Safety in Mines Research Advisory
Committee
Final Project Report
Improved seismic locations and
location techniques
by
A Cichowicz, SM Spottiswoode, LM Linzer,
D Drent, PS Heyns, and MF Handley.
Research Agency:
Project Number:
Date:
University of Pretoria
SIM020304
2 March 2005
Executive summary
The location of a seismic event is critically important because not only does a good
location provide a guide for the rescue effort, but also all other parameters (e.g.
moment, and energy) are dependent on it. Location errors scatter the seismic
locations, causing them to plot as diffuse clouds, which obscure any seismic patterns
that might be emerging. This impacts negatively on seismic hazard assessment,
which is often based on spatial-temporal seismicity trends, hence location error has a
direct effect on mining safety.
There are two broad groups of event location methodologies, namely absolute
location methods, and relative location methods. An absolute location is defined in
this report as a once-off location of a single seismic event using some or all the
seismograms recorded by the seismic system for the event. A relative location uses
some or all of the seismograms from two or more neighbouring seismic events to
locate them relative to each other. The rationale for relative locations originates from
the observations of Omori, a Japanese seismologist, who in 1905 noted that
horizontal pendulum seismometers produced similar motions for neighbouring
earthquakes. Relative locations tend to be more accurate than absolute locations
because of the increased utilisation of data from each neighbouring event in the
group.
At present both earthquake and mining seismologists use absolute event locations as
standard. Absolute location errors in the mining industry range from 30 m to about
100 m, depending on the sensitivity of the system. With errors of this order it is
usually possible to pinpoint which working place is closest to the event, and to draw
reasonably credible event location – damage distribution correlations. It is also
possible to positively identify the most seismically active areas on a mine, and to
assess the effectiveness of measures to reduce the seismic hazard. However, these
benefits can only be approximately determined at best, which places mine leadership
in the invidious position of having to implement short-term measures to minimise
employee exposure to the seismic hazard. Short-term measures consist of deciding
whether to evacuate, therefore potentially losing face shifts, to improve stope support
temporarily to minimise the probability of a rockburst, or a seismically induced fall of
ground, or to continue work as before, thereby exposing employees to a perceived
seismic hazard. Since seismic data remains insufficiently reliable for such decisionmaking, the mining industry has rightly steered clear of any implementation of such
short-term measures, excepting in the most exceptional circumstances.
One effective way to improve absolute location accuracy is to install more geophones
or accelerometers, that is, to increase the sensitivity of the system so that more data
are included in the location. Since the seismic system becomes more sensitive, it
records many more events than before, which places a larger load on seismic
processing staff. Another rationale for increasing system sensitivity is to improve the
reliability of the data on which to base implementation of short-term measures to
minimise exposure to the seismic hazard. In a nutshell, increasing system sensitivity
improves the potential to reliably predict seismic events, which would have a major
impact on mine safety. This project aims to explore the potential of an alternative
route: get the best out of the seismic system already in place at the lowest possible
cost, while at the same time improving safety through more reliable seismic hazard
assessment.
2
This option proposes to leave the seismic system unchanged, and to use relative
location techniques to improve location accuracy. All this requires is modifications
and additions to existing seismic location software, and will be free from all the other
problems associated with more data, and higher processing loads. The relative
location techniques can be performed automatically, so they should not add to
processing loads on seismic staff. However, biases can creep in, so methods to
check this are necessary. The most effective way to ensure the quality of relative
locations is to locate the events in relation to a master event of known location. In
mining, catalogues of master events could be built up from signals recorded from the
daily blast. This may not always be possible, so the mine seismologist could turn to
one of the many methods used by earthquake seismologists, who seldom have
artificially produced master events of known location to work with.
Improved seismic locations using relative location techniques have been
demonstrated in numerous case studies in the literature (see main report for more
details). In many instances the authors of these case studies have indicated that
merely relocating events using relative relocation techniques will produce only
marginally improved results unless other aspects of the seismic data such as signal
to noise ratio, P- and S-pick inconsistencies, seismic signal quality, velocity models,
and arrival time accuracy are addressed simultaneously. This often requires
painstaking work on the part of the operator, which is not practical for day-to-day
seismic monitoring in mines.
This study aims to address all these problems and constraints simultaneously, in a
way that would be practically implementable in the mining environment. In order for
this to be possible the following objectives for a new location algorithm must be met:
1. The relative location algorithm must be able to handle large amounts of data,
of the order of 10000 events per day;
2. It must be able to extract constrained locations from the seismic data
obtained from existing seismic systems using one or more of the relative
location techniques, with location errors of the order of one magnitude smaller
than those currently obtained with absolute location methods;
3. The algorithm must provide clear seismic clustering patterns (i.e. clear
images of seismically active features in the rockmass) in the located data,
together with a viable means of demonstrating that there are no systematic
biases or errors in the images provided;
4. The algorithm must be sufficiently automated in order that it requires
minimum operator intervention, thereby minimising location and quality
control workloads, while at the same time providing more time for analysis
and interpretation;
5. It must provide significantly improved seismic data for management and
research purposes;
6. It must be able to relocate seismic events in existing databases
retrospectively, thereby creating better quality databases.
In a proposal for this research written in 2001, the following outputs were identified
for this project:
1. The primary output is a greatly improved seismic location algorithm compared
with those currently available - the error of location determination should be
one order of magnitude smaller than that possible with current algorithms, all
other variables such as the seismic system, rock mass structure and rock
mass properties being constant;
3
2. An automatic location algorithm that accurately picks P- and S-onsets in both
good signal:noise seismograms and noisy seismograms with an error level of
1 in 104 picks;
3. The secondary outputs include more accurate seismic parameter
quantification e.g. energy and scalar moment; reliable automatic P- and Swave onset determination, or a means of constraining these picks so that they
provide reliable locations;
4. A significant reduction in the manpower needed to locate the events, manage
the data, and perform quality checks.
The proposal went on to outline the principal research thrusts that would be
necessary to achieve the above goals and outputs:
1. Investigate weaknesses and strengths of the waveform similarity technique
and other location improvement techniques to define the applicability of each
as a general location algorithm;
2. Identify and quantify sources of noise in seismograms. Use the University of
Pretoria servo-hydraulic equipment to carry out this task with geophone and
accelerometer clusters;
3. Determine options for identifying seismic signals in noisy seismograms;
alternatively determine the signal to noise ratio relationship with P- and Sonset determination accuracy, and define an acceptable signal: noise ratio
criterion.
4. Develop waveform similarity software for inclusion in current seismic location
algorithms
5. Review and refine automatic means of P- and S-onset identification in all
seismic signals (including noisy ones); alternatively develop an automatic
means to separate signals with acceptable signal: noise ratio for P- and Sonset determination;
6. Test improved seismic location techniques, determining errors of location and
reliability of automatic location. Refine seismic location algorithms during this
test phase;
7. Estimate the error of seismic parameter determination (confine this to location
error, scalar moment, and energy, considering site effects, seismic system
variables, mining factors, and geotechnical conditions), backing this up with
laboratory experiments if need be;
8. Comprehensive report on the project, covering all aspects of the research, the
location algorithms and their implementation, together with limitations and
capabilities;
9. Workshops on the software, its capabilities and limitations, estimated error of
determination of location, scalar moment, moment tensor, and energy, and
the relationships between these errors and site factors, seismic system
factors, mining factors, and geotechnical conditions.
