1

 Proceedings of the 5th International Symposium on Rockbursts and Seismicity in Mines,

edited by G. van Aswegen, R.J. Durrheim, and W.D. Orlepp (Johannesburg, S. Africa,

Sept. 17-19, 2001). S. African Institute of Mining and Metallurgy, Johannesburg, S.

Africa, 2001, pp. 11-17

DEVELOPMENT OF AN AUTOMATED PC-NETWORK-BASED SEISMIC

MONITORING SYSTEM1

Peter L. Swanson, geophysicist

National Institute for Occupational Safety and Health

Spokane Research Laboratory

315 E. Montgomery Ave.

Spokane, WA 99207

USA

(voice) 509-354-8072

(fax) 509-354-8099

(email) pswanson@cdc.gov
2

Synopsis

NIOSH has developed an automated PC-based seismic monitoring system for

use in mine ground control studies. The distribution and coordination of data

acquisition and processing functions across a local area network (LAN) is a

central feature of the system. Design requirements include (1) the use of lowcost, off-the-shelf, data acquisition and computer hardware, (2) the capability of

automatically merging waveform data sets from different types of monitoring

systems, (3) delivery of raw and processed data underground and on the

surface, as well as to remote sites, and (4) automated data collection and

processing over long periods of time without the need for human intervention.

Current mine-wide installations utilize multiple, autonomous seismic networks

located both underground and on the surface and provide 20 to 40 stations

within a radius of several kilometers. Event locations, magnitude estimates,

and other processed data are distributed for display and analysis on network

nodes both underground and in the offices of mine engineers and managers.

This paper describes the system=s hardware and software components, examples

of installations in hard-rock and coal mines, and a few observations from

western U.S. longwall coal mines.

Introduction

The U.S. Bureau of Mines (USBM) was eliminated as a federal agency by the

U.S. Congress in 1996. The continuing Congressional mandate for health and

safety research in mining was then transferred to the National Institute for

Occupational Safety and Health (NIOSH) under the Centers for Disease Control

and Prevention (CDC). NIOSH=s research laboratories in Spokane,

Washington, and Pittsburgh, Pennsylvania, conduct much of its mine safety

research.

Seismic monitoring capabilities were required in several NIOSH studies in

which the reduction of hazards from rock mass instabilities, such as rock

bursts, coal bumps, mine collapse, and roof falls, was the primary objective.

However, most of the USBM seismic monitoring hardware, software, and

expertise did not make the transition to NIOSH. Therefore, an automated

seismic monitoring system was developed for use in these ground control

studies. 

Design philosophy

Several technical and nontechnical issues guided system design. The expense

of hardware and software, and expected costs of field installation, maintenance,

and operation were of prime concern. Thus, the initial emphasis was in
3

developing several inexpensive automated systems to detect and locate events

and estimate magnitudes. Automation of full-waveform inversions and related

analyses1,2 was not part of the original objective, although this functionality can

be added. This latter approach was not emphasized in the initial mine-wide

monitoring program in part because of the expense of obtaining clear, highquality signals. Installation of sensors in boreholes is generally required to

obtain signals that are not influenced by the fractured halo surrounding mine

openings and the surface weathered zone. This is a prohibitively expensive

option for the current project work in the deep mines of the western United

States. As a result, a certain amount of signal filtering by the rock mass is to

be expected in the applications.

Modest-sized arrays of single-axis sensors were selected over networks made

up of a smaller number of triaxial stations. This choice was made for the

simplicity of automating P-wave arrival picking. Accuracy and consistency in

shear-wave arrival picking is much more difficult to achieve using an automated

procedure, especially when signals are attenuated because of near-surface

effects. While shear-wave arrivals provide strong constraints on locating

events, an overriding goal is to establish event locations as accurately as

possible with fully automated processing. Errors in shear-wave arrival picks

can compromise the accuracy of an event location.

In addition to low cost, reliability and flexibility were important considerations.

The expense and downtime associated with timely repair and/or replacement of

custom data acquisition and processing hardware, as experienced with some

previous USBM systems, was to be avoided. Mass-produced PCs and

commercial multichannel analog-to-digital (A/D) converters are very reliable.

A centralized data collection system was also to be avoided to minimize the

chance of having a single hardware failure bring about total loss of monitoring

coverage. A system of distributed data acquisition modules avoids single-point

failures and allows flexibility for network expansions and for configuring

systems to monitor complex or difficult-to-access ore bodies. Distributed data

sources may include multiple autonomous data acquisition systems operating in

different parts of a mine or mining district, surface and underground networks,

borehole arrays, portable instruments, etc. The need to deliver summary

processed results to mine personnel stationed underground was an additional

constraint. These design requirements are most readily satisfied with a PCnetwork-based system of data acquisition and processing computers (Figure 1).

