The document discusses using a neural network model to forecast seismic activity in the Koyna-Warna region of India based on past earthquake and reservoir water level data. It develops a feed forward neural network model with a single hidden layer trained on 13 years of past data and tested on 4.5 years of data. The model results show it can accurately forecast seismicity levels and validate observed seismicity in 2000, predicting heightened activity for March that year. Further models are developed using reservoir water levels and seismicity data as inputs to forecast magnitudes of over 5 earthquakes in the region.

1. A Neural Network model for occurrence of seismicity in
Koyna – Warna region, India
J. Pavan Kumar *,D. V. Ramana and R. K.Chadha
National Geophysical Research Institute (CSIR) Hyderabad
pavanj@ngri.res.in
Earthquake forecasting has become an emerging
science, which has been applied in different areas of
the world to monitor seismic activities. The
application of the artificial neural network has been
proposed to predicate the seismicity of the Koyna –
Warna region, India. The neural network technique is
widely used, since it is having the capability of
capturing the non-linear relationship. In this work we
study past earthquakes in the region to give better
forecasting the earthquakes. Here, we developed the
scheme based on feed forward neural network model
with single hidden layers. The model consists of
neural network training from the known input and
testing the data. The model results reveal the
application of the ANN is relatively useful in
forecasting the seismicity of the region.
The seismicity of the Koyna – Warna region is observed and the
number of events occur in this region per month has been
calculated.
The histograms of the earthquakes at different times are shown
in figure. From this figure it is very clear that the count is high, at
times it is maximum when an earthquake of M > 4.7 occurred.
From the counts it is clear that increase in foreshock activity or
increase in aftershock up to 15 days after an M > 4 earthquake.
This makes us to that forecasting the count of the seismicity for
a month identical to forecasting the earthquake of magnitude
M>4.7 for that particular month in this region.
We considered long data set i.e., the events occurred from 1990
to 2009 as a time series. In twenty years data set, to train the neural
network model we considered 13 years data as the training data to
obtain the model and 4½ years data as for testing purpose. The
model results are shown in the figure with training, testing and
validated for 2½ years data.
It is very clear from the validation, 5% from the total 100% can
only be validated more accurately but in the figure we have given
up to 10% data points for the validation.
The model results are used to validate the 2000 seismicity and the
results show the maximum seismicity for the month of March in
2000 and it is well observed.
Different models are developed to validate the seismicity of the
Koyna-Warna region based on the past seismicity data of the region.
We developed the ANN model with reservoir water level as one
input parameter to undergo training and testing phases. These
results are validated, by further reservoir water level data with
respect to different time scales.
All the model results are matched with the observed data sets
within the tolerance errors.
Independently, the seismicity of the region is forecasted by
developing ANN models.
Developed the models with two input parameters i.e., Reservoir
water levels and the Seismicity to forecast the seismicity of the
region.
The models are well constrained and these models are further
useful in forecasting the M>5 earthquake in Koyna – Warna region.
Neural networks are members of a family of computational
architectures inspired by biological brains. Such architectures
are commonly called "connectionist systems", and are
composed of interconnected and interacting components
called nodes or neurons. Neural networks are characterized by
a lack of explicit representation of knowledge; rather,
knowledge is implicitly represented in the patterns of
interactions between network components .A graphical
depiction of a typical feed forward neural network is given in
Figure 1. A simple neural network model.
The Sigmoid threshold unit .
In the employment of the back propagation algorithm, each
iteration of training involves the following steps:
The histograms of the earthquakes
at different times.
The Koyna reservoir water level changes
from 1990 to 2008
Time series of the earthquake counts
from 1990-2008
Figure 6 Seismicity count data set from
2000 – 2005
M=5.2 M=5.0
The Koyna - Warna region is 225 km south of
Mumbai in western India and 50 km east of the
Arabian Sea located within the interior of the Indian
plate. After the main shock of Dec. 1967 the present
of moderate earthquakes are occurring in this region.
This Koyna - Warna region has been a site for
triggered earthquakes for the last forty five years after
the impoundment of the Koyna reservoir in 1961.
Because the earthquakes are occurring in a small area
15 x 30 km2, this region is called as a natural
seismological laboratory and it is an ideal site for
monitoring the precursory phenomena. The
prediction of the earthquake has been ultimate goal
for the earth scientists and generally it is classified
into short, mediate and long term forecasting. The
model results show that the developed ANN models
are more adequate in this region.
Fig4 Seismicity count data set
from 2001-2006
Seismicity count data set
from 1990 – 1994
Koyna reservoir Water Level
from 2001-2006
The monthly histograms of the
earthquakes for year 2005.
Seismicity count data set
from 1995 to 2000
Water level data set
from 1995 to 2000
M =5.0
Year 2005 Observed Forecasted
JAN 2 3.237717
FEB 6 5.645138
MAR 39 41.684522
APR 9 8.064301
MAY 20 19.796038
JUN 27 28.059394
JUL 3 3.749682
AUG 1 2.783316
SEP 2 3.237717
OCT 2 3.237717
NOV 7 6.396098
DEC 9 8.064301
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