The second point in the above list addresses noise, noise sources, and their effects
on the seismograms because this aspect cannot be excluded from a holistic
approach to the problem, as has been suggested in the case studies reviewed. All
the above-mentioned work should be confined to mining-induced seismic events with
local magnitudes of 0.0 and larger, and aimed at typical mine-wide seismic systems
with 10 to 30 sensors covering an area of about 25 sq.km. If the results achieve the
stated goals of an order of magnitude reduction in location error and a reliable
automatic location algorithm, then the door to real-time seismic monitoring and
analysis will have been opened.
4
The project began on 1 April 2002, continuing for two years to 31 March 2004. The
project proposal appears in Appendix IV. The cost of the project totalled R3.2-million
with the bulk going to the two outside sub-contractors, namely the CSIR Division of
Mining Technology, and Integrated Seismic Systems International (ISSI), who were
both to develop and integrate relative location algorithms into existing seismic
location software for use on the mines. The department of mechanical engineering
at the University of Pretoria also carried out some research on geophone noise, and
data distortion because of site effects and geophone misalignment. The full reports
from the three sub-contractors appear in Appendices I-III. A summary of the project
results follows.
The work by CSIR Miningtek has resulted in significant improvements in location
methodologies. In particular, the hybrid location method, an offshoot of the double
difference location method, was developed and shown to provide significant
improvements in location accuracy. Additional work by the CSIR Miningtek group
included showing the potential benefits of using development blasts to improve
locations and advancing the robust use of waveform similarity in automatic locations.
An example of how locations can be improved appears in Figure E1. More details of
analyses and ways of evaluating the error of improved locations appear in Appendix
I. All the software used to carry out this work has been implemented in the PRISM
suite of programs.
Figure E1: Relocated event clusters (dark gray) are far more
concentrated around development blast locations than the absolute
clusters (light gray) in a deep level mine shaft pillar.
ISSI, through a series of case studies, has used several relocation techniques to
improve seismic locations. The techniques include group locations using a single
master event, group locations using multiple master events, and double difference
methods for relative relocation of large event clusters. All methods show
improvement in location accuracy, but the double difference method produces the
best results, similar to those produced by Miningtek. ISSI also had the brief to
continue with development of an automatic location algorithm, with results shown in
Figure E2. The automatically located results are very good, and would be amenable
to relocation using relative location methods, together with P- and S-pick
inconsistency minimization methods. At present, there is no reliable automatic Swave picker because so many seismigrams have S-onsets in the P-wave coda,
thereby making it alost impossible to obtain a reliable pick. The results shown rely on
P-wave picks only, where reliability is extremely good, with fewer than one in 10000
5
P-picks in error. This means that routine automatic location without operator
intervention and quality checking is very close.
Figure E2: Comparison of absolute locations obtained manually (top)
and automatically (bottom).
Details of all the analyses appear in Appendix II. All the software required to perform
the research has been included and implemented in the ISSI suite of seismic
software. The report also contains tutorials aimed at the mine seismologist on how to
use the relative relocation techniques. The techniques can be used in real-time (i.e.
6
locating events as they happen), or off-line (reprocessing the daily seismic data to
improve locations during quiet periods, say at 5am every morning).
Appendix III contains the report from the University of Pretoria, which was to
determine the effect of installation conditions on data recorded by geophones in
South African mines. The two major aspects considered were geophone alignment
(horizontal and vertical) and the grouting conditions. Misalignments of a few degrees
have been shown to have significant effects on geophone output, thereby introducing
systematic errors into seismic locations. A method to accurately determine
geophone alignment at geophone sites underground has been developed. This can
be used to correct geophone outputs, thus removing systematic bias from location
data.
This project has been successful in that it has achieved all the objectives originally
set for it. Like all projects there were management problems that arose from
unexpected deviations from the original work plan. These did not occur because of
poor management, but because of incorrectly estimated workloads for each enabling
output. For example enabling output 4: Noise in seismograms and its removal
proved to require far less effort than anticipated, while it became evident that, once
the project had started, a literature survey was critical to the future direction of the
project. No provision for a literature survey had been made in the proposal. The
case studies and error determinations went off as planned, as did the implementation
of the relative location algorithms in the seismic location software. The laboratory
work carried out at the University of Pretoria quickly grew to much more than had
been anticipated by the proposal, and is still ongoing as an MSc project at the
University. The proposal envisioned only laboratory work, but the project had to be
expanded to the underground environment in order to develop a reliable method of
determining geophone alignments in-situ.
The prime objective, to reduce location error by an order of magnitude (i.e. 10-fold),
was considered too ambitious by all the sub-contractors, even though researchers in
earthquake seismology had claimed 10-fold error reductions using the double
difference method, and a 5-fold reduction in error using the waveform similarity
method. These results, obtained from smaller datasets than mining datasets,
required meticulous and time-consuming work by the authors, which is impractical in
the mining environment because of the manpower cost and the large number of
events involved. What this project has achieved is a 2- to 5-fold reduction in location
errors when compared with absolute locations, depending on circumstances such as
the validity of velocity models, and the accuracy of master event locations. This
achievement has been balanced by another, which is to obtain improved results with
minimum operator input and no extra cost, a far more difficult task than had been
anticipated in the proposal. As experience with the software grows, and mine-wide
velocity models improve with implementation of the software, it is anticipated that the
10-fold reductions in location error will become routine within five years.
Finally, the automatic location algorithm, combined with an automated relative
location algorithm and an automatic P-and S-pick inconsistency algorithm, should be
able to produce seismic data of a consistently good quality. This will meet another
goal, namely to reduce operator workloads, freeing up time for data analysis and
interpretation, at no extra cost. This will have significant effects on seismic hazard
management, safety, and future research projects, while at the same time making
viable seismic prediction a real possibility. Further research should be undertaken in
this direction.