To take advantage of as much freely available software as possible, the seismic

data acquisition and processing software made available through IASPEI

(International Association of Seismology and Physics of the Earth=s Interior)
4

was selected to serve as the initial basic building block for data acquisition.

IASPEI is a nonprofit geophysics organization that produces software modules

for low-cost regional and teleseismic earthquake monitoring3 Several

centralized IASPEI PC-based systems were deployed by USBM researchers4 for

data acquisition in the late 

Figure 1. PC-network-based seismic acquisition and processing system

1980s, and updated versions of these systems are still in operation in several

western U.S. mines. A common thread to much of the IASPEI software is the

use of the PC-SUDS (Seismic Unified Data System) data format based on C

language data structures5,6. While providing a starting point for collecting

mine-wide seismic data, much of the IASPEI processing software is geared

toward larger-scale networks and is not directly applicable to smaller-scale

arrays.

Hardware

Sensors

Both accelerometers and moving-coil geophones are used in the sensor arrays.

Low- noise, high-sensitivity (40 V/g) accelerometers (3 to 3000 Hz) were

selected for one hard-rock environment to detect small seismic events occurring

within dense localized arrays. In other array deployments, use is made of
5

inexpensive 4.5-Hz geophones mass-produced for the oil industry. The

geophone signals are fed into variable gain (1 to 50,000 X) preamplifiers and

transmitted on shielded, twisted-pair cables. 

Sensors are anchored directly to competent roof or rib surfaces or epoxied to

the end of tensioned roof bolts. On the surface above the mine most geophone

sensors are placed in waterproof housings in the soil bottom of 30- to 40-cmdeep holes.

Data acquisition

Two different types of digitizing systems are used. With the first kind, seismic

signals are transmitted via cable to a commercial off-the-shelf A/D converter

attached to a local data acquisition PC. In the second type, seismic signals are

digitized near the sensors, and the data are transmitted digitally to a data

acquisition PC on the network. In each case, the data acquisition PCs are

dedicated solely to collecting waveform files either continuously or in a

triggered event-capture mode.

A/D converter attached to network computer

Commercially available PC-based A/D conversion boards used in this

development effort are listed in Table I. Typical sampling rates are between

500 and 2500 samples per second per channel for systems having 8 to 64

channels. The maximum sampling rates are not listed as they can depend upon

the number of active channels, processor speed, and the selected operating

system. Nominal resolution values reported in Table I were given by the

manufacturers for a particular sampling rate; actual resolution can be

significantly reduced at maximum sampling rates. Antialias filtering is

provided by commercial and/or custom programmable filter units.

Table I. A/D converter hardware

Manufacturer and model Number of channels Nominal resolution

(bits)

Interface

Symmetric Research:

 Par4ch 4 24 Parallel

 Par24b 8 24 Parallel

 DSPA64 64 16 ISA

Data Translation:

 DT2824 16 12 ISA

 DT-EZ 16 11 ISA

 DT2821 16 12 ISA
6

2 Mention of specific products or manufacturers does not imply endorsement by the National

Institute for Occupational Safety and Health.

The IASPEI data acquisition software XRTP was developed for Data

Translation2

 boards and requires the DOS operating system. The RTPSR

(TottCo) software for the Symmetric Research DSPA64 A/D converter is

similar. Data files are output directly in PC-SUDS format in both programs.

Event detection is based upon an STA/LTA algorithm similar to that reported in

Lee and Stewart7

.

The Symmetric Research boards come with data acquisition software that can

be used under DOS, in a DOS window under Windows95/NT, and with the

Linux operating system. Executable and source code are provided for each

operating system so that the user can implement his/her own triggering criteria

and make other customizations. Each version has been implemented in different

applications of the monitoring system.

Remote A/D conversion and transmission of digital seismic data

Low-power, three-channel 16-bit A/D converters are used to digitize signals at

or near remote sensors. Maximum sampling rate is 1000 samples per second

per channel. Data are continuously sampled and transmitted back to a data

acquisition computer via a cable or wireless telemetry link using the U. S.

