Final Report:

Seismic Monitoring Measurements of

Nonfragmenting Explosive Tests

at the Glendora Test Facility, NSWCC

by

Professor Gary L. Pavlis, Ph.D.

Department of Geological Sciences

Indiana University

Bloomington, IN 47408

Prepared for:

Mark Anderson

Comarco Systems Inc.

Route 6, Box 28

Bloomfield, IN 47424

Subcontract 0002-3 of Navy Contract N00164-98-D-0008

March 31, 1999

Table of Contents

Organization of document	1

Executive Summary	2

Data Collection	2

Indiana Blasting Regulations	2

Fundamentals of Wave Propagation	4

Analysis	8

Underground Mine Hazard Assessment for Glendora	12

Reference Books on Seismic Wave Propagation	13

Technical Report	17

Introduction	17

Data Collection	17

Data Analysis	19

Raw Data Processing	19

Amplitude Measurements	20

Deriving a form for the amplitude scaling relation	23

Separation of yield and depth effecs from geometric spreading	28

Estimation of the shot size-depth scaling coefficients	36

Determination of the distance decay constants	37

Scientific Problems Identified from these Data	41

Data Availability	41

List of References	41

Appendix A: Record Sections	43

Appendix B: Measured P and S amplitudes	84

Organization of Document

This document is organized in three parts. The executive summary is a less technical description of the results of extensive seismic measurements made during nonfragmenting explosion tests conducted at the Glendora Lake Facility on September 10-11, 1998. This section of the document is intended for the nontechnical audience interested primarily in the conclusions of the study. The technical report section that follows outlines the details of the analysis I conducted on these data. It provides the background that justify the conclusions reached in the executive summary. The reader should note that some background sections are repeated in the Executive Summary and Technical Report sections on the assumption that each of these sections may be copied and distributed to different audiences. Finally, two appendices are included that graphically show the majority of the data. This was done to more fully document the actual data used the analysis.

Executive Summary

Data Collection

Seismic ground motion measurements were made during nonfragmenting explosion tests conducted at the Glendora Lake Facility, NSWCC, on September 10-11, 1998. To make the measurements we utilized 102 digital recorders equipped with 4.5 Hz natural period seismometers. This instrumentation was borrowed from a national facility administered by the Incorporated Research Institutions for Seismology, a nonprofit corporation that is a consortium of over 80 universities with active research programs in seismology with primary support from the National Science Foundation. These instruments were deployed on a total of 174 points on the ground within a 3000 meter radius of the shot points (Figure 1). 63 of these stations were operated in fixed, linear profiles directed radially away from the shot points in three different directions. These stations had a nominal station spacing of 75 m. Four additional instruments were equipped with strong-motion accelerometers. These were deployed near the foundations of buildings 8000, 8001, and 8002 and in the southwest corner of the floating building I will refer to in this report as the "Barge".

The remaining 105 points shown in Figure 1 form three small clusters of stations labeled "Array 1", "Array 2", and "Array 3". These arrays were formed by utilizing 35 instruments that were moved during the course of the experiment. The numbers indicate the deployment sequence. Array 1 recorded the first four shots, array 2 recorded the final two shots on September 10, and array 3 ran for all of September 11. These arrays were designed to examine wave propagation effects in the Glendora region in an attempt to better quantify the variability of seismic wave propagation in different directions at the site. Arrays 1 and 2 were designed to understand surface wave interactions with the edges of the mined out region and array 3 was designed to investigate possible site amplification at the top of the highwall pit on which building 8000 is situated.

The locations of all these stations were determined to a precision of approximately 1 cm using a Real-time Kinematic Global Positioning System owned by the Indiana University Department of Geological Sciences. We also used this equipment to precisely locate the shot point, the relative positions of survey points on most of the structures currently present at the Glendora Lake Facility, and a digital outline of the lake itself. The later was accomplished by putting the GPS system in a continuous acquisition mode and driving a boat around the shoreline. These data were sent to personnel at Comarco and the Glendora Lake facility several weeks ago via electronic mail along with diagrams showing the survey point locations relative to buildings of the facility.

Indiana Blasting Regulations

The key question for I was asked to address in this report was how measured ground motions during this experiment relate to established ground motion limits imposed by surface mining laws. The 1998 Indiana Administrative Code references section 310 IAC 12-5-36 on this issue. This section is titled: "Surface mining: explosives; blasting operation". The IAC states::

(2) Maximum peak particle velocity.

(i) The maximum ground vibration, measured as peak particle velocity, shall not exceed the following limits at the location of any dwelling, public building, school, church, or community or institutional building:

Distance (D) from the

blasting site (feet)	Maximum allowable peak particle

velocity (Vmax) for ground

vibration (inch/second)	Scaled-distance factor (Ds)

to t applied without seismic

monitoring
0 to 300	1.25	50
301 to 5000	1.00	55
5001 and beyond	0.75	65

The nearest receiver we had to any shot was approximately 100 m from the shot point, so the first number in the table is largely irrelevant for Glendora. Consequently, the legal limit is 1 inch/second for all the data we recorded. All our analysis software utilizes the metric system, so for reference this limit is 2.54 cm/s.

Fundamentals of Seismic Wave Propagation

Before discussing the results of our measurements, I believe it will be useful to educate my readers on first-order principles of seismic wave propagation in the earth. For a more on this subject the reader is referred to the list of reference books found at the end of the executive summary.

Seismic waves can be understood as sound waves that travel in the solid earth. They are sometimes also called "elastic waves" because they are a wave disturbance in which energy is exchanged between the potential energy of stored elastic strain in the material and kinetic energy of the motion of the medium. Sound in air is a type of seismic wave equivalent to a mode of propagation called P waves in seismology. Sound (P) waves are formed by compressions and rarefactions of the air (rock) that are driven by fluctuations in pressure inside the air (rock). Solid materials like rock, as opposed to air which is a fluid, can support a second mode of propagation seismologists call a shear (S) wave. Shear waves in a solid exist because the material can support shearing forces. (A fluid, in contrast, cannot support shearing forces and will simply flow in response to shearing motions.) This produces a second mode of propagation in which the motion of the material is transverse to the direction of propagation. (P waves, like sound, are longitudinal waves meaning the particle motions are parallel to the direction of propagation.) It so happens that P waves always propagate faster than S waves. It should be noted that an explosion in water will theoretically only directly generate P waves as a uniformly expanding bubble produces a pure compressional shock wave the expands outward as a pure P wave. S waves are generated only by scattering at the water bottom, or for shots near the bottom by direct interaction of the bubble with the bottom.