7
Table of contents
1
2
3
4
5
6
7
Introduction ........................................................................................................ 11
Literature survey ................................................................................................ 14
2.1
Improved location algorithms ...................................................................... 15
2.1.1
Waveform similarity ............................................................................. 15
2.1.2
Joint hypocentral location (JHD).......................................................... 17
2.1.3
Double difference relocation method ................................................... 18
2.1.4
Other relocation methods .................................................................... 18
2.1.5
Location uncertainty using relative location methods .......................... 19
2.1.6
Brief comparison of mining induced seismicity and earthquakes ........ 19
2.2
Improvement of seismic data ...................................................................... 20
2.2.1
Site effects on data distortion .............................................................. 21
2.2.2
Managing noisy seismograms and automatic picks ............................ 21
2.3
Guidelines for research strategy from literature survey .............................. 23
Research work ................................................................................................... 25
Discussion of project conformance with stated goals ........................................ 25
Recommendations for future work ..................................................................... 26
References......................................................................................................... 27
Bibliography ....................................................................................................... 30
APPENDIX I…………………………………………………………………………..51
APPENDIX II……………………………………………………………………….. ..91
APPENDIX III………………………………………………………………………. 134
APPENDIX IV……………………………………………………………………….162
8
List of figures
Figure E1: Relocated event clusters (dark gray) are far more concentrated around
development blast locations than the absolute clusters (light gray) in a deep level
mine shaft pillar.................................................................................................... 5
Figure E2: Comparison of absolute locations obtained manually (top) and
automatically (bottom).......................................................................................... 6
Figure 2.2.2.1: Binomial probability of mislocated event vs. no. of stations used in
location............................................................................................................... 22
9
List of tables
Table 1: Enabling Outputs and Work Split ............................................................... 24
10
1 Introduction
Current seismic event location algorithms are based on time residuals and velocity
models (Eccles and Ryder, 1984; Gibowicz and Kijko, 1994; and Mendecki and
Sciocatti, 1997). Such locations are referred to as absolute locations, in that they are
obtained from some or all the seismic signals from a single seismic event recorded
by the seismic system. Absolute locations for the typical mine-wide seismic
monitoring system have errors of the order of 30 to 100 metres, depending on the
distribution of the seismic sensors, their distance apart from each other, and factors
such as the mining geometry and the geotechnical environment. Absolute location
accuracy has not materially improved over the last ten years, and there is little
potential for a significant reduction in location errors.
With errors of the order of 30-100 m it is usually possible to pinpoint which working
place is closest to the event, and to draw reasonably credible event location –
damage distribution correlations. It is also possible to positively identify the most
seismically active areas on a mine, and to assess the effectiveness of measures to
reduce the seismic hazard. However, these benefits can only be approximately
determined at best, and it is impossible to pinpoint the greatest seismic hazards, for
example a potentially seismically active fault or dyke ahead of a face.
One effective way to improve absolute location accuracy is to install more geophones
or accelerometers, that is, to increase the sensitivity of the system so that more data
are included in the location. Since the seismic system becomes more sensitive, it
records many more events than before, which places a larger load on seismic
processing staff. Another rationale for increasing system sensitivity is to improve the
potential to predict seismic events. This project aims to explore the potential of an
alternative route: get the best out of the seismic system already in place at the lowest
possible cost, while at the same time improving safety through more reliable seismic
hazard assessment.
This option proposes to leave the seismic system unchanged, and to use relative
location techniques to improve location accuracy. All this requires is modifications
and additions to existing seismic location software, and will be free from all the other
problems associated with more data, and higher processing loads. The relative
location techniques can be performed automatically, so they should not add to
processing loads on seismic staff. A relative location uses seismograms from two or
more seismic events, to locate them relative to each other. Relative locations are
generally more accurate than absolute locations because more data (i.e. more
seismograms) are used to locate them. However, biases can creep in, so methods
to check this are necessary. The most effective way to ensure the quality of relative
locations is to locate the events in relation to a master event of known location. In
mining, catalogues of master events could be built up from signals recorded from the
daily blast. This may not always be possible, so the mine seismologist could turn to
one of the many methods used by earthquake seismologists, who seldom have
artificially produced master events of known location to work with.
Uncertainties in event locations (or hypocentre scattering) can be grouped into two
categories: random errors and systematic errors. Random errors are generally
caused by errors in the arrival times of picked wave phases. Such errors include
picking inconsistencies, the misidentification of seismic phases, variations in signalto-noise ratios, and human subjectivity. Systematic errors are caused by the
variation in the rock mass structure between the seismic source and the receiver.
11
Such variations could be due to the local geology, or the presence of stopes and
associated fractured rock that result in ray path focusing and defocusing of the elastic
wave between source and receiver. All these errors combine to a greater or lesser
degree in the location error associated with the absolute location of each seismic
event.
Thus, the quest for more data in order to improve the seismic service is probably
incorrect beyond a certain threshold quantity. It appears that if a seismic system is
sensitive down to magnitude zero, it provides a manageable amount of data while at
the same time allowing sufficiently effective seismic interpretation for adequate
seismic hazard management. Van Aswegen (2004) has demonstrated improved
seismic hazard analysis and management by increasing seismic system sensitivity
locally, for example in an area on the mine or around a geological feature that is
known to be seismically hazardous.
An alternative approach is to improve the quality of the data already being recorded
and processed without adding any sensors to the existing systems, in other words,
without increasing costs. This approach is not in any way at odds with SIMRAC
Project GAP 601, which advocates increased sensitivity for improved data analysis
and interpretation. Instead, this project actually complements GAP 601, because the
results obtained will lead to improved seismic locations obtained from seismic
systems with enhanced sensitivity. This project aims to improve seismic locations by
an order of magnitude; that is, to reduce the error of location by an order of
magnitude, without requiring any changes or additions to existing mine-wide seismic
systems. Note that the principal output of this project is equally applicable in systems
with improved sensitivity.
The only techniques available to improve location accuracy without changes to the
seismic system are relative location techniques, which use seismic information from
two or more seismic events to constrain the location of the event of interest. The
waveform similarity technique is one relative location method that has already
demonstrated its ability to achieve this goal. There are other relative location
techniques, such as the Joint Hypocentre Determination or JHD method, the Doubledifference Method, and relative relocations based on the known positions of master
events.
Improved seismic locations using one of the above-named relative location
techniques have been demonstrated in numerous case studies in the literature (see
Literature Review below). In many instances the authors of these case studies have
indicated that merely relocating events using relative relocation techniques will
produce only marginally improved results unless other aspects of the seismic data
such as signal to noise ratio, P- and S-pick inconsistencies, seismic signal quality,
velocity models, and arrival time accuracy are addressed simultaneously. This often
requires painstaking work on the part of the operator, which is not practical for dayto-day seismic monitoring in mines.
This study aims to address all these problems and constraints simultaneously, in a
way that would be practically implementable in the mining environment. In order for
this to be possible the following objectives for a new location algorithm must be met:
1. The location algorithm must be able to handle large amounts of data, of the
order of 10000 events per day;
2. It must be able to extract constrained locations from the seismic data using
one or more of the relative location techniques, with location errors of the
12
order of one magnitude smaller than those currently obtained with absolute
location methods and existing mine-wide seismic systems;
3. The algorithm must provide clear seismic clustering patterns (i.e. clear
images of seismically active features in the rockmass) in the located data,
together with a viable means of demonstrating that there are no systematic
biases or errors in the images provided;
4. The algorithm must be sufficiently automated in order that it requires
minimum operator intervention, thereby minimising location and quality
control workloads, while at the same time providing more time for analysis
and interpretation;
5. It must provide significantly improved seismic data for management and
research purposes.
In a proposal for this research written in 2001, the following outputs were identified
for this project:
1. The primary output is a greatly improved automatic seismic location algorithm
compared with those currently available;
2. The secondary outputs include more accurate seismic parameter
quantification e.g. energy and scalar moment; reliable automatic P- and Swave onset determination, or a means of constraining these picks so that they
provide reliable locations; a significant reduction in the manpower needed to
locate the events, manage the data, and perform quality checks.
