Lg DEPTH ESTIMATION AND RIPPLE FIRE 
CHARACTERIZATION USING 
ARTIFICIAL NEURAL NETWORKS 
John 
L. Perry and Douglas R. Baumgardt 
ENSCO, Inc. 
Signal Analysis and Systems Division 
5400 Port Royal Road 
Springfield, Virginia 22151 
(703) 321-9000, perry@dewey.css.gov 
Abstract 
This study has demonstrated how artificial neural networks (ANNs) can 
be used to characterize seismic sources using high-frequency regional 
seismic data. We have taken the novel approach of using AN-Ns as a 
research tool for obtaining seismic source information, specifically 
depth of focus for earthquakes and ripple-fire characteristics for 
economic blasts, rather than as just a feature classifier between 
earthquake and explosion populations. Overall, we have found that 
ANNs have potential applications to seismic event characterization and 
identification, beyond just as a feature classifier. In future studies, these 
techniques should be applied to actual data of regional seismic events 
recorded at the new regional seismic arrays. The results of this study 
indicates that an ANN should be evaluated as part of an operational 
seismic event identification system. 
1 INTRODUCTION 
1.1 NEURAL NETWORKS FOR SEISMIC SOURCE ANALYSIS 
In this study, we have explored the application of artificial neural networks (ANNs) for 
-the characterization of seismic sources for the purpose of distinguishing between 
explosions and earthquakes. ANNs have usually been used as pattern matching 
algorithms, and recent studies have applied ANNs to standard classification between 
classes of earthquakes and explosions using waveform features (Dowla, et al, 1989), 
(Dysart and Pulli, 1990). However, in considering the current state-of-the-art in seismic 
event identification, we believe the most challenging problem is not to develop a superior 
classification method, but rather, to have a better understanding of the physics of seismic 
source and regional signal propagation. 
544 
Lg Depth Estimation and Ripple Fire Characterization 545 
Our approach to the problem has been to use ANN technology as a research tool for 
obtaining a better understanding of the phenomenology behind regional discrimination, 
with emphasis on high-frequency regional array data, as well as using ANNs as a pattern 
classifier. We have explored two applications of ANNs to seismic source 
characterization: (1) the use of ANNs for depth characterization and (2) the recognition of 
ripple-firing effects in economic explosions. 
In the first study, we explored the possible use of the Lg cross-coherence matrix, 
measured at a regional array, as a "hidden discriminant" for event depth of focus. In the 
second study, we experimented with applying ANNs to the recognition of ripple-fire 
effects in the spectra of regional phases. Moreover, we also investigated how a small 
(around 5 Kt yield) possibly alecoupled nuclear explosion, detonated as part of a ripple-fire 
sequence, would affect the spectral modulations observed at regional distances and how 
these effects could be identified by the ANN. 
1.2 ANN DESCRIPTION 
MLP Architecture: The ANN that we used was a multilayer perceptron (MLP) 
architecture with a backpropagation training algorithm (Rumelhart, et al, 1986). The 
input layer is fully connected to the hidden layer, which is fully connected to the output 
layer. There are no connections within an individual layer. Each node communicates 
with another node through a weighted connection. Associated with each connection is a 
weight connecting input node to hidden node, and a weight connecting hidden node to 
output node. The output of "activation level" of a particular node is defined as the linear 
weighted sum of all its inputs. For an MLP, a sigmoidal transformation is applied to 
this weighted sum. Two layers of our network have activation levels. 
MLP Training: The MLP uses a backpropagation training algorithm which employs 
an iterating process where an output error signal is propagated back through the network 
and used to modify weight values. Training involves presenting sweeps of input patterns 
to the network and backpropagating the error until it is minimized. It is the weight 
values that represent a trained network and which can be used in the 
recognition/classification phase. 
MLP Recognition: Recognition, on the other hand, involves presenting a pattern to 
a trained network and propagating node activation levels uni-directionally from the input 
layer, through the hidden layer(s), to the output layer, and then selecting the class 
corresponding to the highest output (activation) signal. 
2 Lg DEPTH ESTIMATION 
In theory, the Lg phase, which is often the largest regional phase on the seismogram, 
should provide depth information because Lg results from the superposition of numerous 
normal modes in the crust, whose excitation is highly depth dependent. Some studies 
have shown that Lg amplitudes do depend on depth (Der and Baumgardt, 1989). However, 
the precise dependency of Lg amplitude on depth has been hard to establish because other 
effects in the crustal model, such as anelastic attenuation, can also affect the Lg wave 
amplitude. 