Geological Survey=s RS-232 digital telemetry format3. In wireless links, data

are telemetered over distances of several kilometers using pairs of 900-MHz

and/or 2.4-GHz, frequency-hopping spread-spectrum radio transceivers

(license-free operation in the United States). Directional 6-dB Yagi antennas

are employed over line-of-sight paths without repeaters. The data are received

by a network PC equipped with a 16-channel serial port board using a version

of the XRTPDB software3 that was modified to accept sampling rates of up to

1600 samples per second per channel.

Several commercial seismic and strong-motion data recorders used for

earthquake monitoring also provide a compatible RS-232 output stream that can

be utilized in this data collection procedure. However, the maximum sampling

rates in these units only extend to a few hundred samples per second per

channel.

Time-base synchronization

Each data acquisition system records a common timing signal as a data channel.

Time signals are subsequently decoded, and each waveform trace is time

corrected. The synchronization signal is typically an analog IRIG time code
7

output commonly available on timing products manufactured in the U.S. Either

IRIG-E or IRIG-B codes with 10- and 1-s frames, respectively, are used. The

time codes are derived from global positioning system (GPS) and/or GOES

satellite receiver data or are generated as a stand-alone time code in closed

systems. Analog time code signals are transmitted to data acquisition systems

located underground over copper or fiber-optic cables.

Software

Networking

Several different types of networking and operating system software were

examined during system development. As the initial data acquisition systems

were restricted to DOS, peer-to-peer networking to the data processing

platforms was achieved using the NetBEUI protocol available in Workgroups

Add-On for DOS. TCP/IP protocol is used to connect to Linux-based data

acquisition PCs. The main processing PCs use either Windows95 or NT

operating systems, allowing them to network to all of the data acquisition

platforms tested.

Use of mining companies= general-purpose LANs for seismic data acquisition

and processing has met with mixed success. If there is no one on site to tend to

the monitoring system on a regular basis, a stand-alone, or isolated, LAN is

preferred. This avoids problems associated with many diverse users, corporate

firewalls, and other security issues. If the corporate system administrator is onsite and attuned to the needs of the seismic monitoring system, problems can be

minimized. By isolating the data acquisition nodes onto a separate LAN, these

problems can be largely limited to periodic temporary disruption of the

automated data processing. 

Data processing

A sentinel program watches for the creation of event-triggered and/or

continuously recorded data files on the different data acquisition nodes (Figure

2). The program controls the flow of raw and processed data, waveform data

file merging, and data processing across the network. Any source of seismic

data can be utilized providing it resides on the LAN and can be converted to

PC-SUDS format. Upon receipt of a file at a data processing node, a series of

batch files is executed to carry out the data processing. Both the data

acquisition and processing are controlled through user- settable parameters in

ASCII text files. As shown in Figure 2, the initial batch file archives the raw

data, decodes the time code and corrects the timebase, makes node-specific
8

timing adjustments, then demultiplexes the data, and performs any other nodespecific processing. The data are then brought into a buffer that holds timestamped data from the other data acquisition nodes. Time-correlated data are

then merged into a single SUDS files and sent on to further downstream

processing. Typical downstream processing options are shown in the flowchart

of Figure 2.

Several different event location methods, which allow some flexibility in

dealing with different array installations, velocity structures, and processing

requirements, have been used in the automated processing. These include

direct, iterative (gradient), and simplex solution methods8,9. Event locations are

continuously updated on a user-interactive graphical display and exported for

additional downstream processing by other user groups. The modular nature of

the PC-SUDS utilities and the flexibility inherent in batch processing gives

significant user control over the data processing.

Figure 2. Flow of data acquisition and processing

Example installations

Galena Mine

A prototype of the automated monitoring system was advanced in a joint project
9

with Silver Valley Resources, Inc., at the Galena Mine, Wallace, Idaho. As

with most deep mines in the Coeur d=Alene district, rock bursting is prevalent

in veins penetrating strong, brittle quartzites. Approximately 70 veins with as

many as 25 working stopes are distributed over lateral and vertical distances of

2.5 and 0.8 km, respectively. The design goal was to provide processed

seismic data anywhere on the LAN so that (1) supervisory and safety personnel

could view seismic activity in the mine while they were still underground and

(2) engineering and management personnel on the surface could use the data to

facilitate both day-to-day operations and longer-term planning. While the initial

design called for three separate data acquisition systems to be centered in three

major, widely separated mining areas, the eventual configuration has one minewide array and one smaller array that is moved to cover active stope areas of

current special interest. A fiber-optic network connects the two data acquisition

nodes to each other and to the surface and underground data processing PCs

(Figure 3). Processed seismic data are imported into the mine=s graphics

modeling software and a database program for automatic report generation.

