred immediately in 

front of the face, and to the sides of the panel (headgate 

and tailgate). Failure of the roof strata behind the 

longwall shields is termed the „gob‟ and allows for 

pressure relief. Very competent strata above a longwall 

system, such as massive sandstone, may not cave 

immediately, contributing to extremely high abutment 

stress in front of the face which can result in rockbursts 

at the face, and damage to shields due to dynamic 

loading [45]. The exact distribution of the abutment load 

is dependent upon the properties of the roof strata and 

the mining geometry, but abutment stress is usually the 

largest on the tailgate, if it is adjacent to a previously 

mined out panel. Front abutment pressure is detectable at 

a lateral distance ahead of the face approximately equal 

to the overburden depth, and typically reaches a 

maximum at a distance of one-tenth the overburden 

depth [45]. In addition to vertical stress redistribution, 

joints, faults, inhomogeneous layering, and horizontal 

stress orientation may contribute to larger abutment 

stresses and more erratic failure. Even in optimum 

conditions roof failure behind longwall shields is rarely 

uniform [46]. 

The relationship between induced stress and velocity is 

not monotonic and is complicated by confinement. In 

uniaxial compression the velocity of an elastic wave 

traveling through the sample increases as existing 

microfractures are closed. At some point on the stressstrain curve new microfractures are formed which 

eventually coalesce into the failure plane. The point at 

which these new microfractures begin forming varies 

with lithology, but is generally between 50 and 90 

percent of the ultimate failure strength. As these new 

microfractures are formed the elastic wave travels more 

slowly. Few laboratory studies have been conducted to 

evaluate the effect of confinement, however it is likely 

that because the ultimate strength of the sample is 

increased, the stress at which the new microfractures are 

formed is also increased and the behavior is similar to 

that of uniaxial compression. 

In this study, as the longwall face retreated down the 

panel, high stress zones were formed where the 
Velocity (m/s)

4260

4240

4220

N

100m

Velocity (m/s)

4260

4240

4220

N

100m

d) Velocity (m/s)

4260

4240

4220

N

100m

e) Velocity (m/s)

4260

4240

4220

N

100m

f)

Figure 1. Tomogram near seam level for a) Day 4, b) Day 6, c) Day 8, d) Day 12, e) Day16 and f) Day 18. Geophone locations on 

earth‟s surface are shown as filled triangles. The mining face for the given day is denoted by the solid black line. Note that as the 

face advances, a parallel high-velocity zone ahead of the face also advances. This zone loses its coherency as the face approaches 

and passes through the backfilled entries (Figure 1a-d) and is then clearly reestablished in subsequent days (Figure 1e,f). 

a

Velocity (m/s)

4260

4240

4220

N

100m

b) Velocity (m/s)

4260

4240

4220

N

100m

c)
supporting coal was removed. This condition was 

especially met along the mining face, which had a newly 

formed void on one side of it, and along the pillars in the 

tailgate, which had void on either side. 

0

5

10

15

20

25

0 150 250 350 450 550 650 750 850 950

Percent of Events (%)

Depth (m)

Initial Location Simulps Relocation

>

Figure 2. Histogram showing depths of seismic events as 

initially located and as relocated using SIMULPS. Seam is at 

a depth of 360 m. Narrowing of the distribution of depths is 

attributed to the improved velocity model used for event 

location.

6.0 CONCLUSIONS 

Several conclusions can be drawn from this study. The 

first is that local earthquake tomography can be used to 

generate a reasonable, coherent redistribution of velocity 

indicative of and correlated with, stress redistribution 

due to mining. Even with the sub-optimal arrangement 

of receivers located only on the earth‟s surface well 

above mining-induced seismicity, a high-velocity zone 

was consistently shown to move with the mining face. 

This high velocity feature indicates the location of the 

forward abutment stress which moves with the face. 

The second conclusion is that improved seismic event 

locations are obtained through the use of local 

earthquake tomography. Initially, the events were 

located based on a velocity model that increased 

monotonically with depth, but did not vary laterally. By 

generating a more accurate velocity model the events 

were relocated closer to the seam that was being mined. 

Several improvements can be made in the future. The 

data set would be improved by including sensors at the 

seam level and also by daily observation of conditions at 

the mining face. Future research must also include 

correlating a tomogram to seismic energy generated at a 

subsequent time, with the goal being a tool for 

forecasting regions of elevated seismic activity. The 

ultimate goal for the mining engineering community is 

the increased safety of underground miners. 

ACKNOWLEDGEMENTS 

The authors are grateful to the National Institute for 

Occupational Safety and Health for providing the raw 

data for this study. This research was funded by a 

National Science Foundation CAREER Grant (CMS0134034). 

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