A third important mode of propagation for Glendora is a more abstract mode of propagation called a Rayleigh wave. Rayleigh waves are a type of "surface wave" that are most appropriately viewed as a waveguide mode formed by energy trapped in the near- surface. That is, in the same way that pressure in the ocean increases dramatically with depth, confining pressure in rock also increases rapidly with depth. The increase in pressure with depth leads to a nearly universal tendency for both P and S waves velocities to increase with depth. This gradient in velocity causes waves to be turned back toward the surface leading to a waveguide effect in which waves mirror off the free surface of the earth and subsurface layering. Rayleigh waves are exceptionally prominent on much of the Glendora data because of an artificial waveguide produced over much of the area from surface mining. That is, the mining process turned originally intact sandstones and shales into pulverized rock fragments. This mining spoil defines the upper 100-200 feet of the subsurface in the area east of the highwall pit that defines most of Glendora Lake. This material has a much lower seismic velocity than the intact rock located below it. This creates a strong waveguide at this site leading to exceptionally high amplitude Rayleigh waves within the mined out area.

Figure 2 illustrates how these different wave modes appear in the Glendora data. The wiggly lines in this figure are seismograms that graph ground velocity in three cardinal directions (EHE = +east, EHN=+north, and EHZ=+down) as a function of time (time increases from left to right). In this figure the seismograms are all displayed at a common scale so relative amplitudes between components are preserved. P is the strong pulse at the beginning of the signal. Notice that the main motion of this pulse is in the vertical direction. S, in this case, is the first large pulse seen on the horizontal components. The Rayleigh waves form the complicated pattern of lower frequency signals outlined by the grey area in Figure 2. Notice that outside of these three "phases" there are many secondary pulses that in many cases have amplitudes comparable to the primary P and S phases. These secondary signals are various scattered phases reflected from a host of subsurface irregularities in the earth.

Well established theoretical models have two endmembers that describe the decay of seismic waves with distance. P and S mode amplitudes in an infinite, homogeneous medium are predicted to decay as 1/r where r is the distance from a point source. In contrast, a Rayleigh mode on an infinite half space is expected to decay as 1/r½. (see e.g. Aki and Richards, 1980) The real earth is more complex than either of these simple models, and several well established effects cause amplitudes to either fall off faster or slower than these simple models. Two effects can lead to slower rate of decay than these simple models.

1.	The surface of the earth is almost universally covered with a highly variable layer of weathered rock we commonly refer to as soil or just plain dirt. Unconsolidated materials like soil or the pulverized rock produced by surface mining at Glendora have seismic properties that are drastically different from intact rock. The lower rigidity of unconsolidated materials relative to rock are responsible for a universal phenomenon called site-amplification that is a pervasive theme of earthquake engineering. Site amplification occurs because energy that is generated by a seismic source (explosion or earthquake) propagates in the subsurface through intact rock that is a very rigid material. When this wave energy reaches the surface conservation of energy requires a dramatic increase in ground motion in the soil relative to the bedrock because of the soils lower rigidity. This phenomenon, however, is strongly frequency dependent and is only important for frequencies at which the seismic wavelength is of the same order of magnitude as thickness of the soil column. The practical importance of this for explosions at Glendora is that site effects have a very strong influence on amplitudes of signals for points that have been surface mined, but have a relatively minor influence on unmined areas. That is, amplitudes on areas that have been mined can be expected to show larger amplitudes than unmined areas. An exception, we will see, is the top of the highwall on which building 8000 is constructed. This experiment has quantified an effect that had previously only been predicted theoretically. That is, the free face of the highwall generates a large scale resonance of the edge that induces an amplification of about a factor of 2 in ground motion at building 8000.

2.	The tendency for seismic velocities to increase with depth in the earth that promotes Rayleigh wave propagation (see above) has a similar effect on P and S modes. The first-order effect of this gradient is to cause the earth to act like a large scale lens. A significant fraction of the energy generated by a seismic source is turned back toward the surface in wave propagation modes called head waves (see e.g. Aki and Richards, 1980). In addition, a significant fraction of the energy released by an explosion like those detonated in Glendora Lake are reflected off subsurface layering in the earth and returned as distinct echoes. Both head waves and reflections decay at faster rates than the primary P and S modes. They are relevant, however, because sometimes the forces of nature conspire against you. Secondary pulses are sometimes produced by constructive interference of one or more distinct modes of propagation that cause the largest observed amplitude to be something other than the one of the primary phases (P, S, or R).

On the flip side, several first-order effects tend to reduce seismic amplitudes more rapidly than that expected from the simple theoretical models.

•	Heterogeneities in the earth scatter seismic waves much like light is scattered in a fog. In the same way that light intensity falls off faster when it is foggy than on a clear day, variations in material properties in the earth cause seismic waves to be scattered. This takes on many forms that are topics of current research in seismology. Waves are back scattered from subsurface layering (this is the basis of all oil and gas exploration), waves are forward scattered from P to S waves and from S to P waves, and surface wave modes like R are scattered by topography and other lateral variations in the waveguide through which they propagate.

•	Seismic waves are attenuated by intrinsic attenuation effects. That is, all vibrations are damped by a variety of mechanisms. Intrinsic attention, however, has almost no relevance for the blasting activity at Glendora. The reason is that structures and property lines that define the safety limits of interest here are close enough to the shot point that attenuation effects can be neglected for any frequency of interest. For instance, at the peak frequency of all the observed explosions of 5-10 Hz, building 8003 is always less than one P wavelength from the shot point while 8000 is slightly more than 2 wavelengths away.

•	The R phase undergoes a phenomenon called dispersion that can be readily observed in the graphical display we call "record sections" found in Appendix A. That is, at stations located close to the shot point all three primary modes of propagation observed here (P, S, and R) all arrive within a few milliseconds of each other and are virtually inseparable. P propagates outward in a well defined packet that defines the burst of energy at the front of the record. The S phase behaves similarly, but it is less clear because of the interference of various scattered phases that arrive simultaneously. The R phase, in contrast, spreads out dramatically in time causing it to span several cycles of the dominant period within a few wavelengths from the source. This is the reason the Rayleigh wave shown in Figure 2 is not a compact pulse like P and S. This dispersion continues and at the most distant station it defines a packet of energy spanning nearly 2 seconds of time. This is caused by a well known waveguide effect that characterizes surface waves of a wide range of frequencies (e.g. Lay and Wallace, 1995, pp. 116-172). From a regulation perspective this can be viewed as a good thing. Although it appears all of these shots transmit a significant fraction of their energy into Rayleigh waves, the rapid dispersion of this phase causes the peak amplitude of the R phase to not be that large. Thus even though the R waves decay at a slower rate than P or S, dispersion generally overwhelms differences in amplitude falloff. The only possible exception are the nearest stations where the individual phases are essentially all interfering constructively and are difficult to separate. On the other hand, dispersion can significantly extend the time of large amplitude ground motion which could have consequences for damage to structures not accounted for by existing regulations.