The proposal went on to outline the principal research thrusts that would be
necessary to achieve the above goals and outputs:
1. Investigate weaknesses and strengths of waveform similarity technique and
other location improvement techniques and define the applicability of each as
a general location algorithm;
2. Identify and quantify sources of noise in seismograms. Use the University of
Pretoria servo-hydraulic equipment to carry out this task with geophone and
accelerometer clusters;
3. Determine options for identifying seismic signals in noisy seismograms;
alternatively determine the signal to noise ratio relationship with P- and Sonset determination accuracy, and define an acceptable signal: noise ratio
criterion.
4. Develop waveform similarity software for inclusion in current seismic location
algorithms
5. Review and refine automatic means of P- and S-onset identification in all
seismic signals (including noisy ones); alternatively develop an automatic
means to separate signals with acceptable signal: noise ratio for P- and Sonset determination;
6. Test improved seismic location techniques, determining errors of location and
reliability of automatic location. Refine seismic location algorithms during this
test phase;
7. Estimate the error of seismic parameter determination (confine this to location
error, scalar moment, and energy, considering site effects, seismic system
variables, mining factors, and geotechnical conditions), backing this up with
laboratory experiments if need be;
8. Comprehensive report on the project, covering all aspects of the research, the
location algorithms and their implementation, together with limitations and
capabilities;
9. Workshops on the software, its capabilities and limitations, estimated error of
determination of location, scalar moment, moment tensor, and energy, and
13
the relationships between these errors and site factors, seismic system
factors, mining factors, and geotechnical conditions.
The second point in the above list addresses noise, noise sources, and their effects
on the seismograms because this aspect cannot be excluded from a holistic
approach to the problem, as has been suggested in the case studies reviewed. All
the above-mentioned work should be confined to mining-induced seismic events with
local magnitudes of 0.0 and larger, and aimed at typical mine-wide seismic systems
with 10 to 30 sensors covering an area of about 25 sq.km. If the results achieve the
stated goals of an order of magnitude reduction in location error and a reliable
automatic location algorithm, then the door to real-time seismic monitoring and
analysis will have been opened.
2 Literature survey
The location of a seismic event is the most important seismic parameter because all
other parameters (e.g. moment, and energy) are dependent on it. In addition, the
interpretation of seismogenic features is critically dependent on the accuracy of the
estimated hypocentre positions, while hypocentral scattering due to location error
affects other parametric data, for example, spatial-temporal seismicity trends, and
rockmass structure imaging. Thus seismic hypocentre location and relocation is as
old as seismology itself, which began to grow as a science with the development of
the first seismometer by L Palmieri in 1855, and the development of horizontal
pendulum seismometers by John Milne in Japan in 1880 (Encylcopædia Britannica,
2003). Some twenty-five years later, Omori (1905) noted that horizontal pendulum
seismometers produced similar motions for neighbouring events. Perhaps the
greatest boost to the development of seismology came with the San Francisco
earthquake of 18 April 1906, which was followed by the formation of the
Seismological Society of America on 20 November 1906 (Byerly, 1964).
Even at these early stages, the precise location of a seismic source was of primary
importance, and ways were sought to reduce errors. This did not meet with
immediate success until telemetry (for remote data transmission and seismic station
management) and clock synchronisation (for seismic wave arrival times at different
stations) had improved considerably. Relocation techniques to improve locations
from existing datasets therefore remained relatively undeveloped until the 1960s,
when researchers began to devise and publish techniques to improve hypocentre
location errors. The imperative for developing these techniques came not only from
earthquake seismology, but also from the need for accurate location and
characterisation of nuclear detonations during and after the Cold War. For example
Thurber and Engdahl (2000), in a review of advances in seismic location techniques,
state that global concerns about nuclear armament testing and proliferation demand
improvements in global and regional seismic event location capability. They state
that improved locations can come from:
•
•
•
•
Improved data quality and data usage;
Improved velocity models;
Better station and/or source-region travel-time corrections;
Better location algorithms.
The above points can be divided into two main groups, namely improved data quality,
and improved location algorithms. The literature survey that follows will be split up
into these two categories, since they both form major thrusts in the current research.
14
2.1 Improved location algorithms
There are many ways to optimise the location of an event, amongst which the Nelder
and Mead Simplex Optimisation method (Nelder and Mead, 1965) has been applied
by seismologists. This method is a non-linear numerical algorithm that is more
efficient than the derivative-based Gauss-Newton location algorithms, especially in
cases where only a few P-waves are detected. Other methods described in the
literature to reduce hypocentral scattering are based on time residuals and a velocity
model. Earthquake relocation techniques have recently been markedly improved by
using a number of different techniques, several of which are highlighted in this report.
There are numerous case studies in which reprocessed seismic data using various
techniques has resulted in dramatic improvements in imaging seismically active
structures.
A few examples of the most commonly used relocation techniques will be covered to
provide an indication of the degree of success of these methods, their advantages
and disadvantages, and their applicability to mining induced seismicity. Coverage is
confined to more recent results only, because there are hundreds of papers on event
relocation in the literature, and the latter relocation case studies are probably more
dramatic than earlier studies. A bibliography containing a sample of over 300 papers
written on relocation and related subjects appears after the references. This
bibliography is by no means complete, thus it serves to show how extensively these
techniques have been reported in the literature. The second purpose of the
bibliography is to provide a reliable guide to the serious student of seismic
hypocentre relocation techniques.
2.1.1 Waveform similarity
Waveform similarity relocation techniques are based on Omori (1905), who observed
that events with neighbouring locations possess similar waveforms. The physical
reasons for this are that mechanisms of neighbouring events are likely to be similar,
and the hypocentre–geophone ray paths for the two events should pass through
similar rock. Based on this it is possible to improve P- and S-wave arrival time
measurements by making them more consistent in the dataset. The accuracy of the
relative locations can be further enhanced if the events are clustered spatially and
have similar waveforms. The waveform similarity is exploited to pick precise arrival
times (e.g. Got et al. 1994; Nadeau et al., 1995; Spottiswoode and Milev, 1998;
Rubin et al., 1999). The maps of the enhanced locations determined using these
techniques show details not evident in absolute locations computed using
conventional methods.
Slunga et al. (1995) discuss three case studies of relocated event clusters in Iceland
which not only fit fault plane solutions obtained from the data, but also fit fault plane
positions determined by dip and strike measurements at surface. Nadeau et al.
(1995) used waveform similarity techniques on microseismic data from the San
Andreas Fault, which showed clustering on the fault surface. This data was also
used to assess loading of the fault. Got et al. (1994) used multiplet (multiple similar
waveforms) relative location of seismicity below Mount Kilauea, Hawaii, to resolve
subsurface structural features responsible for the seismicity. There are several more
papers in the bibliography on seismicity associated with volcanicity at Mount Kilauea
and its implications for subsurface structure and tectonism.
Phillips et al. (1997) used the similar waveform technique to relocate some 11000
seismic events in a geothermal reservoir at Fenton Hill, New Mexico. Nearly all the
relocated events fell into spatially distinct, planar clusters of 80-150 events, which
revealed the activation of joints in the rockmass. Many of the clusters showed sharp
15
linear edges, which indicate the boundaries of slipping joints truncated by aseismic
joints. The authors conclude that they were able to determine a detailed joint
structure from the relocated seismic data.
