Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2006. / Earthquakes and other seismic events are known to have catastrophic effects on
people and property. These large-scale events are almost always preceded by smallerscale
seismic events called precursors, such as tremors or other vibrations. The use of
precursor data to predict the realization of seismic hazards has been a long-standing
technical problem in different disciplines. For example, blasting or other mining
activities have the potential to induce the collapse of rock surfaces, or the occurrence
of other dangerous seismic events in large volumes of rock. In this study, seismic
data (T4) obtained from a mining concern in South Africa were considered using
a nonlinear time series approach. In particular, the method of surrogate analysis
was used to characterize the deterministic structure in the data, prior to fitting a
predictive model.
The seismic data set (T4) is a set of seismic events for a small volume of rock in a
mine observed over a period of 12 days. The surrogate data were generated to have
structure similar to that of T4 according to some basic seismic laws. In particular,
the surrogate data sets were generated to have the same autocorrelation structure
and amplitude distributions of the underlying data set T4. The surrogate data
derived from T4 allow for the assessment of some basic hypotheses regarding both
types of data sets.
The structure in both types of data (i.e. the relationship between the past behavior
and the future realization of components) was investigated by means of three test
statistics, each of which provided partial information on the structure in the data.
The first is the average mutual information between the reconstructed past and futures
states of T4. The second is a correlation dimension estimate, Dc which gives an
indication of the deterministic structure (predictability) of the reconstructed states
of T4. The final statistic is the correlation coefficients which gives an indication
of the predictability of the future behavior of T4 based on the past states of T4. The past states of T4 was reconstructed by reducing the dimension of a delay coordinate
embedding of the components of T4. The map from past states to future
realization of T4 values was estimated using Long Short-Term Recurrent Memory
(LSTM) neural networks. The application of LSTM Recurrent Neural Networks on
point processes has not been reported before in literature.
Comparison of the stochastic surrogate data with the measured structure in the
T4 data set showed that the structure in T4 differed significantly from that of the
surrogate data sets. However, the relationship between the past states and the
future realization of components for both T4 and surrogate data did not appear to
be deterministic. The application of LSTM in the modeling of T4 shows that the
approach could model point processes at least as well or even better than previously
reported applications on time series data.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/2257 |
| Date | 12 1900 |
| Creators | Bredenkamp, Ben |
| Contributors | Aldrich, C., University of Stellenbosch. Faculty of Engineering. Dept. of Process Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 4288699 bytes, application/pdf |
| Rights | University of Stellenbosch |
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