Analysis

I measured P and S wave amplitudes for all of the seismic data recorded on the three linear profiles. This represents a total of 2005 measurements. This measurement is relatively simple and is illustrated for P and S in Figure 2. I focused on P wave amplitudes (1035 data points) rather than the "peak amplitude" as specified in the legal code for two basic reasons: (1) peak amplitudes are not quantitatively useful for comparing results from different shots or different stations because the largest amplitude point is variable, and (2) the potential bias in using P as a proxy for peak amplitude is small. Additional justification for this approach can be found in the technical report.

From measured amplitudes I sought to develop an empirical relation between measured P wave amplitudes, shot size, and shot depth. Through a lengthy averaging process described in detail in the technical report, I developed the following relationship that can be used to predict peak seismic ground motions at the Glendora Lake Facility:

where A is the predicted peak ground motion velocity in units of cm/s, R is the distance from shot to receiver point in meters, Y is the yield of the explosion in kilograms, and Z is the source depth in meters. Note also that the logarithms here are base 10, not natural logarithms. I also determined the uncertainty of this prediction. I found the residuals of measured P wave amplitudes relative to this formula were reasonably well approximated by a log normal distribution. With this assumption I estimate that the 95% confidence intervals on log A are ±0.242 (Note this is an uncertain in log A not A because the log of the amplitudes follow the standard "bell" curve, not the actual amplitudes.).

Figure 3 illustrates some of the implications of this formula graphically. The figure cannot show the whole story because A is a function of three variables: R, Y, and Z. I illustrate part of the effects of variation in yield and depth by showing the predicted average decay of amplitude with distance for two endmembers of the dataset: 20 lbs at 5 feet (the smallest amplitudes observed) and 100 lbs on the bottom of the lake (the largest amplitudes observed). The figure focuses on the decay of amplitudes with distance as this is the dominant, practical issue that follows from this formula. The primary conclusions I reach from this relationship and the original data from which it was derived is as follows:

No measured amplitude at any existing structure or outside the property lines of the Glendora Lake Facility came within an order of magnitude of the legal limit.

The largest measured amplitudes were about a factor of four times smaller than the legal limit. These were measured at stations on the shore of Glendora Lake directly north of the shot point and were recorded for the 100 lb bottom shots. The vast majority of the data were more than an order of magnitude below the legal limit.

The existing feature that would limit the size of shots that could be fired in Glendora Lake without exceeding the legal limit is building 8002 because it is located at a distance of only 271 meters from the shot point. Figure 4 shows an application of equation (1) to predict the maximum shot size limit imposed by seismic ground motion limits at building 8002 for various shot depths. I show two different curves: (a) the maximum shot size predicted from the mean curve defined by equation (1), and (b) a projection from the upper 95% confidence interval (Figure 3). The numbers are large in either case: thousands of kilograms. This projection is this large because of a combination of two factors: (a) the rapid decay of amplitude with distance causes a huge difference between peak ground motion recorded in our data the closest point on the shore (about 100 m) and building 8002 (271 meter); and (b) the approximate square root scaling with yield drastically increases the multiplier in the projection. As a result I conclude that seismic motion limits probably impose no practical limits on the size of explosions that might be detonated in Glendora Lake.

Dobrin (1976, p. 118) gives an empirical formula for the critical depth at which the bubble generated by an explosion would break the surface before beginning to oscillate.

where d is the critical depth (in feet) for a charge of size w in lbs of dynamite. This is an interesting relationship for Glendora as shots above this critical depth will transmit a large fraction of the energy released into the geyser that erupts after such a shot, and, more importantly, into air waves. Figure 4 plots the critical depth curve for the range of water depths spanned in these tests. (For reference note that the critical shot size for bottom shots at Glendora computed from equation (2) is 2300 lbs assuming d=50 feet.) The point to glean from this is that shots over a certain size would almost certainly generate large air blasts. Consequently, I would hypothesize that the true upper limit on the size of explosions that could be detonated are limited by two nonseismic problems: (a) air waves, and (b) the large volume of water that would be moved by big shots would probably cause large water waves that could be a serious safety hazard.

It is worth closing this section with a precautionary note. Figure 4 represents a large extrapolation of an empirical formula. Predictions made by an empirical formula outside the range of actual data is dubious at best. If additional testing of larger explosions ever takes place at Glendora, it would be prudent to a least monitor ground motions at buildings in order to test the measured motions against predictions based on equation (1). Furthermore, transport of an empirical formula like this to a different testing area is even more suspect. Local wave propagation effects and variations in coupling could easily lead to systematic differences of as much as an order of magnitude.

Underground Mine Hazard Assessment for Glendora

I was asked to make an assessment of hazards imposed by potential future explosive testing activity in speeding the natural process of the collapse of underground mines in the area. The first step in this process was assembly of data on the locations of underground mines near Glendora. Figure 5 was produced from data on all known underground and surface mines in Indiana. These data were recently assembled into a Geographical Information System database by the Indiana Geological Survey. Given that Glendora Lake is the final working face of an abandoned strip mine, it is not surprising that the area is surrounded by abandoned and active mines.

A completely accidental event allows me to say far more about this problem than I had expected would be possible. Our recording instruments were set up on what we call time triggers. This means they turned on for five minutes at 15 minute intervals during the working day. The final shot in the series at Glendora occurred late on Friday, September 11. We had set the recorders to continue recording through Saturday, September 12, 1998, in the event delays in the shooting occurred. We were unable to retrieve all our instruments before nightfall, so we returned on the morning of the 12th to pick up the rest of the equipment. We were lucky enough to record data from a mining explosion that was fired at approximately 11 am local time. I estimate this event was approximately 10-15 km southeast of Glendora. It may have been in the active mine in the lower right corner of Figure 5, although the data indicate it probably was somewhat further away. Figure 6 provides a useful perspective on ground motion produced by this mining explosion relative to that produced an the explosion in Glendora Lake. Figure 6 was determined by performing spectral analysis on two seismograms recorded on the vertical component of station 120: the last shot in the Glendora tests (40 lbs in 5 feet of water), and the mining explosion recorded on September 12. The power spectrum estimates show the signal power for the two different recordings as a function of frequency and is an alternative representation of signal amplitude. The conclusion from Figure 6 is that vibrations generated by shots in Glendora Lake at distances where underground mines exist (station 120 is 1556 m from the shot point) are of the same order of magnitude as those produced by the mining explosion. Because mining explosions like this occur on at least a daily basis, it can be argued that shock waves generated by explosions comparable in size to those in the September 11-12, 1998, series would have a negligible effect relative to the nearly continuous background of mining activity in this region.

Reference Books on Seismic Wave Propagation

Aki, K. and P. G. Richards (1980). Quantitative Seismology: Theory and Methods, V. 1, Freeman, San Francisco, 557 p.

Dobrin, M. B. (1976). Introduction to Geophysical Prospecting, McGraw Hill, New York.

Garland, G. D. (1971 and later editions). Introduction to Geophysics: Mantle, Core, and Crust, Saunders, New York.

Lay, T. and T. C. Wallace (1995). Modern Global Seismology, Academic Press, San Diego, California, 521 p.