Menke (1999) discusses waveform similarity (waveform cross-correlation) and
differential traveltimes to constrain earthquake locations, for application in nuclear
nonproliferation. He claims (for worldwide networks) that differential traveltimes can
be measured to millisecond precision, allowing relative location precise to a few
metres. The author briefly presents two case studies, firstly a synthetic dataset
containing 64 events with 99% improvement in misfit, and secondly, using the
principal of reciprocity, to locate eleven seismic stations in a linear array using
seismograms measured at the stations from a single earthquake. The solution in the
second case reduces the data misfit by 95%, and recovers the linearity of the station
locations and their relative order. Although the paper does not contain much detail of
the computation-intensiveness of the procedure, the author notes that about 100000
iterations-requiring a few minutes on a computer workstation-are necessary to obtain
a solution. The program is available in the public domain, and should be tested in
the mining environment.
Aster and Rowe (2000) assert that inconsistent P- and S-wave arrival estimates in
large seismic datasets will obscure true hypocentre distribution in space, thereby
masking important seismogenic structures such as faults or abyssal magma
movements in volcanoes. Available algorithms have resulted in dramatic
improvements in discerning seismogenic structure, but they still require substantial
analyst intervention, therefore limiting their applicability to small datasets. They
summarise progress toward a fully automated technique based on waveform
similarity that removes P- and S-wave pick inconsistencies in large datasets.
In a follow-up case study using the above techniques on large datasets, Rowe et al.
(2001) were able to reduce the median hypocentre discrepancy distance from 31m to
7m at the Soultz Geothermal Reservoir. They conclude in this study that large-scale
automatic reprocessing will not provide the flexibility and adaptability of human
analysis for some time. As far as is known to date, this algorithm is not yet
implementable automatically, still requiring considerable operator intervention to
ensure reliable seismic data. Although this approach could be applicable in the
mining situation because it promises to reduce operator load, it is considered not yet
suitable for reprocessing mine events, because many mine arrays detect in excess of
1000 seismic events daily.
In the only known publication of the use of waveform similarity in a mining situation,
Young et al. (2001) extended the work of Riviere-Barbier and Grant (1993) to analyse
clusters of similar waveforms from mining-site blasts in New Mexico and Arizona.
The startling aspect of this work is that the authors were able to relocate the clusters
on the respective mines, which lay up to 300 km beyond the boundaries of the 15station NMTSN (New Mexico Tech Seismic Network). Their method is complicated
and computer-intensive, and is probably not suitable for daily use on a seismically
active mine.
Waveform similarity techniques clearly produce startling improvements to seismic
datasets, to the point that diffuse clouds of absolute locations resolve into images of
seismically active structures. Although the results are good, waveform similarity
techniques rely on extensive operator intervention and judgment. The authors of the
above papers report that it is painstaking and slow work, and is not suited to large
seismic catalogues. The waveform cross-correlation methods are also computer-
16
intensive, and not always reliable. It therefore appears that this technique will not be
easily automated, and it is thus not appropriate for relocating large mining-induced
seismicity catalogues. The waveform similarity method is probably the most
extensively used relocation technique, becoming increasingly popular in earthquake
studies. For example, the most recent issue of the Bulletin of the Seismological
Society of America (Volume 92 - issue 5, 2002) has two out of 33 papers on the
topic, namely that of Rubin (2002), and Got et al. (2002).
A common thread to all the papers to date on the topic is that doublets or multiplets
with almost identical waveforms are fairly common, but not the rule. The degree of
similarity decreases with inter-event distance, but in a controlled manner that can be
used with advantage. When processing for large mine data sets, each event of large
data sets should be compared with all nearby events. This approach may not be
foolproof, because hypocentral scattering may bring relatively distant, non-similar
events together. Conversely, widely scattered similar events may never be
compared.
2.1.2 Joint hypocentral location (JHD)
The effects of errors in the knowledge of the earth structure can be minimised by
using relative location methods (e.g. Got et al., 1994). These methods apply if the
hypocentral separation between two earthquakes is small compared to the eventsensor distance and the scale length of the velocity heterogeneity. Under these
conditions, the ray-paths between the source region and receiver can be considered
common, and the difference in travel times for two events observed at one station
can be attributed to the spatial offset between the events with high accuracy. JHD,
or Joint Hypocentre Determination (Dewey 1971, Pujol 2000) is a relatively simple
but effective way to account for lateral velocity variations not included in onedimensional velocity models used to locate seismic events. Experience shows that
JHD always improves relative locations, but may introduce error to absolute
locations, for example a systematic shift in true locations in some circumstances. It
is however, an effective semi-quantitative method for three-dimensional velocity
assessment, and therefore may have application in developing evolving velocity
models for the mining situation.
Jones and Stewart (1997) presented a statistical approach to reducing hypocentral
‘fuzziness’ caused by pick inconsistencies. This method is also referred to as
‘collapsing’ (see also Fehler et al., 2000 below), and is based on the assumption that
the pick errors form a Gaussian distribution. According to Rowe et al. (2002)
synthetic tests as well as comparisons to results from manual repicking of recorded
data sets have demonstrated considerable success. These researchers suggest that
collapsing, in combination with techniques for adjusting the velocity model and/or
near-surface static corrections, may be a good approach to improve delineation of
seismogenic features. The advantage of collapsing is that waveform data is not
required. However, the collapsing technique can be prone to artifacts.
Fehler et al. (2000) propose a method known as ‘JHD-collapsing’. This method is
based on the same basic assumption of Jones & Stewart (1997) that there is a
greater clustering on actual earthquake locations than there is in locations
determined using conventional techniques. The difference is that the method is
implemented as part of the Joint Hypocentre Determination (JHD) process so it
operates on raw travel-time data rather than on derived hypocentres. These
researchers found that the JHD-collapsing method resulted in the hypocentres being
significantly more clustered than those found using JHD alone. The authors present
one case study based on that by Phillips et al. (1997), where the researchers had
painstakingly re-picked the relative arrival times of events in a catalogue. JHD-
17
collapsing produced comparable results to the manually re-picked results from
Phillips et al. (1997) with considerable savings in time and manpower.
2.1.3 Double difference relocation method
Waldhauser and Ellsworth (2000) have developed a ‘double-difference’ location
algorithm, which is an offshoot of the JHD-collapsing method described above.
Details of the method appear in Appendix II. It is effective in sharpening the
seismogenic source volume image of large numbers of earthquakes. The location
method incorporates absolute travel-time measures (obtained from catalogue data)
and/or cross-correlations of P- and S-wave differential travel-times (derived from
cross-spectral methods). The method is based on the premise that if the interhypocentre distance is much less than the source-sensor distance, then the seismic
waves must have travelled along similar paths through essentially the same rock,
and the resulting travel-time residuals must therefore be an accurate measurement of
the offset between the two hypocentres (see also Got et al., 1994). The residuals
between the observed and theoretical travel-time differences are known as ‘doubledifferences’ and are minimised for pairs of earthquakes at each station while linking
together all observed event-station pairs.
