bug groveling

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A SOFTWARE PACKAGE TO ANALYZE
SEISMICITY: ZMAP
Stefan Wiemer
S E I S M O L O G I S T
ELECTRONIC SEISMOLOGIST
Institute of Geophysics
ETH Hoenggerberg
CH-8093, Zurich
Switzerland
Telephone +41 633 6625
stefan@seismo.ifg.ethz.ch
Steve Malone
E-mail: steve@geophys.washington.edu
Geophyics, Box 351650
University of Washington
Seattle, WA 98195
Telephone: (206) 685-3811
Fax: (206) 543-0489
The Electronic Seismologist (ES) has been known to actually
do some research in the field of seismology from time to
time. As an operator of a seismic monitoring network the
research done often is related to the seismicity of the monitored region. Detecting changes or trends in seismicity is relevant to earthquake and volcano hazards; but are the trends
detected real or only an artifact of changes in the network
operating parameters? Because all seismic networks evolve,
change staff, change software and hardware, there is always
the nagging feeling, if not outright knowledge, that interesting patterns in the catalog reflect network changes rather
than changes in the Earth. How can one tell the difference?
The ES is happy to report that there is a handy-dandy
software package ideally suited to answering exactly this
question (and many others). ZMAP, developed by Stefan
Wiemer, allows the user to examine an earthquake catalog
from many different angles. Not only does it include the traditional map, cross-section, and time sequence parameters,
but also several others, such as event size and mechanism.
These can be combined in interesting ways to present the
user with different “views” into the data. Considerable seismological acumen lies behind the use and presentation of
these parameters, which helps the user get the most out of
the analyzed catalog. ZMAP is fairly intuitive to use and produces attractive output. In fact, the ES actually has fun
“playing” with it and gets useful results besides. Perhaps one
of the best ways to get a sense of how ZMAP might be used
is to take a tour of case studies. The following includes many
examples, and if they’re not enough there are a slew of references where one can find more. In his traditional groveling
way the ES has prevailed on Stefan Wiemer to write this
month’s column for him.
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Seismological Research Letters
Volume 72, Number 2
Introduction
Earthquake catalogs are probably the most fundamental
products of seismology and remain arguably the most useful
for tectonic studies. Modern seismograph networks can locate
up to 100,000 earthquakes annually, providing a continuous
and sometime overwhelming stream of data. ZMAP is a set of
tools driven by a graphical user interface (GUI), designed to
help seismologists analyze catalog data. ZMAP is primarily a
research tool suited to the evaluation of catalog quality and to
addressing specific hypotheses; however, it can also be useful
in routine network operations. Roughly 100 scientists worldwide have used the software at least occasionally. About 30
peer-reviewed publications have made use of ZMAP. A comprehensive listing of ZMAP features is given in Table 1.
ZMAP was first published in 1994 and has continued to
grow over the past seven years. Concurrent with this article,
we are releasing ZMAP v. 6, which contains numerous bug
fixes and a few new features, as well an updated manual.
This paper illustrates some of the various capabilities
and applications of ZMAP by summarizing a few case studies
that have been published previously. The examples include
(1) catalog quality assessment and data exploration; (2) mapping b values beneath a volcano to infer information about
the location of magma; (3) estimating seismicity rate changes
caused by a large earthquake; (4) stress-tensor inversion on a
grid to measure the heterogeneity of a stress field; and (5)
mapping the magnitude of complete reporting.
The Philosophy of ZMAP
Matlab-based, open-source code. ZMAP is written in
Mathworks’ (http://www.mathworks.com) commercial software language, Matlab®, a package widely used among
researchers in the natural sciences. Users must purchase a
Matlab license to run ZMAP. Although ZMAP is written in
Matlab, no knowledge of the Matlab language is needed
since ZMAP is GUI-driven. The ZMAP code is, however,
open, and users are welcome to modify or supplement as
desired by diving into the guts of the numerous scripts
(about 80,000 lines of native code in 600 scripts). ZMAP
should run on all platforms supported by Matlab. We have
tested it under Unix, Linux, PC, DEC ALPHA, and Macintosh computers (Caveat: Some code, such as stress-tensor
inversions, requires the compilation of external FORTRAN
or C programs).