Officer, C. B. (1974). Introduction to Theoretical Geophysics, Springer-Verlag, New York, 385p.

Sharma, P. V. (1986). Geophysical Methods in Geology, Elsevier, New York.

Sheriff, R. E. (1978). A first course in geophysical exploration and interpretation, Int. Human Resources Dev. Corp., Boston.

Telford, W. M., L. P. Geldart, R. E. Sheriff, and D. A. Keys (1976), Applied Geophysics, Cambridge University Press, New York.

Figure 5. Known surface and underground mining in the area near Glendora Lake. Outlines of surface mines and underground mines are shown as different grey scale regions displayed on a map base showing major roads in the area. Currently active surface mines are also illustrated. This map was prepared at no cost to this project by the Indiana State Geological Survey using data recently compiled on Geographical Information System database.

TECHNICAL REPORT

Introduction

This section describes details of the analysis methods I applied to reduce seismic data recorded during nonfragmenting explosion tests conducted at the Glendora Lake Facility, NSWCC, on September 10-11, 1998. I assumed people who read this document have a sufficiently strong technical background to understand theoretical concepts used in the data reduction procedures. However, I expect few people who read this would have more than a rudimentary familiarity with geophysics and wave propagation in the earth. Consequently, parts of the report review some first-order principles needed to justify the approach I used. Other parts assume a basic knowledge of time series analysis, statistics, and linear algebra.

Data Collection

Seismic ground motion measurements were made during nonfragmenting explosion tests conducted at the Glendora Lake Facility, NSWCC, on September 10-11, 1998. To make the measurements we utilized 102 digital recorders equipped with 4.5 Hz natural period seismometers. This instrumentation was borrowed from a national facility administered by the Incorporated Research Institutions for Seismology, a nonprofit corporation that is a consortium of over 80 universities with active research programs in seismology. These instruments were deployed on a total of 174 points on the ground within a 3000 meter radius of the shot points (Figure T1). 63 of these stations were operated in fixed, linear profiles directed radially away from the shot points in three different directions. These stations had a nominal station spacing of 75 m. Four additional instruments were equipped with strong- motion accelerometers. These were deployed near the foundations of buildings 8000, 8001, and 8002 and in the southwest corner of the floating building I will refer to in this report as the "Barge".

The remaining 105 points shown in Figure T1 form three small clusters of stations labeled "Array 1", "Array 2", and "Array 3". These arrays were formed by utilizing 35 instruments that were moved during the course of the experiment. The numbers indicate the deployment sequence. Array 1 recorded the first four shots, array 2 recorded the final two shots on September 10, and array 3 ran for all of September 11. These arrays were designed to examine wave propagation effects in the Glendora region in an attempt to better quantify the variability of seismic wave propagation in different directions at the site. Arrays 1 and 2 were designed to understand surface wave interactions with the edges of the mined out region and array 3 was designed to investigate possible site amplification at the top of the highwall pit on which building 8000 is situated.

The equipment we used was all manufactured by Refraction Technology Inc. (REFTEK). We had two different model REFTEK instruments: (a) newer units that were equipped with 24 bit digitizers, and (b) older units that used 16 bit digitizers. The 24 bit units were placed in the linear profiles at the points closest to the shot point. However, the more limited dynamic range of the 16 bit units (96 dB) led to clipping on some of the shots for 16 bit units located within 1000 m of the shot point. This could have been prevented by lowering the fixed gain on these instruments, but time constraints and access restrictions imposed for safety reasons allowed us to make a gain change only on units in the moving arrays during the second day of the experiment.

The locations of all these stations were determined to a precision of approximately 1 cm using a Real-time Kinematic Global Positioning System owned by the Indiana University Department of Geological Sciences. We also used this equipment to precisely locate the shot point, the relative positions of survey points on most of the structures currently present at the Glendora Lake Facility, and a digital outline of the lake itself. The later was accomplished by putting the GPS system in a continuous acquisition mode and driving a boat around the shoreline. These data were sent to personnel at Comarco and the Glendora Lake facility several weeks ago via electronic mail along with diagrams showing the survey point locations relative to buildings of the facility.

Data Analysis

Raw Data Processing. Significant effort was expended in the process of turning data recorded on individual instruments into a workable data set that could be analyzed with computer software we had available to us. Most of the software tools to accomplish this task were supplied by the IRIS Consortium at no cost to the project. The complete sequence we used to reduce these data to a form that could be processed was as follows:

•	Each seismic recorder was a small computer that recorded to an internal hard disk. After removing all the instruments from the field we spent two days at Glendora extracting digital data from each instrument and writing the data onto a DAT computer tape. For this process we used large capacity hard disks supplied by IRIS and copied data from 1-10 instruments onto these copy disk. We then used a laptop computer equipped with a tape drive to make a raw copy of the data on DAT tape. In addition, the data at this stage were converted to a first-order processing format called SEGY (a standard tape format defined by the Society of Exploration Geophysicists ). We then made a second copy of the data in SEGY format onto a second DAT tape.

•	The REFTEK instruments we used have internal clocks used to keep precise, absolute time. The clocks were set in the field at the time of deployment and reset at the time the instruments were pulled from the field using an external GPS clock time. For the stations used in the phased array deployments this process was repeated three times. In addition, some stations (approximately 20%) were equipped with GPS clocks that reset the internal clock every hour. Consequently, we had a complicated mix of clock timing corrections to make on these data. Fortunately, IRIS had a mature series of computer programs available for applying timing corrections. Nonetheless, a great deal of time was expended on this process because we had to carefully examine each "log file" (a text file produced when the raw data were read that contained timing information along with a great deal of other information on the stat of health of the individual instrument) to verify the timing information contained there was sensible. Most of the data were routinely handled by the timing software from IRIS, but several stations required manual editing of the logfiles to fix various timing inconsistencies. Two types of timing errors are still present in these data that I viewed as unrepairable. First, some of the pulse time sets appear to have been seriously in error. This is reflected in relatively large linear time drift curves and clear timing inconsistencies of the order of 10s of milliseconds relative to adjacent stations. Secondly, at least one station equipped with a GPS clock has an erroneous 1 second timing error I was never able to figure out how to repair. This error is relatively minor, however, because it occurs in only two of the shots and it was then reset to the correct time by the GPS clock. Furthermore, since the focus of this study is ground motion measurements, timing errors of the scale of those that remain in the data are largely irrelevant.

•	The preliminary report I submitted in late September 1998 utilized raw data in SEGY format. For the more comprehensive data analysis described here an extensive series of database operations had to be preformed to merge all the ancillary data (e.g. receiver locations, shot locations, shot information, instrument response information, etc.) into a complete relational database that described the experimental data. This was necessary both to assure the analysis software we used could correctly index all the measurements we made on the data, and to prepare the data for submittal to the IRIS data center. We used a combination of brute force manual editing and custom processing scripts to assemble database tables describing the following experimental parameters:

site table: geographical location of each receiver

sitechan table: describes sensor orientation information for each recording channel.

instrument, sensor, and stage tables: describe frequency response of each instrument and factors used to convert measurements to ground motion.

shot table: describes information about each explosion.