These researchers applied the double-differences algorithm in two separate case
studies to relocate earthquakes on the northern Hayward Fault, California. The
double difference relocations improved location uncertainty by an order of magnitude,
condensing the two clusters on the fault surface into sharply defined features. The
relocated on-fault seismicity revealed a nearly planar and vertical fault zone
extending beneath the surface trace of the Hayward Fault. In an earlier study,
Waldhauser et al. (1999) revealed a focused picture of events on the Hayward Fault
aligning in depth along linear, horizontal streaks. Waldhauser (2001) later reported
on the computer program HypoDD, which was written to perform the doubledifference relocations. The software is available in the public domain. This method
is automated, fast, can be applied to dense clusters of seismic events (in fact it works
best in this situation), and can be applied to large event catalogues, giving it strong
potential for use in the mining situation.
2.1.4 Other relocation methods
Myers and Schultz (2000) use a master event location technique to calibrate travel
times along certain paths. Because events (nuclear detonations) with known space
and time coordinates are very restricted, a technique of imperfect calibration has
been developed using well-located earthquakes. This work does not appear to have
application in the mining industry unless calibration blasts with known space and time
coordinates are carried out on seismically active mines.
Sambridge and Kennett (2001) applied a recently developed direct search method for
inversion known as the Neighbourhood Algorithm (NA) to the location problem. The
algorithm uses randomised sampling of a 4-dimensional hypocentral parameter
space, to search for solutions with an acceptable fit to data. At each stage, the
hypocentral parameter space is partitioned into a series of convex polygons called
Voronoi cells. Each cell surrounds a previously generated hypocentre for which the
fit to the data has been calculated. New hypocentres are generated randomly in the
neighbourhood of those hypocentres with smaller data misfit as the algorithm
proceeds. In this way, all previous hypocentres guide the search and the more
promising regions of parameter space are preferentially sampled, until a solution is
found with acceptable data fit.
18
The procedure is repeated on an event-by-event basis, thus enabling the procedure
to be fully automatic. The authors present two case studies of which the first is the
Marinas event relocation, which quickly converges to the optimal position. The
second case study involves a nuclear detonation at the Semipalatinsk Test Site in
east Kazakhstan, for which the Soviets had supplied the authors the precise
coordinates (Bocharov et al., 1989). The result showed good convergence with best
fits in a block 5 km in a North-South direction by 3 km in an East West direction. This
result was based on sparse data. The authors claim that the method provides good
quality locations with far fewer travel-time evaluations than any previous non-linear
location procedure. This procedure shows promise for the mining situation because
it is automatic, it provides good locations both inside and outside the seismic array,
and the procedure requires only sparse information to obtain a location. As far as is
known, this procedure has never been applied in a mining situation.
Rabinowitz (2000) bases his Flexible Tolerance Method (FTM) on that of Nelder and
Mead (1965), considering it a natural extension, and an efficient location optimization
algorithm. This scheme has applications in automatic location algorithms, where
there is a real need to incorporate nonlinear restraints to minimise the possibility of
an incorrect location. The constraints that can be applied are the time-order of Parrivals, back azimuths to some stations, and source-to-station distances from S-P
separation information. All these are effectively incorporated in the FTM scheme,
improving its efficiency, and down-weighting possible outlying observations. This
scheme is especially effective where data is few, and where the hypocentre lies
outside the array, which will often cause large location errors with standard location
algorithms.
2.1.5 Location uncertainty using relative location methods
Jones and Stewart (1997) say that there is always greater clustering at actual
earthquake locations than there is in absolute locations obtained by conventional
techniques. This means that diffuse clouds of seismicity, which do not lend
themselves to much further analysis and interpretation, are brought into sharper
focus using an improved location technique. It would seem that most of the authors
above are satisfied with results purely on the basis that imaging of seismogenic
structures has improved by the denser clustering of events around them. Some of
the results are compelling, e.g. spatial correlation of imaged structures with surface
fault traces in Iceland (Slunga et al., 1995), and the Northern Hayward Fault,
California (Waldhauser and Ellsworth, 2000).
Very little error analysis has been undertaken in the recent literature, certainly with
respect to the relocation methods discussed above. Rodi and Toksös (2001) have
developed a general theoretical and computational framework for characterising the
uncertainty in seismic locations, especially in situations where the seismic data is
sparse. The authors show the uncertainty distributions surrounding event locations
using sparse data in three separate case studies, concluding that elliptical
uncertainty regions derived from a linear Gaussian analysis do not adequately
characterise the uncertainty. This work is to be followed by developing a
methodology, which is likely to be based on seismic catalogues obtained from local
and regional networks in Turkey.
2.1.6 Brief comparison of mining induced seismicity and
earthquakes
Seismic data from the mining environment shows three fundamental differences with
earthquake data. The first difference is that mining-induced seismicity is dynamic.
By the term dynamic we mean that different rockmass structures will be excited (or
19
induced to release seismic energy) at different times as the mining advances into
previously unmined ground. This should place constraints on the choice of clusters,
since newer events in the cluster may not have taken place on the same structure as
earlier events. The implications of this are not known, although it is certain that
waveform similarity techniques will probably be less applicable than in the natural
environment, where seismicity is essentially static (i.e. confined to one or several
seismogenic structures, and for all intents and purposes remaining approximately
constant in our lifetime).
The second significant difference is that mining data are generally not sparse, since
data from eight or more geophones is often available from seismic events with ML ≥
0.0 on the typical mine-wide seismic system. The third difference is that mininginduced seismic events are small (approximate effective range -4.0 ≤ ML ≤ 5.0) in
comparison with earthquakes (approximate effective range -4.0 ≤ ML ≤ 8.6), yet the
hypocentre size in mines in relation to the source-sensor distance is often
considerably larger than the case for earthquakes. These fundamental differences,
and the lack of objective error analysis, require that objective relocation uncertainty
estimates should be addressed in the mining situation. Although it has been implied
in this project, no direct provision has been made for uncertainty analysis, or for the
objective evaluation of bias-free relocated datasets.
2.2 Improvement of seismic data
Good locations are born of good seismic data. Good data is only possible if all of the
following criteria have been met:
•
•
•
•
•
•
•
•
•
•
•
Vertical geophones are vertical;
Horizontal geophones are horizontal, and correctly aligned in the desired
directions;
The sensors are well coupled with the rockmass (i.e. solidly grouted into the
borehole);
The sensors are sited in solid rock;
Mining induced stress changes at the sensor site are expected to remain
low so that there is no stress-induced damage to the surrounding rockmass
or the borehole grout;
There are no sources of either mechanical or electrical noise near the
sensor installation, e.g. fans, or electrical interference;
The sensor cluster, and the borehole grout-sensor interface do not have
any undesirable natural frequencies that introduce noise to the
seismograms at typical seismic frequencies;
The sensor cluster is properly sealed against the ingress of water or any
other substance that could result in corrosion or other damage to the
installation;
The sensors are good quality, and correctly specified for the environment
and seismic signals they will record;
The sensors are connected to data collection and data transfer systems
which will reproduce the measured signals with a high degree of fidelity;
The installation as a whole is adequately protected from any damage that
may occur as a result of normal mining operations, or from vandalism.
This work is aimed at the first five points, since most of these have been
inadequately addressed in the past, while solutions have already been found and
implemented for the latter six points.