March/April 2001
TABLE 1
Comprehensive Listing of ZMAP Functions and Relevant References
Tool
Objective
Comments and References
Histograms
Histograms of magnitude, depth, time, hour of the day
Data Import
Data import as ASCII, column-separated files, using one of several existing input format filters or a custom-designed one.
Catalog Comparison
Identification of identical events in two catalogs spanning the same
region. Plot of the mean difference in magnitude, depth, location, and
temporal evolution of these differences. Map of hypocenter shifts.
Time Series Analysis
Cumulative number of events, time-depth plots, time-magnitude plots,
cumulative moment release. Significance of rate changes using z, ß, and
translation into probability.
Data Subset Selection
Select data inside or outside polygons, cut in magnitude, depths, or time.
Maps
Maps of seismicity; legend by time, depth, or magnitude. 3D view and
rotation hypocenters. Cross-sections with one or multiple segments.
Link to M_Map toolbox. Importing an plotting topography files
(ETOPO5, ETOP2, GTOPO30, USGS 1deg). Importing hierarchical coastline data.
GENAS
Evaluating homogeneity of magnitude reporting with time. Compute
magnitude signatures; compare FMS for two periods and model rate
changes.
(Habermann, 1983, 1986, 1987; Zuñiga and
Wiemer, 1999; Zuñiga and Wyss, 1995)
Declustering
Separation of dependent and independent seismicity, identification of
clusters. Based on Reasenberg’s algorithm.
(Reasenberg, 1985)
Mapping Seismicity Rates Map seismicity rates in map view, cross-section, or 3D. Animate maps of (Maeda and Wiemer, 1999; Wiemer and Wyss,
z and ß values as a function of time. Compute alarm cubes, explore 6D 1994; Wyss et al., 1996; Wyss et al., 1997a;
parameter space,
Wyss and Wiemer, 1997, 2000; Wyss and Martyrosian, 1998)
Aftershock Decay Rates
Estimate aftershock decay rates based on modified Omori law. Compute (Kisslinger and Jones, 1991; Reasenberg and
probabilistic aftershock hazard. Compute maps and cross-section of p Jones, 1989, 1990; Wiemer, 2000; Wiemer et
values and aftershock probabilities. Link to ASPAR software.
al., 2001)
Frequency-magnitude Dis- Estimating a and b values and uncertainties using maximum likelihood (Wiemer and Benoit, 1996; Wiemer and
tribution
or weighted least squares as a function of depth, time, and magnitude. McNutt, 1997; Wiemer and Wyss, 1997; Wyss
Map b and a values in map view, cross-section, or 3D. Compute local et al., 1997b)
recurrence time maps. Differential b value maps for two periods. Create
synthetic catalog with constant b.
Magnitude of Completeness
Estimate magnitude of completeness based on the deviation of the FMD (Wiemer and Wyss, 2000)
from a power law. Analyze Mc as a function of time or depth. Map Mc in
map view or cross-section.
Fractal Dimension
Compute the fractal dimension of hypocenters based on the correlation
integral. Create maps and cross-sections of the fractal dimension.
Quarry Maps
Compute and map out the daytime to nighttime ratio of events in order to (Wiemer and Baer, 2000)
identify explosion. Dequarry catalogs by removing daytime events at significantly anomalous nodes.
Time to Failure
Estimate the time to failure based on accelerated moment release or
Benioff strain.
Stress Tensor
Inversion for the best fitting stress tensor using Michael’s or Gephart’s External call, requires compilation of FORTRAN
approach. Uncertainty estimation. Maps/cross-sections of stress orien- and C code. (Gephart, 1990a; Michael, 1984;
tation and variance/heterogeneity of the stress field. Maps of the tempo- Wiemer et al., 2001)
ral change in the stress field.
Cumulative Misfit
Compute the cumulative misfit to a predefined stress tensor. Cumulative External call, requires compilation of FORTRAN
misfit as a function of time, depth, magnitude, lat, lon, or in map view or and C code. (Lu et al., 1997; Wyss and Lu,
cross-section.