In addition, individual time seismic traces were indexed with a table called wfdisc (waveform disc) that was produced by software that converted SEGY datafiles into a more compact set of files that combined all data from each shot into a single data file.

•	The field recordings ran for 5 minute windows set at 15 minute intervals during the days of shooting. The actual signal of interest had a duration of no more than a few seconds. Consequently, a large fraction of the original data was background noise. To reduce data volume I used an extraction program that reduced the working data 20 second long windows.

Amplitude Measurements. Once the complete relational database was assembled we were finally in a position to begin making the primary measurements required for this project. Because the regulations describe ground motion limits in terms of peak amplitudes we utilized a program designed for this purpose called dbpick. Dbpick was an ideal tool for this task as it was set up to automatically write the results into the relational database we had assembled through the earlier processing. It is an interactive tool that allows the analyst to record the arrival time and amplitude of one or more named phases on any seismogram.

The primary data used for the amplitude measurements were recordings of all the shots made along fixed, linear profiles (100, 200, and 300 lines illustrated in Figure T1). Now the reader needs to recognize an important technical point. Although the legal limits on ground motion in Indiana law are written in terms of peak ground motion, there are serious problems with the utility of this simple measurement for repeatable, quantitative measurements. The reason peak velocity is used as a basis for defining legal limits on blasting is the simplicity of the measurement. All that is involved is searching for the largest value in a recorded seismogram and converting that number to ground velocity. Since the most common and inexpensive sensors used in these measurements (commonly called geophones) produce an output voltage directly proportional to ground velocity, the peak reading can be converted to ground velocity by multiplication with a simple scale factor. Because the measurement is easily made and readily understood by anyone, it is ideal as a legal tool. If an instrument is placed on or near a structure, a simple yes or no answer can be quickly recovered from the data; you either did or did not exceed the limit.

Peak velocity as an analytic measurement, on the other hand, leaves much to be desired. The main problem is that the measurement is prone to large errors because of two fundamental problems related to wave propagation in the earth:

1.	The portion of the seismogram that has the largest amplitude is not always the same physical "phase". The simplest way to see this is to examine the graphical displays of all the data found in Appendix A. The portion of the signal that defines the "peak" velocity is not always the same. Consequently comparing maximum amplitude peaks quantitatively is quite literally like comparing apples and oranges.

2.	The details of what defines the peak, even for the same phase, are unstable and the actual height of the peak can change by a large amount with sometimes surprisingly small changes in experimental parameters. Figure T2 illustrates this for seismograms recorded at the same station for two different shots. The appearance of the two sets of records (each is a set of three-component records scaled consistently between components, but with a different scale factor for the two shots.) differ significantly due primarily to differences in the frequency content of the pulse generated by the two explosions. (The larger shot has more energy at lower frequencies.) This difference in frequency content changes the interference pattern generated by the many superimposed propagation modes (reflections, head waves, surface waves, and pervasive volume scattering) leading to sometimes dramatic changes in the detailed shape of the recorded seismogram. These interference effects lead to large fluctuations of measured amplitudes across the surface of the ground and from shot to shot.

Given these problems with measuring peak amplitudes, it is a major technical challenge to design an analytical procedure that can produce a dependable empirical relationship for predicting peak ground motion as a function of distance, shot size, and shot depth. I was able to accomplish this mainly because of two important factors: (1) the sheer volume of data we collected provided sufficient redundancy that I was able to utilize statistical techniques to provide solid estimates of the parameters describing an empirical formula for peak amplitudes; and (2) I developed a suite of innovative analysis methods to improve the accuracy of the results.

The first thing that was necessary to produce repeatable amplitude measurements was to make consistent measurements. This meant that rather than simply hunt for the largest amplitude recorded at each station for each shot, I focused on distinct seismic "phases". In particular, I measured amplitudes for three major phases: (1) the first arrival, P, phase; (2) the first arrival, S, phase); and (3) the Rayleigh (surface) wave. (See the executive summary above for an introductory explanation of P, S, and Rayleigh waves). Because the Rayleigh wave was wildly variable in appearance in the data, and because it was rapidly attenuated outside the area that had been surface mined I have elected to not deal with the Rayleigh wave data quantitatively. The discussion here focuses on first arrival P and S phases.

The first important assumption I made in reducing these data was that the amplitude of the initial P wave would serve as a proxy for the peak amplitude. Data justifying this assumption is presented in Figure T3. The histograms shown in the plot demonstrate that P defines the peak amplitude for a significant fraction of the data, and using P in place of the largest amplitude never introduces a bias less than a factor of 3. As we will see below, the nominal scatter in measured amplitudes (This scatter is caused by the inherent problems in measuring amplitudes described above.) is comparable to this, so I would argue that choosing the P wave amplitude as a proxy for the peak amplitude is not likely to lead to a serious bias in my results. In many respects it is not unexpected that the initial P phase forms the peak amplitude for much of the data. An explosion in water will directly generate only P waves (S waves do not propagate in a fluid) so everything else, according to conventional theory, can be understood as a scattered wave of some kind.

I measured first arrival P and S amplitudes for every seismogram from the 100, 200, and 300 lines. P amplitudes were measured from the vertical component and S were measured on the radial, horizontal component (east-west direction for the 100 and 300 line data and north-south for the 200 line). This is justified because earth structure nearly universally causes seismic waves to be refracted resulting in P waves being strongly vertically polarized and S waves being nearly horizontally polarized. Particle motion analysis we have done on these data confirm this is also true at Glendora. Note the total number of measurements this represents is 2005 of a maximum total of 2520 (2 × number of shots × number of stations). Amplitudes could not be measured on some stations for some shots because the signals were clipped on some of the stations that used 16 bit digitizers.

Deriving a form for the amplitude scaling relation. From established theories of wave propagation we expect the most important effect on amplitudes to be distance. A typical amplitude decay curve for P and S phases measured this way is shown in Figure T4. These are plotted on a log-log scale because wave propagation theory universally predicts a power low decay of amplitude with distances (commonly called geometric spreading -- see e.g. Aki and Richards, 1980). For reference we plot the expected theoretical decay curves for three simple models: (1) 1/r = expected decay of a P wave in an infinite, homogeneous medium; and (2) 1/r1/2 = expected decay for Rayleigh waves on a half space, and 1/r2 = expected decay for a head waved formed by a single, uniform layer over a half space. We see that the actual data fit none of these theoretical models. Instead the decay curve seems to be characterized by two segments: (1) at distances less than about 400 m P wave amplitudes fall off at approximately 1/r2 and (2) at greater distances amplitudes decay at a rate ~1/r. The final empirical formula derived below is based on a distance decay formula made up of two such linear segments. Before deriving a quantitative measure of the decay of amplitudes with distance, it is necessary to consider two other parameters that have a clear influence on the observed amplitude: shot size and shot depth.