20
2.2.1 Site effects on data distortion
Geophone calibration studies are primarily aimed at calibrating the geophone sensor
before installation (Faber and Laroo, 2001; Kim and Lee, 2000). The manufacturer
typically supplies these data to the user along with the sensors purchased. No postinstallation calibrations are described excepting for geophone misalignment
corrections in the horizontal plane (see below). Feroci et al. (2000) discuss grouting
conditions for survey purposes, while the effect of grouting conditions in the mines
has not been covered in the available literature.
Most of the research found by the literature study was aimed at using geophones in
order to obtain a more detailed image of the subsurface geological structure of
certain areas (Feroci et al., 2000; Elkibbi and Rial, 2003). Very little research has
been done on the vertical alignment of geophones, or the effect that vertical
misalignments may have on the data. As far as is known, no work has been done on
how to quantify the magnitude or direction of misalignment in the vertical direction
with the use of the electrical response of the geophone itself.
Geophone alignment in the horizontal plane has already been explored by many
researchers and mining experts (e.g. Bland and Stewart, 1996; Zou and Wu, 2001),
while a lot of work has been done on the calibration of geophone installations in the
horizontal plane (Luna and Jadi, 2002; Elkibbi et al., 2003). Most of this work utilises
events with known time and magnitude in order to calibrate the geophone data. As a
result of the fairly extensive research in this area, most seismic analysis programs
contain software with the capability to rectify misaligned geophones in the horizontal
plane.
2.2.2 Managing noisy seismograms and automatic picks
Noise in seismograms has the effect of reducing the quality of the P- and S-picks for
location purposes, while at the same time introducing errors to the scalar energy and
moment estimations. There are two types of noise: regular, cyclic, or predictable
noise; and white or random noise, which is irregular and unpredictable. Most noise in
nature is a combination of the two. Regular noise can be easily analysed for
frequency content and then subtracted from seismograms where it is present. All
seismic location software available includes this facility.
White noise is far more difficult, if not impossible to eliminate. Imaging of subsurface
structures by active seismic survey uses the signal stacking technique to eliminate
random noise when weak reflections fall below the noise threshold (e.g. Goldstein,
1998). The premise behind stacking is that if all the returning signals are similar
while the noise is generally random, one can “stack” the signals i.e. add them
together resulting in the weak reflections superimposing upon each other, while the
random noise will tend to cancel itself out, resulting in a readable reflection signal.
This of course will require several repeated soundings, which is not possible in the
case of seismicity, since each event only happens once. There may be potential for
signal stacking highly similar waveforms, although this is extremely unlikely because
determining waveform similarity is impossible for signals swamped in noise; it would
have to be done on a trial and error basis with an expected very low success rate.
Researchers have scarcely ventured into this area with perhaps Niewiadomski
(2002) being the sole researcher who has developed a neural network algorithm to
remove noise from seismograms. Numerous authors refer to the subject in passing,
but merely as an issue to be addressed when it poses a problem. For example,
Zhang et al. (2003) describe an automatic P-onset picker that is capable of dealing
with noisy seismograms. The authors claim it is able to pick P-onsets with 95%
21
reliability. If the probability of a successful pick is distributed binomially, the
probability of a mislocated event versus the number of stations used in the location is
shown in Figure 2.2.2.1 below.
Probability of Mislocated Event
0.5
0.4
0.3
0.2
0.1
0.0
4
5
6
7
8
9
10
11
12
Number of Geophones used in Location
Figure 2.2.2.1: Binomial probability of mislocated event vs. no. of
stations used in location
The result shows that the automatic picking algorithm has a 19% probability of
mislocating an event using 4 stations, increasing to 46% for 12 stations using P-Picks
only. Better constraints to the location using S-picks are possible, but far more
difficult. This is especially true when the S-onset lies in the P-coda; Cichowicz (2002)
claims that there is still no solution in sight. This is still far from satisfactory, and
would require extensive operator intervention to manage the quality of the database.
In order to locate 99.99% of all recorded events reliably, the probability of an
unsuccessful P-pick will have to be of the order of 0.00001 (i.e. one in one hundred
thousand), assuming a 12-station location.
Noise reduction and elimination in seismograms does not hold much promise at the
moment for the following reasons:
•
•
•
There are several successful techniques to eliminate P-and S-pick
inconsistencies in seismic datasets, although these still require extensive
operator intervention;
Automatic location algorithms would improve dramatically if P- and S-pick
inconsistencies could be eliminated from large datasets using an automatic
algorithm;
Relative relocation methods such as the Double Difference Method need
only time residuals to obtain reliable locations, which could be made even
more reliable using calibration blasts (master events) and improved velocity
models (which are a natural result of relative relocation methods).
Noisy seismograms can be dealt with in several ways, either by routine maintenance
of the seismic system, or by software methods:
•
Replace the geophone cluster, or relocate it elsewhere if there is a nearby
source of mechanical or electrical noise;
22
•
•
•
•
Investigate possible sources of noise or interference in data transmission
lines/components and rectify or replace where necessary;
Discard the data if the problem is intractable, and consider replacing the
geophone/data link;
Subtract the noise from the seismogram using one of the many frequency
analysers available, and already implemented in seismic location software;
Routinely apply Niewiadomski’s (2002) neural network algorithm to all
seismic data before it is processed for location – this is only possible if the
neural network is adequately “trained” to remove reasonably predictable
noise, and impractical if the neural network has to be “retrained” for each
incoming event.
Experience with seismic location suggests that the signal: noise ratio must be at least
1.25:1.00 for the first swing of the P-wave for P-onset picking, and at least 1.75:1.00
for S-onset picking. There is no simple criterion determinable for this problem,
because these figures will vary according to the type of noise, whether the picks are
manual or automatic, and the location accuracy desired.
It is concluded that this area does not require further investigation, because the
rewards are far outweighed by the costs, and results are far more readily obtainable
from relative location methods.
2.3 Guidelines for research strategy from literature
survey
Although results have been spectacular, the literature survey indicates that the oldest
and most popular technique, waveform similarity, may not be an ideal approach for
the mining environment because it requires extensive operator intervention, intensive
computational input, while it still suffers from waveform cross-correlation pitfalls, and
has been confined to relatively small datasets. Finally, it has not yet been
automated, and due to the complexity of the procedure, it still requires the flexibility
and innovation of human intervention, thereby making it generally unsuitable for the
mining environment at present. Menke’s (1999) procedure may have the most
applicability, although it must be borne in mind that his procedure relates to a very
special class of earthquakes, namely nuclear detonations.
JHD-collapsing (Fehler et al., 2000), the Double Difference Method (DDM)
(Waldhauser and Ellsworth, 2000), the Flexible Tolerance Method (FTM)
(Rabinowitz, 2000), and the Neighbourhood Algorithm (NA) (Sambridge and Kennett,
2001) all promise to be applicable in the mining environment because they have
been automated, are capable of dealing with large datasets, and will require only
minimum operator intervention. Of these JHD-collapsing, and the DDM offer the best
promise because they have both produced good results in fairly extensive testing.
The remaining two approaches should not be tested yet unless there is clear
evidence that the first two methods will not be applicable in the mining environment.
In the long term they should be investigated because they may have applicability with
as-yet unforeseen advantages.