1995)
Seismological Research Letters
(Sammis et al., 2001)
(Bufe et al., 1994; Bufe and Varnes, 1996;
Jaume and Sykes, 1999; Varnes, 1989)
Volume 72, Number 2
March/April 2001
375
▲ Figure 1. Snapshots of some ZMAP windows. The upper left frame shows the cumulative number of events (0 < M<1.2; thick line) for the creeping
section of the San Andreas Fault north of Parkfield. The thin line is the z value, which measures the significance of a seismicity rate change. Note the
decrease in rate around 1995. The lower left shows catalog completeness, Mc, as a function of time, computed for overlapping windows each containing
1,000 earthquakes. The upper right shows the annual rate of earthquakes as a function of magnitude. Rates are computed based on the periods 1990–1995
(“o”) and 1995–2000 (“x”). Note the decrease in the detection ability for M < 1.2 after 1995. The top frame is the cumulative, the middle frame the noncumulative form. The bottom frame shows the magnitude signature. The lower right window plots a histogram of hypocentral depth.
Interactive data exploration. ZMAP combines many standard and advanced seismological analysis tools, aspiring to
make data exploration easier and more efficient. The user
can quickly select subsets in space, time, and magnitude,
plot histograms, compute b or p values, compare the frequency-magnitude distributions of different time periods
and locations, compare daytime versus nighttime activity,
compute the fractal dimension of hypocenters, create crosssections, overlay topography, compute stress-tensor inversions, and much more (Table 1). The ability to apply and
combine these analysis tools within one software platform
helps users explore or mine their data in detail. A typical
snapshot of some ZMAP windows is shown in Figure 1.
Mapping seismicity parameters. Identifying and evaluating
spatial and temporal variations in seismicity is one of the primary research objectives of ZMAP. By creating dense spatial
grids and sampling overlapping volumes of circular (2D) or
spherical shape (3D), users can map such parameters as seismicity rate changes, b values, p values, stress-tensor orienta376
Seismological Research Letters
Volume 72, Number 2
tions, and the magnitude of completeness. In any map, the
user can interactively view the source of the parameter under
investigation (e.g., a frequency-magnitude plot) and compare neighboring volumes.
Maps are computed on an interactively defined grid that
generally excludes low-seismicity areas (Figure 2B). There are
two methods programmed into ZMAP to map seismicity:
using either constant radii or a constant number of samples.
The first method produces maps with a continuous spatial
resolution but varying sample sizes. Consequently, uncertainties can vary significantly in space. A constant sample
size, on the other hand, results in more homogeneous uncertainties, but the resolution, which is inversely proportional
to the density of earthquakes, will vary across the region of
interest. This is demonstrated in Figure 2B, where we plot a
cross-sectional view of the hypocenters beneath Mt. St.
Helens. Circles plotted at selected nodes indicate the volumes sampled around each particular node. The grid spacing
is generally chosen such that the volumes overlap significantly, providing a natural smoothing of the results.
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0
A
B
C
1
2
Depth [km]
3
4
5
6
7
8
9
0.5
1
b-value
1.5
2
3
4
Distance [km]
▲ Figure 2. (A) The b value as a function of depth at Mount St. Helens. The seismicity for the period 1987–1995 with M > 0.3 was analyzed, using a
sliding window of 100 earthquakes. Vertical bars indicate the uncertainty in b, horizontal bars the depth range sampled. (B) Cross-sectional view (northsouth) through Mount St. Helens. Crosses mark the locations of nodes of an interactively selected grid (spaced at 0.2 × 0.2 km) used to compute the bvalue image shown in (C). For selected nodes, the circles mark the volumes sampled, each containing N = 100 earthquakes. (C) Image of the b-value distribution underneath Mount St. Helens, computed using the grid shown in (B). Dark colors indicate low b values.
Sample Applications
The sample applications shown below are intended to illustrate some of the capabilities of ZMAP. The images shown
were all created with ZMAP, edited manually using the Matlab edit capabilities, and then imported as JPEG files or
Windows metafiles into PowerPoint to be arranged on a
page. The online help (http://www.seismo.ethz.ch/staff/stefan/) discusses in detail how each analysis was performed.
Each case study is taken from published work that discusses
the science and interpretation in detail.