It is intuitively obvious that because the shot point was at a fixed point in these experiments, we should expect the observed amplitudes at a given station to increase as the shot size increases. The question, however, is how the amplitudes scale with shot size? The scaling is not linear because the shot size is directly proportional to the energy yield of the explosion. The energy carried by seismic waves is roughly proportional to the square of the ground velocity. Consequently we should expect amplitudes to scale roughly as the square root of the yield of the explosion. There are, however, nonlinear near-field effects that alter the coupling coefficient as a function of source size. For example, Douglas and Marshall (1995) find that for underground nuclear explosions of known yield detonated at the Nevada Test Site, seismic amplitudes at great distance scale with yield Y as Y0.75 to about Y0.83 . Although the explosions at Glendora were obviously many orders of magnitude smaller than the nuclear tests at NTS, that background suggests a prudent approach is to simply assume a power law relation between amplitude and yield.

The final parameter that has a clear effect on observed amplitudes is the depth of the shot. The physics of detonating a chemical explosion in water is remarkable complex. I found an especially educational discussion of this problem in Dobrin (1976). There are at least three important physical processes that effect how well a shot couples into seismic waves:

1.	If a shot is suspended in midwater and not too large, the initial explosion generates a bubble of gas fully contained by water. Following the initial expansion that forms the bubble, the bubble oscillates at a predictable frequency that depends on shot size and depth. For the shots at Glendora empirical curves shown in Dobrin (1976) indicate this oscillation period varies between 0.5 and 0.8 s.

2.	For shots above a critical depth the initial bubble expansion is not fully contained. In this situation the top of the bubble "blows out" launching water upward at high velocities and simultaneously coupling into a high amplitude air wave. Dobrin (1976) notes that Lay (1945) found that the critical depth at which this happens can be estimated as.

where d is the critical depth (in feet) for a charge of size w in lbs of dynamite. Nine of the shots fired in the nonfragmenting tests at Glendora were above the critical depth, and 11 were below it (Figure T5).

3.	The coupling of shots near the water bottom is a more complex problem. There is an obvious asymmetry in the way the explosive bubble is able to expand that depends on the relative compressibility of the underlying lake bottom mud compared to water. This is further complicated by the fact that the water bottom is irregular (It probably became progressively more irregular with time though the series of four bottom shots which visibly moved a lot of mud.) on several length scale lengths. In short, detailed modeling of this interaction would be extremely challenging.

The reason for this brief review of the physics of the detonation of explosive in water is to point out the complexity of the problem to the reader. Years could be spent analyzing these data to test a range of source models. To make progress on the problem, however, I chose a simple empirical approach: I made a simplifying assumption that amplitudes scaled with source depth as a power law. This was largely done for expedience because then I could assume the overall relation of observed seismic amplitudes could be written as

where A is P wave amplitude (proxy for peak amplitude), R is distance, Y is the energy yield of the explosions, and Z is shot depth. c1, c2, c3, and c4 are constants.

Separation of yield and depth effects from geometric spreading. Equations (4) as an empirical model is particularly useful because the logarithmic relations allow us to readily separate the physical effects of yield and depth from the dominant decay due to geometric spreading (the log R term). That is, if we neglect the dependence of radial distance, R, on shot depth we can separate the effects of Y and Z from R by choosing one of the shots as a reference event.

To see this define Aij as be the measured amplitude at the ith station recorded for the jth shot. Then equation (4) can be written in a subscripted for as

Suppose we choose event k as a reference event. It then follows that we can write the following simple expression for relative amplitudes

That is, the ratio of the measured amplitude at the ith station relative to the amplitude recorded for the reference event (event k) does not depend upon distance. Because equation (6) is independent of distance, it suggests we could simply average relative amplitudes from all stations to get a handle on the relative size of a given shot to the reference.

I first experimented with just that process; average amplitude ratios to get a single number quantifying the relative size of all the events in the data set. I found, however, that the scatter in the computed amplitude ratios was large generally being of the order of a factor or 3 at the 95% confidence level. Consequently, I decided to try a different approach that built on the lesson of more than 20 years of seismological research in the nuclear monitoring community. In the 1970s and 1980s the major issue in monitoring underground nuclear tests revolved around the problem of the estimation of the yield of underground nuclear explosions from seismographic recordings at remote seismic stations. An enormous number of papers were published in that time period that use an endless array of statistical techniques to circumvent the same fundamental problem I faced here: peak amplitude measurements are an inherently unstable measurement. In the threshold test band treaty debates of the 1970s and 1980s most people focused on measuring peak amplitudes of various seismic "phases" and using these to compute yield. That problem is exactly the same as the one I was faced with here. The only difference is the scale of both source size and distance from the source.

The yield estimation problem was finally laid to rest in the late 1980s when it was demonstrated that the basic flaw in all the earlier approaches was the dependence on peak amplitudes as a quantitative measure of source size. The NORSAR pioneered the use of rms signal strength over a finite time window for a phase called Lg (e.g. Hansen et al., 1990). Using this approach they were able to dramatically reduce the uncertainties in estimated yields for Soviet nuclear tests and demonstrate the validity of their approach using newly released yield measurements at the Soviet test site.

The approach I used to estimate the scaling coefficients for yield at Glendora is similar in philosophy to that used by NORSAR in their pioneering work on Lg, but differs completely in detail. The approach I used is dependent on a universally accepted theoretical construct in seismology called the convolutional model of a seismogram (e.g. Lay and Wallace, 1995, pp. 364-379. The convolutional model says we can write the observed signal, dij , recorded by the ith seismograph from the jth source as

where s is a source wavelet, e is the "earth filter", i is the instrument response, and the symbol denotes the mathematical operation of convolution. The "earth filter" is the most complex of these three terms. It represents the complete sequence of all propagation modes (reflections, surface waves, etc.) that form the observed signal. It would be virtually impossible to compute with any accuracy for a complex geometry like that at Glendora as the only existing approach to handle the problem would be three-dimensional finite difference simulations. The approach I used seeks to try to remove the effect of e as much as possible.

Convolution has an important relationship to Fourier transforms. A standard result in time-series analysis is that the complex operation of convolution can be replaced a simple multiplication in the frequency domain. That is, the Fourier transform of equation (7) is

where is temporal frequency. To remove the effects of the earth filter from our measurements I use basically the same trick used in equation (5); we utilize ratios. In particular, I select a reference event, k; look up the ith station/channel recording for that event; and then compute

Equation (9) says simply that the ratio of the Fourier spectra between the same sensor recorded by two shots is a direct measurement of the ratio of the output spectrum of the two sources. Figure T6 is a graphic demonstration that this work. Figure T6a shows the full collection of spectral estimates from every seismogram recorded for event 3. Figure T6b then shows the computed spectral ratios for the same set of spectra relative to evid 1. Although the original spectra vary of many orders of magnitude, the spectral ratios are remarkably well matching. This is shown by the fact that 50% of the spectral ratios fall within a range of about ±25% of the median. This indicates that equation (9) can be used to measure variations in relative amplitudes.