Since so little work has been done on vertical misalignment of geophones, this
should be investigated in the laboratory, both for its effects on the vertical as well as
the horizontal geophones. These are bias determinations, and should not, as far as
is known, result in random noise. Grouting effects should also be investigated in
order to determine their influence on noise. Geophone site effects should also be
tested, using in-situ tests underground. This should result in a viable method to
23
determine geophone misalignment underground, and then to correct for it in the
seismograms.
Table 1: Enabling Outputs and Work Split
Enabling
Output
Number
1
2
3
4
5
6
7
8
9
Description
Subcontractor
Investigate weaknesses and strengths of waveform
similarity technique and other location improvement
techniques and define the applicability of each as a
general location algorithm
Identify and quantify sources of noise in seismograms.
Use the University of Pretoria servo-hydraulic
equipment to carry out this task with geophone and
accelerometer clusters
Determine options for identifying seismic signals in
noisy seismograms; alternatively determine the signal
to noise ratio relationship with P- and S-onset
determination accuracy, and define an acceptable
signal: noise ratio criterion
Develop waveform similarity software for inclusion in
current seismic location algorithms
Review and refine automatic means of P- and S-onset
identification in all seismic signals (including noisy
ones); alternatively develop an automatic means to
separate signals with acceptable signal: noise ratio for
P- and S-onset determination
Test improved seismic location techniques,
determining errors of location and reliability of
automatic location. Refine seismic location algorithms
during this test phase
Estimate the error of seismic parameter determination
(confine this to location error, scalar moment, and
energy, considering site effects, seismic system
variables, mining factors, and geotechnical conditions),
backing this up with laboratory experiments if need be
Comprehensive report on the project, covering all
aspects of the research, the location algorithms and
their implementation, together with limitations and
capabilities
Workshops on the software, its capabilities and
limitations, estimated error of determination of location,
scalar moment, moment tensor, and energy, and the
relationships between these errors and site factors,
seismic system factors, mining factors, and
geotechnical conditions
Miningtek
BE@UP
MF Handley
Miningtek
ISSI
Miningtek,
ISSI
Miningtek,
ISSI
All
All
Noise removal from seismograms should not be pursued any further because this is
an intractable problem that has only been tackled in passing by most authors in
pursuit of other goals, while only a very few have ever tackled the problem as a
primary goal. It is far more prudent to adopt effective seismic system maintenance
policies, and where P-and S-picks are inconsistent because of noise or other system
errors, to improve them using relocation software.
24
3 Research work
In a meeting held at the ISSI offices in Stellenbosch in May 2002, it was mutually
agreed that the task of developing methods to remove noise from seismograms was
too difficult (if not impossible) and therefore this should be reviewed only in a
literature survey, which had not been planned for the project. The literature survey
was undertaken because it became apparent that there was little sense in duplicating
research already described in the literature, and that there were many case studies
and other fundamental work that would provide clear guidelines for the project. The
research work was split between the subcontractors as shown in Table 1.
Late in 2002 it became apparent that the laboratory work scheduled for geophone
distortion testing would not be enough, again indicated by a literature review and
initial laboratory trials (see Appendix III). This led to the decision to incorporate the
work in an MSc, which would go into far more detail than, was provided for in the
proposal. New planning meant taking the study underground, where actual in-situ
conditions at geophone installations could be tested and measured. This required
devising a technique to achieve this, using a modal hammer and instrumented pin.
Details of the research and research results for all subcontractors are included in
Appendices I-III.
4 Discussion of project conformance with
stated goals
This project has been successful in that it has achieved all the objectives originally
set for it. Like all projects there were management problems that arose from
unexpected deviations from the original work plan. These did not occur because of
poor management, but because of incorrectly estimated workloads for each enabling
output. For example enabling output 4: noise in seismograms and its removal proved
to require far less effort than anticipated, while it became evident that, once the
project had started, a literature survey was critical to the future direction of the
project. No provision for a literature survey had been made in the proposal. The
case studies and error determinations went off as planned, as did the implementation
of the relative location algorithms in the seismic location software. The laboratory
work carried out at the University of Pretoria quickly grew to much more than had
been anticipated by the proposal, and is still ongoing as an MSc project at the
University. The proposal envisioned only laboratory work, but the project had to be
expanded to the underground environment in order to develop a reliable method of
determining geophone alignments in-situ.
The prime objective, to reduce location error by an order of magnitude (i.e. 10-fold),
was considered too ambitious by all the sub-contractors, even though researchers in
earthquake seismology had claimed 10-fold error reductions using the double
difference method, and a 5-fold reduction in error using the waveform similarity
method. These results, obtained from smaller datasets than mining datasets,
required meticulous and time-consuming work by the authors, which is impractical in
the mining environment because of the manpower cost and the large number of
events involved. What this project has achieved is a 2- to 5-fold reduction in location
errors when compared with absolute locations, depending on circumstances such as
the validity of velocity models, and the accuracy of master event locations. Source
parameter estimation errors are reduced proportionately using the standard
25
seismogram-source parameter relationships in seismology. Errors should be further
reduced when geophone misalignments and site effects have been quantified and
corrected.
The achievement of 2- to 5-fold location error reduction has been balanced by
another, which is to obtain improved results with minimum operator input and no
extra cost, a far more difficult task than had been anticipated in the proposal. As
experience with the software grows, and mine-wide velocity models improve with
implementation of the software, it is anticipated that the 10-fold reductions in location
error will become routine within five years.
Finally, the automatic location algorithm, combined with an automated relative
location algorithm should be able to produce seismic data of a consistently good
quality. This will meet another goal, namely to reduce operator workloads, freeing up
time for data analysis and interpretation, at no extra cost. This will have significant
effects on safety, management of seismicity, and future research projects, while at
the same time making viable seismic prediction a real possibility.
5 Recommendations for future work
Since this project is not considered complete, there are several recommendations for
future work, listed below.
1. Annually audit the level of industry implementation of the outputs from this
project;
2. Automate the P- and S-pick inconsistency algorithm so that it can be easily
and quickly applied to large datasets;
3. Couple the automated P- and S-pick inconsistency algorithm with the
automatic location algorithm to eliminate inconsistencies in all automatic
locations, thereby improving the data;
4. Build and maintain refined velocity models of all rockmass volumes currently
monitored in the mining industry;
5. Develop and automate a reliable, unbiased, measure of relocation quality;
6. Continue with practical site characteristic determination methods, including
geophone/rockmass coupling, and accurate geophone orientation, together
with automatic compensation methods for seismograms when orientation
errors etc., have been determined.
In the case of the relocation quality measure, development is almost non-existent
both in earthquake and mining seismology. During this project two methods were
developed, and a third proposed, namely:
1. Comparing relocated blast data with the actual blast locations (this could be
improved by introducing a system trigger so that not only blast location but
time are available as master event data)
2. Cichowic’s relocation stability criterion (see Appendix II)
3. Spottiswoode and Linzer’s seismic cluster dimension criterion (does the
cluster form and position conform with known geological or mining features?).
New work should investigate the reliability of the three methods outlined above, and
then to develop towards a reliable relocation quality measure. Once these goals
have been met, other research such as the relationship between geological features
and mining induced seismicity, and seismic prediction could be more effectively
researched and developed.
26
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