Assessing Catalog Homogeneity and Interactive Data
Exploration
ZMAP can be used to investigate or monitor the reporting
history and health of a seismic network. The user can address
questions such as: Did the detection threshold change in a
particular area at a certain time? Did the meaning of magnitude change? A long list of man-made changes in earthquake
catalogs has by now been documented (Habermann, 1983,
1986, 1987, 1991; Wyss and Toya, 2000; Zuñiga and
Wiemer, 1999; Zuñiga and Wyss, 1995). These changes in
the reporting rate can be introduced by modifications to the
network and can either mask or mimic natural changes in
the seismicity. Using GENAS (investigation of rate changes
as a function of magnitude threshold), magnitude signa-
tures, b-value curves, and maps of rate changes one can
attempt to unravel the reporting history of earthquake catalogs as a function of space and time.
A simple example of network quality assessment is
shown in Figure 1. The cumulative number of events along
the creeping section of the San Andreas Fault north of Parkfield (0 < M < 1.2) indicates a decrease in the rate of small
earthquakes around 1995. The cumulative and noncumulative number of events as a function of magnitude is compared for two periods (1990–1995 and 1995–2000). This
plot reveals that the number of events with M < 1.2 dropped
by about 65% in the latter period, whereas no change is
observed for larger earthquakes. The simplest explanation of
this pattern is that there was a change in the network configuration or processing strategy which decreased the detection
ability of the CALNET network in the creeping section after
1995.
The b Value beneath Mount St. Helens
ZMAP is frequently used to facilitate spatial mapping of the
b value in various seismotectonic regimes. The b value,
defined as log10N = a – bM, where N is the cumulative number of earthquakes, and a and b are constants related to the
activity and earthquake size distribution, respectively
(Gutenberg and Richter, 1944; Ishimoto and Iida, 1939),
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377
has been shown to vary spatially on scales of hundreds of
meters to tens of kilometers (e.g., Wiemer and Benoit, 1996;
Wiemer and Katsumata, 1999; Wiemer and McNutt, 1997;
Wiemer et al., 1998; Wiemer and Wyss, 1997; Wyss et al.,
2000). These variations are related to differences in stress,
pore pressure, and material heterogeneity and therefore can
give important constraints when analyzing the seismotectonics and hazard potential of a region. High b values are often
correlated with the presence of magma in volcanic regions
(Jolly and McNutt, 1999; Murru et al., 1999; Power et al.,
1995; Wiemer and McNutt, 1997; Wiemer et al., 1998;
Wyss et al., 1997b). We present as an example data from
Mount St. Helens (Wiemer and McNutt, 1997), using
earthquakes of magnitude 0.4 and greater recorded by the
local network during the period of 1988–1995, a total of
about 2,000 events.
Using ZMAP, we can investigate spatial variations in b
value in one, two, and three dimensions. Looking at b values
as a function of depth (Figure 2A), we find high values of b
(b > 1.1) at around 2.5 km and deeper than 6 km below sea
level. For this analysis, a constant number of events per sample (100) is used, incremented downward by 25 events for
each step. The two-dimensional gridding along a 2-kmwide, north-south-trending cross-section (Figure 2B) shows
that indeed the b value exhibits its strongest variations as a
function of depth. The orientations of the cross-section and
the hypocenters are shown in Plate 1A. Finally, a threedimensional gridding is applied and a perspective view of the
topography of Mt. St. Helens added (Plate 1A). For this particular case study, the 3D view contributes little to the scientific analysis of the data, since the seismicity distribution is
largely one-dimensional. Creating an artistic image such as
Plate 1A often requires some effort using the editing options
in Matlab in order to get the perspective and the light properties right; however, the outcome may be worth the effort.
To verify that the mapped differences in b value are indeed
significant, we plot in Figure 3A comparisons of b values for
the shallowest earthquakes (b = 0.77) and the depth range 2–
3 km (b = 1.82). The difference in the frequency-magnitude
distributions is clear to the eye and highly statistically significant, which is established using a statistical test proposed by
Utsu (1992).