546 Perry and Baumgardt 
In this study, we have considered if the Lg coherence, measured across a regional array, 
might show depth dependency. This idea is based on the fact that all the normal modes 
which comprise Lg propagate at different phase velocities. For multilayered media, the 
normal modes will have frequency-dependent phase velocities because of dispersion. Our 
method for studying this dependency is a neural network implementation of a technique, 
called matched field processing, which has been used in underwater acoustics for source 
water-depth estimation (Bucker, 1976), (Baggeroer, et al, 1988). This method consists of 
computing the spectral matrix of an emitted signal, in our case, Lg, and comparing it 
against the same spectral matrix for master events at different depths. In the past, various 
optimal methods have been developed for the matching process. In our study, we have 
investigated using a neural network to accomplish the matching. 
2.1 SPECTRAL MATRIX CALCULATION AND MATCHED FIELD 
PROCESSING 
The following is a description of how the spectral matrix is computed. First, the 
synthetic seismograms for each of the nine elements of the hypothetical array are Fourier 
transformed in some time window. If Si (co) is the Fourier transform of a time window 
for the i the channel, then, the spectral matrix is written as, Hij (oo) --S i (o) Sf*(o ), where 
St (o) =A  e 'J t,, *  ( , )1, the index j is the complex number, t is the phase angle, and 
the * represents complex transpose. The elements, aik of the spectral matrix can be 
written as aa(o)=Ai Ake'ii'kl where the exponential phase shift term 
 (o)-  (o)- ro 
is c, (to)  Cn (co) represents the phase velocity for 
mode n, which is a function of frequency because of dispersion, xi - Xk is the spatial 
separation of the i th and k th channels of the array, and  i k (C0) is the time shift of 
mode n at frequency co across the two channels. The product of the synthetic 
eigenfunctions, A i , and thus, the spectral matrix terms, are functions of source depth and 
model parameters. 
The spectral matrix, H ij (co), can be computed for an entire synthetic waveform or for a 
window on a part of the waveform. The elements of the spectral matrix can be 
normalized by inter- or intm- window normalization so that its values range from 0 to 1. 
2.2 ANN . MATCHED FIELD DEPTH ESTIMATION 
Two different depth studies were performed during this effort. The first study evaluated 
using the ANN to classify deep (greater than 4 kilometers) vs. shallow (less than 4 
kilometers) seismic events. The number of input nodes equaled the number of points in 
the spectral matrix which was 1620 (36 data points x 45 spectral elements after 
smoothing). The number of output nodes was dependent on the type of classification we 
wanted to perform. For the shallow-deep discrimination, we only required two output 
nodes, one for each class. Training the ANN involved compiling a training (exemplar) 
set of spectral matrices for various shallow and deep events and then presenting the 
training set to the ANN. 
In the second study, we investigated if the ANN could be used to classify seismic events 
at different depths. Again, we used five windows on the Lg phase and implemented the 
Lg Depth Estimation and Ripple Fire Characterization 547 
interwindow and intrawindow normalization procedure. The second network was trained 
with a seven-element depth vector, whose elements represent the depths of 1, 3, 6, 9, 12, 
16, and 20 kilometers. 
3 RIPPLE-FIRE CHARACTERIZATION 
In this study, we wanted to determine if spectral modulations could be recognized by the 
neural network and if they could be attached to concepts relating to the source parameters 
of ripple-fired events. Previous studies have demonstrated how such patterns could be 
found by looking for time-independent spectral modulations (Baumgardt and Ziegler, 
1989), (Hedlin, et al, 1990). In this study, we assumed such a pattern has been found and 
that the input to the ANN is one of the time-independent spectra. An additional issue we 
considered was whether it would be possible to hide a nuclear explosion in the ripple-fare 
pattern, and whether or not such a pattern might be recognizable by an ANN. In this 
study, as in the previous depth characterization study, we relied entirely on simulated data 
for training and test. 
3.1 ANN - RIPPLE FIRE CHARACTERIZATION 
We performed two ripple-fried studies which were designed to extract different parameters 
from the tipple-fared events. The two studies characterized the following parameters: 1) 
time delay (Experiment A), and 2) normal vs. anomalous (Experiment B). The purpose 
of the time delay study was to estimate the time delay between explosions irrespective of 
the number of explosions. The goal of the second study was to determine if the ANN 
could extract a "normal" ripple-fired explosion from a simulated nuclear explosion buried 
in a tipple-fired event. 