Figure T6b also illustrates the averaging process I used here. I computed the average spectral ratio for the jth shot as

where Nc is the number station/channel components that recorded both event j and k. I used the median in place of a simple average to avoid contamination of the results by outliers like the dead channels shown in Figure T6. For the same reason I used the interquartile distance as a robust measurement of the scatter in the distribution (e.g. Chave et al., 1987). Note that this process is repeated at each frequency to yield three functions of frequency: the lower quartile curve, the median curve (equation (10), and the upper quartile curve.

Equation (9) is true only if the earth response filter, e(t), is not strongly dependent upon source depth. For relatively shallow water depths like that in Glendora Lake this is largely an issue at high frequencies. I compute that the characteristic time of the free surface- water bottom multiple (a phase that reverberates between the top of the water and the water bottom) is approximately 30 ms which corresponds to 30 Hz. In contrast, the bubble oscillation period (see above), which determines the predominate output frequency, is larger by an order of magnitude (500-800 ms). Furthermore, the bubble produced by these explosions is not small compared to the water depth. Tables in Dobrin (1976) and photos I have of the explosions indicate the explosion bubble in these tests were of the order of 3-5 m in size. Since the water depth is only approximately 15 m at the shot point, the bubble size is comparable to the total water depth. Consequently, I argue that the influence of shot depth on e can probably be neglected if we utilize only the lower frequency components of the data.

Computed spectral ratios confirm the hypothesis that we can neglect the influence on source depth at low frequencies, but they also reveal an intriguing puzzle. Figure T7 shows average spectral ratios for all 20 shots (equation (9)) relative to evid 1 (same as in Figure T6). Note each of the curves in Figure T7 was produced by averaging of the order of 150 spectral ratios computed from each seismogram recorded for that event (all three components were used in the averaging). Notice that at low frequencies the spectral ratios span several orders of magnitude, while at high frequencies all the curves merge together. Part of this may be a depth effect, but there is something else going on here that I must state I do not understand. Figure T8 shows only the subset of spectral ratios from Figure T7 for shots fired at 20 feet. That is, all the shots used to compute the spectral ratios in Figure T8 were all at a fixed depth. The scaling of amplitude with shot size is clear at frequencies below about 30 Hz, but at higher frequencies all the curves scatter near unity. That means that the source output at high frequencies is nearly independent of shot size. I am currently still searching the literature for an explanation of this phenomenon. It suggests that the pulse generated by an explosion in water has a high frequency limit controlled by the explosive pulse rise time. In any case, the practical consequence of this is that we need to limit the range of frequency averaging if we are to utilize equation (9) to measure relative amplitudes.

Due mainly to the observation that spectral ratios computed by equation (9) become independent of shot size at high frequencies, I elected to quantify the relative amplitude between shots using a band average. That is, I used the following formula to measure the relative amplitude determined from the ith station/channel for the jth shot relative to shot k

where lw and high define the frequency band that is to be averaged. Based on the results shown in Figure T8, I elected to use high = 25 Hz. Because I used Thomson's (1982) multitaper spectral estimation procedure to determine the spectral estimates that went into equation (11), lw was controlled by the time-bandwidth product of the estimator as 1.82 Hz (estimates at frequencies below the this value are not distinguishable from a dc offset). The square root enters in equation (11) because the multitaper method estimates power spectra, not amplitude spectra.

I use the amplitude ratios computed by equation (11) as a direct substitute for the simpler amplitude ratio defined in equation (6). Equation (11) has three important advantages over equation (6):

1.	The measurement is much more stable.

2.	It nearly triples the quantity of data that can be used because the signal recorded on each of the three components can used independently.

3.	It is more objective. The amplitude measurements I made directly from the data used an interactive procedure that required me to define the peak amplitude from a graphical display. This is subject to inconsistency in some situations when the waveforms change across the profile.

Table 1 gives the computed amplitude ratios and uncertainties for all 20 shots using a time window from 0.2 s before to 2.0 s after the first arrival. I again quote the average in terms of the median and the uncertainties in terms of the quartiles of the distribution as these provide a more robust measurement of these quantities.

Table 1. Relative amplitudes computed by spectral ratios

Shot Number (evid)	Shot size

(lb)	Shot depth

(ft)	median

ijk	Lower quartile

ijk	Upper quartile

ijk
1	20	20	1.000000	0.77450†	1.29120†
2	40	20	2.41891	1.89224	2.75099
3	60	20	4.11379	3.18349	4.91252
4	20	10	0.57347	0.480514	0.672653
5	40	10	0.863293	0.69309	1.01051
6	60	10	1.82014	1.30526	2.09168
7	20	5	0.064916	0.053597	0.081326
8	40	5	0.221419	0.143656	0.281578
9	80	20	5.43321	4.3522	6.96984
10	100	26	9.12151	7.26653	11.2208
11	100	20	6.63928	5.59881	9.30148
12	100	10	3.26951	2.37639	4.4629
13	100	50	16.9801	13.7143	22.5229
14	100	10	3.05458	2.24718	4.15132
15	100	50	15.4542	12.4213	19.7456
16	100	50	16.5582	13.0121	21.6423
17	100	50	17.2132	13.9057	21.7608
18	100	10	3.72902	2.79285	5.09799
19	100	10	1.78067	1.14206	2.70002
20	40	5	0.327007	0.222724	0.404619

†Determined by averaging log (interquartile ratio) for data from other shots. This was necessary because evid 1 was used as a reference event.

Estimation of shot size-depth scaling coefficients. I used a weighted least-squares procedure to solve for the shot size and depth scaling coefficients (constants c3 and c4 in equation (4)). That is, I formed a matrix of 20 simultaneous equations in three unknowns of the form

where the constant c5 was added as an intercept term. Equation (12) can be written in matrix form as

where here the x is a vector of the three unknown constants, b is a vector of 20 amplitude ratios (Table 1), and the 20 by 3 matrix A is formed from the two logarithmic terms on the right hand side of equation (12) and a 1 in the third column. I computed a solution vector using the a weighted least squares solution as

where the + superscript denotes the pseudoinverse and W is a diagonal matrix of weights defined as

where i is the spread parameter of the standard normal distribution and ( IQ)i is the spread computed by the interquartiles. They are related by the simple scale factor of 1.346 for normally distributed data. The use of weights based on equation (15) provides an optimal solution when data contain variable scatter. It also provides an objective test of how well the final model fits the data.