The scientific interpretation of these results, of course,
still depends on the ingenuity of the analyst. Based on the
analysis of the b-value at Mt. St. Helens and nine other volcanoes (Jolly and McNutt, 1999; Murru et al., 1999; Power
et al., 1998; Wiemer and McNutt, 1997; Wiemer et al.,
1998; Wyss et al., 1997b; Wyss et al., 2000), we have proposed that (1) the b value underneath volcanoes is not generally higher, but pockets of high b exist in otherwise quite
normal crust. (2) These pockets of high b may signal the
presence of magma, since in the vicinity of a substantial body
of magma, high pore pressure, high temperature gradients,
and high b values all favor high b values. The absence of high
b values, on the other hand, should be taken as a strong indication that no substantial magma body is present near this
volume.
Mapping Seismicity Rate Changes
Measuring changes in the seismicity rate is a tricky business.
It is important, because rate changes are believed to be
directly related to changes in stress or pore pressure (Dieterich, 1994; Dieterich and Okubo, 1996). Applications
include constraining stress changes caused by Coulomb failure (Harris, 1998; Stein et al., 1992) or precursory rate
changes (Katsumata and Kasahara, 1996; Maeda and
Wiemer, 1999; Wiemer and Wyss, 1994; Wyss and Habermann, 1988; Wyss and Martyrosian, 1998; Wyss and
Wiemer, 1997). Measuring rate changes is difficult because
(1) artificially introduced rate changes are common in seismicity rates, (2) aftershocks and other clustered events
should be excluded before measuring background rates, and
(3) defining the significance of an observed rate change is not
simple.
ZMAP helps in various ways to deal with each of these
obstacles. As an example, we investigate the change in the
seismicity rates in southern California associated with the
1992 M 7.3 Landers earthquake. For details, please refer to
Wyss and Wiemer (2000). The first task is preparing a
homogeneous input data set. We spatially map the magnitude of complete reporting, Mc, for different periods. Areas
with higher Mc, such as the offshore region and south of the
Mexican border, can thus be excluded based on an objective
criterion. We next test for the presence of explosions in the
Plate 1. (A) Left: Cross-section view through Mount St. Helens, overlain by topography. The orientation of the cross-section is shown in the inset at
lower left. Hypocenters are color-coded by depth; symbol size indicates magnitude. Right: Three-dimensional image of the b values beneath Mount St.
Helens, based on the seismicity from 1987–1995. Red colors indicate high b values. Horizontal planes are drawn at 8 and 3 km depths. (B) Perspective
view of southern California, centered on the Landers region. Colors map the change in the seismicity rate between the periods 1985–1992.48 and 1992.5–
1999.7. Red colors, or negative z values, indicate an increase in the seismicity rate in the latter periods and vice versa. Triangles mark the epicenters of
the Landers, Big Bear, and Hector Mine main shocks. (C) Map of southern California, centered on the Landers region. Bars indicate the orientation of the
stress field obtained by inverting the 100 focal mechanisms nearest to each node of a grid spaced 2 × 2 km. The period investigated is 1992–2000. Stars
mark the hypocenters of the 1992 Landers and 1999 Hector Mine main shocks. The variance of the individual stress tensor inversions is color-coded, with
blue to purple colors indicating high variance, hence a heterogeneous stress field. The two insets show individual stress-tensor inversions and their
uncertainties, obtained using a bootstrap method (yellow: σ1; red: σ2; blue: σ3). (D) Map of the western U.S.; the magnitude of complete reporting, Mc,
computed by measuring the deviation from an assumed power law, is color-coed. The inset shows the frequency-magnitude plots for two subvolumes
marked A and B.
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(A) Mt. St. Helens b-values
(B) Landers Rate Changes
Hector Mine
Big Bear
N
Rate
decrease
Landers
Z-value
Rate
increase
(C) Stress Tensor Orientation
(D) Magnitude of Completeness
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1000
p = 1.4e–010
900
800
Cumulative Number
Cumulative Number
10 2
b = 0.77 ± 0.17
10 1
500
400
200
0
0.5
1
1.5
2
2.5
3
100
Magnitude
▲ Figure 3. Comparison of the cumulative frequency-magnitude distribution for shallow earthquakes at Mount St. Helens (filled circles) and for
the depth range 2–3 km (open squares). The probability that the two samples come from the same population is about 1.4–10, based on Utsu’s
(1992) test.