The input nodes to all three networks consisted of the elements of the seismic spectra 
which had 256 data points, coveting the frequency range of 0 to 20 Hz. All weights were 
initialized to random values in the range of [-0.5, 0.5] and all networks had their 
momentum term set to 0.9. The number of hidden units, learning rate, and number of 
output nodes varied between each experiment. 
In Experiment A, the number of hidden units was 24, and we used a learning rate of 0.2. 
We used seven output nodes to represent seven different classes with time delays of 37.5, 
62.5, 87.5, 112.5, 137.5, 162.5, and 187.5 ms. These delay times are the centers of the 
following delay time bins: 25-50 ms, 50-75 ms, 75-100 ms, 100-125 ms, 125-150 ms, 
150-175 ms, and 175-200 ms. We used five examples of each class for training derived 
by varying the time delay by -+5 msec. Training involved presenting the five exemplars 
for each class to the ANN until the squared error approached zero, as we did in the depth 
discrimination study. The ANN was trained to return a high activation level in the bin 
closest to the delay time of the ripple-fire. 
In Experiment B, we wanted to determine if the ANN could discern between normal and 
anomalous ripple-fire patterns. The ANN was trained with 50 hidden units and a learning 
rate of 0.1. There were 36 exemplars of each class, which resulted from all combinations 
of six time delays of 5, 6, 7, 8, 9, and 10 ms between individual shots and six time 
delays of 5, 6, 7, 8, 9, and 10 ms between rows of shots. Each time delay was also 
varied +1.0 ms to simulate the effect of errors in the blasting delays. 
548 Perry and Baumgardt 
Two output nodes were defined which represent anomalous or normal ripple-fire. The 
normal ripple-fire class represented all the simulations done for the triangular pattern. We 
assumed each shot had a yield of 1000 kg. The anomalous class were all the simulations 
for when the last row of 10 shots was replaced with a single large explosion of 10,000 
kg. The ANN was then trained to produce a high activation level for either of these 
classes depending on which kind of event it was presented. The effect of the single large 
explosion signal was to wash out the scalloping pattern produced by the tipple-fared 
explosions. We trained the ANN with normal ripple-fired patterns, with no embedded 
nuclear explosions, and anomalous patterns, with an embedded nuclear explosion. 
4 RESULTS 
4.1 RESULTS OF DEPTH STUDY 
In our first study, we wanted to determine if the network could learn the simple concepts 
of shallow and deep from Lg synthetics when presented with only a small number of 
exemplar patterns. We presented the ANN with four depths, 1, 2, 12, and 20 km, and 
trained the network to recognize the first two as shallow and the second two as deep. We 
then presented the network with the rest of the synthetics, including synthetics for depths 
the ANN had not seen in training. 
The results of the shallow-deep discrimination study are shown in Table 1. The table 
shows the results for both the interwindow and intrawindow normalization procedures. 
The test set used to generate these results were also synthetically generated events that 
were either less than 4 km (shallow) or greater than 4 km (deep). Our criteria for a correct 
match was if the correct output node had an activation level that was 0.4 or more above 
the other output node's activation. This is a very conservative threshold criteria, which is 
evident from the number of undecided values. However, the results do indicate that the 
percent of incorrect classifications was only 5.0% for the intrawindow case and 8.3% for 
the interwindow case. The percent of correct classification (PCC) for the intrawindow 
case was 50% and the PCC for the interwindow case was 58.3%. The network appeared 
to be well trained, relative to their squared error values for this study. Using a less 
conservative correct match criteria, where the correct output node only had to be larger 
than the other output node's activation, the PCC was 88.3% for the intrawindow case and 
93.3% for the interwindow case. 
I [nma-%ndow / liner-Window 
Depth cortec incorrect urecrded 
(.la-n. )  Fcazest l 
3 2/3 5/5 0/0 0/0 3/2 0/0 
4 1/2 3/3 0/2 0/2 4/1 2/0 
5 2/3 .x15 0/0 0/0 3/2 I/0 
6 .x/3 5/5 0/1 0/0 Ill 0/0 
7 2/. 4/5 1/0 I/0 2/1 010 
8 3/2 5/5 0/0 0/0 2/3 0/0 
9 3/5 4/5 I/0 0/0 l/0 1/0 
l0 3/3 5/5 0/0 0/0 2/2 : 0/0 
11 1/2 4/3 1/2 1/2 3/I [ 0/0 
13 2/2 5/5 0/0 0/0 3/3 
14 4/4 5/5 0/0 0/0 1/1 0/0 
s s/2 /5 o/o 0/0 2/3 t/o 
ToI 30/35 53/56 3/5 7./4 f 27/.20 t 5/0 
Table 1: Resulls of ANN for Shallow-Deep Discrimination. 