When the data in Table 1 were used with equation (14) I determined the following values for the three scaling constants for Y and Z. They are:

The model residuals can be tested using a standard 2 test. That is, if we assume the data have a log normal distribution with variances given by equation (15), then the residuals, rw=W(b-Ax)

can be shown to have a chi-squared distribution with N-3 degrees of freedom (e.g. Hoel, 1971). Using this property I found that the coefficients in equation (16) yield a fit to the data in Table 1 at greater than the 99% level. I conclude that this is a good statistical model for the dependence of the data on yield and depth.

Determination of the distance decay constants. The final step in deriving equation (1) is to determine the distance decay constants c1 and c2 in equation (4). To do this, I normalized all the P wave amplitude data using the scaling relation for Y and Z derived above. The normalization used corrects the measured amplitudes of each shot be equivalent to evid 1 (the first shot of the series -- 20 lbs at 20 feet). The normalized P wave amplitude data are show in Figure T9. To see the effects of this normalization compare Figure T9 to the unscaled data in Appendix B.

The remaining issue was fitting an amplitude decay curve to these data. It was my judgement that the data could not be fit adequately with a single line on this log-log plot. I separated the normalized amplitude data into two section: (1) points at a distance under 400 m, and (2) points at a distances over 400 m. I computed a least-squares line fit through each of these data segments yielding the following decay constants for distances under 400 m

and for distances over 400 m

The intersection of these two lines in log-log space occurs at R=315 m, which differs somewhat from the distance at which the data was split (400 m). I did not consider this a significant problem. The final empirical equation (1) was then obtain by simply combining equations (18) and (19) with the yield-depth scaling relation derive above.

The final step was computing a set of reasonable uncertainty estimates on the results. To do this I computed the residuals of all the normalized P wave data relative to the two decay segments given by equations (18) and (19). That is, for each normalized P wave amplitude, Ai, I computed

where Ri is offset. The coefficients used for c1 and c2 were from either equation (18) or (19) depending on whether Ri was less than or greater than 400 m respectively. Histograms of these residuals are shown in Figure T10. Figure T10 demonstrates that the amplitudes are approximately log normal distributed. Consequently, I computed a simple standard deviation of the residuals and used this to derive the 95% confidence interval as ±0.242 logarithmic units illustrated in Figure 3. Note that this neglects potential errors in the yield-depth scaling relation. This simplifying assumption is appropriate because the scatter of the amplitude estimates used to define that scaling relation are almost order of magnitude smaller than the scatter in the P wave amplitude measurements.

Scientific Problems Identified from these Data

There are two basic research questions that have appeared as we analyzed these data. The first was discussed briefly above. That is, these data provide an interesting new window into seismic source processes. The fact that the energy output of the different shots seems to become independent of source size or depth at frequencies over about 35 Hz was a surprising result to me. There is a fairly extensive literature on seismic source processes that may have a good physical explanation for this effect. If this phenomenon has not been observed previously, it could prove a fruitful avenue of future research.

A second interesting scientific observation has emerged from array 3. Array 3 was installed to test theoretical models that predicted marked site resonances on sharp topographic slopes like that where building 8000 sits at Glendora. The effect of this edge is quite clear and sometimes rather dramatic. Figure T11 is one way to illustrate this effect. This shows vertical component records of P waves recorded on 300 line stations. These data are displayed at true amplitude but with the amplitude of each seismogram scaled by the using equations (18) and (19) to an common distance. This allows us to see a strong site amplification approximately at least a factor of 2 and a characteristic ringing that indicates a true resonance effect. A student of mine (Christian Poppeliers) and I are in the process of refining that part of the study with plans to submit it for publication in a scientific journal later this spring. If the reader is interested in that paper I urge you to contact me and I will send you a copy when the paper is finished.

Data Availability

I am in the process of packaging up the data from this experiment and delivering it to the IRIS Data Management Center in Seattle. This includes a short data report that remains to be written. Once at the IRIS data center the data will be openly available to any researcher around the world who is interested in using it. Agreeing to produce this data product was a condition I had to agree to in order to obtain the IRIS equipment used for the recording the test explosions.

List of References

Aki, K. and P. G. Richards (1980). Quantitative Seismology: Theory and Methods, V. 1, Freeman, San Francisco, 557 p.

Chave, A. D., D. J. Thomson, and M. E. Ander (1987). On the robust estimation of power spectra, coherence, and transfer functions, J. Geophys. Res.,92, pp. 633-648.

Dobrin, M. B. (1976). Introduction to Geophysical Prospecting, McGraw Hill, New York.

Douglas, D. and P. D. Marshall (1995). Seismic source size and yield for nuclear explosions, in Monitoring a Comprehensive Test Ban Treaty, ed. E. S. Husebye and A. M. Dainty, Kluwer, Boston, pp. 309-353.

Hansen, R. A., F. Ringdal, and P. G. Richards (1990). The stability of Lg measurements and their potential for accurate estimation of the yields of Soviet underground nuclear explosions, Bull. Seism. Soc. Amer., 80, pp. 2106-2126.

Hoel, P. G. (1971). Introduction to Mathematical Statistics, Wiley, New York, 409 p.

Lay, R. L. (1945). Repeated P-waves in seismic exploration of water covered areas, Geophysics, 10, pp. 467-471.

Lay, T. and T. C. Wallace (1995). Modern Global Seismology, Academic Press, San Diego, California, 521 p.

Thomson, D. J. (1982). Spectrum estimation and harmonic analysis, Proc. IEEE, 70, pp. 1055-1096.

Appendix A: Record Sections

The following figures are complete record sections of all the data recorded on the linear profiles referred to as the 100, 200. and 300 lines in the main body of this report (Figure 1). These figures are record sections in which a series of seismograms are displayed at equal intervals in their proper order along a profile. In all cases time increases from the top of the plot to the bottom of the page. All are displayed in true amplitude but with a geometric spreading correction applied based on the empirical amplitude scaling law developed in this study. Consequently relative amplitudes show variability from the standard curves developed here. Figures A1 to A20 show results for the combined linear profile formed by the 100 and 300 lines. The left side of these plots are the west end of that profile and the right side is the east end. Each figure shows three different record sections corresponding to each of the three-components recorded by these instruments. The left panel is the radial component, the center section is the transverse component, and the right panel is the data for the vertical components. The figures are organized in the order of shooting in the field. Each figure has a fileid number shown at the top that defines which shot in the sequence those data are related to.

Figures A21 to A40 are comparable to Figures A1 to A20, but they display data from the 200 line (Figure 1). All other features of the graphics are the same.

Appendix B: Measured P and S amplitudes

The figures presented here show measured P and S wave amplitudes as a function of distance. These are the primary data used to generate the amplitude versus distance relationship derived in this report. In each plot the measured amplitudes are shown in relation to the predicted average decay curve (solid line) and 95% confidence intervals (bracketing dashed lines) derived from the power law model developed in this report. Data from the individual seismic lines running in different direction are indicated with different symbols. For a geographical reference to 100, 200, and 300 lines see Figure 1.