study region by spatially mapping the daytime to nighttime
ratio of events. A significantly enhanced ratio is indicative of
quarry blast contamination that often remains in the data
regardless of the network operator’s efforts (Wiemer and
Baer, 2000). We identify a number of explosion-prone
regions, which we exclude. By studying the homogeneity of
reporting as a function of time and magnitude, we search for
artificial rate changes in the data and the suitable overall Mc
cut-off. Finally we settle for a data set for the period 1985–
1999.8 (before the Hector Mine earthquake) with an overall
Mc of 1.7. This data set is then declustered using Reasenberg´s (1985) approach.
We spatially map the remaining seismicity rate changes,
comparing the periods 1985–1992.48 and 1992.6–1999.8,
using constant sample volumes of 20 km radius and a grid
spacing of 5 km. Two different statistical functions have been
implemented in ZMAP to measure the significance of rate
change: z values (Habermann, 1981, 1988) and ß values
(Matthews and Reasenberg, 1988; Reasenberg and Simpson,
1992). A map of rate changes measured by z is shown in
Plate 1B. Red colors signify rate increases, blue colors rate
decreases. The map is wrapped on top of the GTOPO30
topography; this is possible in ZMAP only when the Matlab
mapping toolbox is available. We can interactively select circular or polygonal volumes from the map and view the
cumulative number of events as a function of time and the z
values that measure rate changes (Figure 4).
The pattern of rate change mapped by this technique is
quite remarkable, since it reveals long-range (> 100 km) and
long-duration (> 7 years) rate changes associated with the
1992 Landers main shock. The pattern of increased and
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600
300
b =-1.82 +/- 0.18
10 0
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1980
1985
1990
1995
2000
Time in years
▲ Figure 4. Cumulative number of earthquakes above M 1.7 south of the
Hector Mine hypocenter. In this volume, the seismicity rate dropped drastically after the 1992 Landers earthquake. The thin line indicates the significance of rate changes, measured using the z value.
decreased seismicity matches qualitatively the predicted rate
changes caused by the combined static and dynamic stress
changes predicted for the Landers rupture (Stein et al.,
1992). In order to establish a significant rate decrease, it is
generally necessary to compare observations for several years.
Stress Tensor Inversions
In addition to hypocenter information, ZMAP can be used
to analyze focal mechanism data either by analyzing the
cumulative misfit of a set of focal mechanisms to a given
stress tensor (Lu and Wyss, 1996; Lu et al., 1997; Wyss and
Lu, 1995), or by computing inversions for the best-fitting
stress tensor. Two inversion programs are called from ZMAP,
Michael’s (Michael, 1984, 1987a, 1987b, 1991; Michael et
al.) and Gephart’s (Gephart and Forsyth, 1984; Gephart,
1990a; Gephart, 1990b). ZMAP allows the computation of
individual stress-tensor inversions, stress tensor as a function
of time and depth, and inversions on a grid in either map
view of cross-section (using Michael’s method only).
An example application, again for southern California,
is shown in Plate 1C. We use the relocated set of focal mechanisms from 1992.48–2000.5 by Hauksson (2000), with a
solution misfit < 0.1. First we create a grid with a 2 × 2 km
spacing, excluding areas of low seismicity. The nearest 100
earthquakes to each node are sampled and their focal mechanisms inverted using Michael’s approach. The resulting
directions of the principal stress axes, σ1, are plotted as lines
on a map underlain by topography. We further color-code
the variance of the resulting inversion at each node. Blue to
purple colors indicate a high variance (i.e., heterogeneous
March/April 2001
stress field). The two inserts show the individual inversion
results and their uncertainties, obtained using a bootstrapping approach (Michael, 1987a).
The overall stress directions obtained agree reasonably
well with a more detailed study by Hauksson (1994). Results
suggest that areas that experience a high slip during the main
shock show a more heterogeneous stress field which cannot
be fit by a single stress tensor, whereas areas outside the main
rupture show a low variance, hence a more homogeneous
stress field (Wiemer et al., 2001).
occurrence of errors. Although the source code is open, it is
not trivial to find the appropriate script and variable in order
to extend or improve ZMAP.