4.2 RESULTS OF THE RIPPLE-FIRED STUDY 
Linear Shot Patterns (Experiment A) 
Table 2 summarizes all the results for the time-delay ripple-fired classification study 
performed during Experiment A. The table shows both two-shot training and a two- and 
three-shot training cases. The test set for both cases were spectra that had time delays that 
were in a +_5 ms range of the target time delay pattern. We set two criteria for PCC for 
the two-shot case. The fLrst was that the activation level for the correct output node be 
larger than the activation levels of the other output nodes. This produced a PCC of 
77.7%, with a 22.2% error rate and no undecided responses. All of the errors resulted 
from attempting to use the ANN to learn time delays from a three-shot pattern where the 
network was only trained on two-shot events. The second criterion was more 
conservative and required that the activation level of the correct output node be > 0.5 than 
the other output nodes. This gave a PCC = 68.2%, an error percentage of 4.5%, although 
the number of undecided responses increased to 27.2%. Again, all the errors resulted from 
expecting the ANN to generalize to three-shot events from only being trained with two- 
shot patterns. Finally, the results for the two- and three-shot training case were much 
more impressive. Using both threshold criteria, the ANN achieved a PCC of 100%. 
atshoid C-item 
Test Set * 0.0 / 0.5 
COTmet I Lncorreet under:deal 
CcA 7/6 I 0/0 0/1 
C-cB 8/7 0/0 0/I 
3 shots 3/2 4/! 0/. 
Total [8/|5 } 4/1 0/6 
 (Trained nth a 2-shot partem 
Thmsioid Cr/tena 
Te$ Set ' 0.0 / 0.5 
CacA 7/7 0/0 i 0/0 
C.uc B 8/8 0/0 0/0 
3 shots 7/7 0/0 0/0 
roai t 22/'-2 ! o/o t o/o 
 G raincO. wih a 2- and 3-shot 
Table 2: Results of ANN for Time-Delay Ripple-Fired Discrimination 
Triangular Shot Patterns - Normal Versus Anomalous (Experiment B) 
Table 3 depicts the results of Experiment B for the normal vs. anomalous study. The 
threshold criteria for the target output node compared to the other output nodes was 0.4. 
Again, the test set consisted of time delays that were within +5 ms of the target time 
delay pattern. The PCC was 69.4%, the error percentage was 2.7%, and the percentage of 
undecided responses was 27.7%. As evident from the table, the majority of undecided 
responses were generated from attempting to classify the anomalous event. 
Thtuold Cruena 
Te Set 0.4 
Normal 3 [ ! 4 
Anomalous 19 I 16 
Tal 50 { 2 t 20 '" 
Table 3: Results of ANN for Normal s. Anomalous 
Rioole-trired Oiscriminalion 
550 Perry and Baumgardt 
5 CONCLUSIONS 
This study has shown that ANNs can be used to characterize seismic waveform patterns 
for the purpose of characterizing depth of focus, from Lg spectral matrices, and for 
recognizing ripple-fire patterns from spectral modulations. However, we were only able 
to analyze the results for simulated input data. In future studies, we intend to use real data 
as input. 
We have demonstrated that events can be classed as shallow or deep on the basis of the Lg 
spectral matrix and that the ANN provided a convenient and robust methodology for 
matching spectral matrices. The fact that we obtained nearly the same recognition 
performance for interwindow and intrawindow normalizations shows that the Lg spectral 
matrix does in fact contain significant information about the depth of focus of a seismic 
event, at least for theoretically derived synthetic cases. 
The results for the ripple-fire recognition study were very encouraging. We found that 
neural networks could easily be trained to recognize many different ripple-f'tre patterns. 
For a given blasting region, a neural network could be trained to recognize the usual, 
routine ripple-fire patterns generally used in the region. We have shown that it should be 
possible to identify unusual or anomalous ripple-fire patterns due to attempts to include a 
large decoupled nuclear explosion in with an ordinary ripple-fire sequence. 
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