Figure 3. Galena Mine network

Willow Creek Mine

A combined surface and underground monitoring system is deployed at the
10

Willow Creek mine in Helper, Utah (Figure 4), in an investigation into the

mechanics of coal bumps in multipanel longwall sequences10. At the level of

the mining operation, 12 roof-mounted and two borehole geophones are

deployed in the mains and bleeders around the first few panels of a longwall

district. Signals are fed by cable to a data acquisition PC housed in an

underground trailer. A network connection to the processing workstation in the

engineering office is established via fiber-optic cable. Signals from nine

surface stations above the longwall panels are sampled continuously with

remote digitizers and transmitted to a data acquisition node on the LAN via

three 900-MHz, spread-spectrum transceiver pairs. Data from several isolated

surface stations are also occasionally merged with data from the main networks

in post-collection processing and analysis. A second processing and display

workstation is located in the shift bosses= meeting room.

Figure 4. Willow Creek Mine network

Example results

Waveforms typical of those observed with this system in western U.S. longwall

coal mines are shown in Figure 5. The large signal-to-noise ratio of the P-wave

first arrivals and the overall event-to-event similarity in waveform character

allow consistent accurate automatic picks. 
11

Example event locations are shown in Figures 6 and 7. The processing

procedures performed for each plot were completely software automated

without any human interaction. Data in Figure 6 were acquired from a

temporary network of geophones deployed on the surface above a longwall

panel at the Foidel Creek Mine near Oak Creek, Colorado. The 600-mdiameter array was 365 m above the coal seam. No underground stations were

used in this deployment. Data shown were collected in one 24-hour period as

the 262-m-wide face advanced 34 m from right to left (Figure 6). The

regularity in fracture and deformation processes associated with each cut of the

longwall face is reflected in a similar regularity in the temporal and spatial

patterns of seismicity. The cross-panel entries through this 5.5-km long panel

had been filled with a lightweight foam cement prior to 

Figure 5. Example waveforms from western U.S. longwall coal mine
12

Figure 6. Seismic activity during 34-m longwall face advance at Foidel Creek Mine

Figure 7. Seismicity observed during extraction of Willow Creek longwall panel

mining through them. Significant changes in the patterns of seismicity were not

discernible as the face passed through the backfill, consistent with the

uniformity observed in the rock mechanics data. As many as 2000 events per

day were detected with this surface array, yielding a detection sensitivity

similar to that observed with panel-wide underground networks. The

automated processing procedures implemented in the field resulted in 500 to

1000 well-located events per day.

Seismicity associated with the extraction of a 1.2-km long portion of a longwall

coal panel at the Willow Creek Mine is shown in Figure 7. Depth of cover

over this panel exceeds 800 m and is highly variable because of the rugged

canyon-mesa topography. Approximately 13,000 events were detected and
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located during 8 months of production. Average source to receiver distance is

1.1 km compared to 0.5 km for the data in Figure 6. Despite the larger array

dimensions (2.2 by 0.8 km), it is still possible to infer a similar regularity in

face deformation processes via the distribution of seismic events. 

Summary

An automated PC-network-based seismic data acquisition and processing system

has been developed by NIOSH for use in mine safety studies. Several systems

have been constructed and deployed in the field for testing and application.

Current installations utilize multiple autonomous seismic networks located both

underground and on the surface. Event locations, magnitude estimates, and

other processed data are distributed for display and analysis on network nodes

both underground and in the offices of mine engineers and management.

System flexibility is derived from the system=s distributed nature, compatibility

with multiple analog-to-digital converters and operating systems, and user

control over the automated processing. These systems are now being applied in

studies designed to reduce hazards associated with roof falls, rock bursts, coal

bumps, and mine collapse.

Acknowledgements

This work would not have been possible without the assistance and cooperation

of Silver Valley Resources, Twentymile Coal, and Plateau Mining companies. 

Specific individuals who were particularly generous with their time and

assistance include Butch Sines, Rocky Thompson, Andy Schissler, and John

Mercier. A number of software modules used in the development of this

system were authored and/or modified by R. Banfill, F. Boler, D. Dodge, L.

Estey, B. Kenner, and P. Santerre. Tim Geiger developed the remote

digitizers. Carl Sunderman developed amplifiers and filter units used in some

of the applications. NIOSH employees Brian Kenner, Todd Krahenbuhl, Keith

Heasley, John Marshall, Craig Compton, John Ellenberger, and Paul Jeran

made critical contributions in the deployment and application of the monitoring

systems described herein.
14

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