As with any software, the garbage in-garbage out principle applies to ZMAP. If you try, for example, to estimate spatial and temporal variations of b values and your catalog
contains only 200 events, you may get colorful maps but
their meaning is questionable at best.
Mapping Minimum Magnitude of Completeness (Mc)
The quality of all regional and local earthquake catalogs
decreases with distance from the center of the network.
Obvious boundaries of deterioration are coastlines, international borders, and seams between networks. To avoid problems that could be introduced in seismicity studies by
heterogeneity of Mc, ZMAP allows the user to map Mc to
define the spatial extent of the high-quality part of the catalog (e.g., Wiemer and Wyss, 2000). The technique used most
frequently to assess Mc is based on estimating it from the
FMD itself. This is often done in seismicity studies by visual
examination of the cumulative or noncumulative FMD;
however, we prefer to apply a quantitative criterion, where
we measure the goodness of fit to an assumed power law
(Wiemer and Wyss, 2000). An example of a map of Mc for
the western U.S., based on the CNSS catalog for the period
1995–2000, is shown in Plate 1D. Mc ranges from > 2.5 offshore Mendocino to < 1 in central California.
The future of ZMAP is somewhat unclear. There will likely
be occasional future updates of ZMAP, largely driven by
research interests. New features that have been partially
implemented or are being considered are:
OBTAINING ZMAP, DOCUMENTATION, AND
SUPPORT
THE FUTURE OF ZMAP
• Probabilistic hazard mapping, both in a Poissonian
(Frankel, 1995) (Bender and Perkins, 1987) or timedependent fashion. We are developing a module based
on ZMAP that will compute probabilistic aftershock
and foreshock hazard maps (Wiemer, 2000) in near-real
time and display the results on the Internet.
• Implementation of the M8 algorithm for earthquake
prediction (Kossobokov et al., 1997).
• A different declustering algorithm based on the ETAS
model (Ogata et al., 1995, 1996).
• A real-time module to monitor the quality of seismicity
data and search for artifacts in reporting.
• Computing Coulomb stress changes with uncertainties
and comparison with observed rate changes.
Suggestions for future developments and criticisms of the
existing package are highly encouraged!
ZMAP is freely available on the Internet. Please refer to
http://www.seismo.ifg.ethz/staff/stefan to download the current version of ZMAP (version 6). The compressed files are
about 5 Mb and should run under Matlab 5.x and 6.0.
Other resources on the ZMAP home page include a list of
papers published using ZMAP, a collection of sample data
files, and a collection of presentations made using the ZMAP
software. If your Internet connection does not allow downloading via the Internet, we can send you a CD-ROM version of ZMAP. Please contact stefan@seismo.ifg.ethz.ch.
The only support currently available beyond the online
documentation is contacting me via e-mail. Help requests
will be addressed as quickly as possible, but as they increase
in volume this may become unmanageable. A ZMAP help email list is being considered.
KNOWN PROBLEMS
From the responses from the 100+ scientists using ZMAP, it
is clear that, although designed to work on any Matlab-supported platform, some users experience problems while running various functions. Others become frustrated with the
variable robustness of certain features of ZMAP and the
ACKNOWLEDGMENTS
The author would like to thank Matt Gerstenberger, Steve
Malone, Charlotte Rowe, and Max Wyss for comments and
suggestions that greatly helped to improve the manuscript. I
am deeply indebted to all those who helped through their
programming to make ZMAP a better tool: Alexander Allman, Denise Bachmann, Matt Gerstenberger, Zhong Lu,
Francesco Pacchiani, Yuzo Toda, and Ramon Zuñiga. Special
thanks to Max Wyss, whose relentless support and creative
ideas over the past eight years has made ZMAP possible. The
support from an IASPEI PC software development grant has
been a great motivation. I am thankful to the University of
Alaska Fairbanks, the Science and Technology Agency of
Japan, and ETH Zurich for supporting the development of
ZMAP.
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SRL encourages guest columnists to contribute to the “Electronic Seismologist.” Please contact Steve Malone with your
ideas. His e-mail address is steve@geophys.washington.edu.
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