Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Analysis and modelling of mining induced seismicity
Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2006.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Stellenbosch : University of Stellenbosch
2008
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613772336594944 |
|---|---|
| access_status_str | Open Access |
| author | Bredenkamp, Ben |
| author2 | Aldrich, C. |
| author_browse | Aldrich, C. Bredenkamp, Ben |
| author_facet | Aldrich, C. Bredenkamp, Ben |
| author_sort | Bredenkamp, Ben |
| collection | Thesis |
| dc_rights_str_mv | University of Stellenbosch |
| description | Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2006. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/2257 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:41:26.849Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2008 |
| publishDateRange | 2008 |
| publishDateSort | 2008 |
| publisher | Stellenbosch : University of Stellenbosch |
| publisherStr | Stellenbosch : University of Stellenbosch |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/2257 Analysis and modelling of mining induced seismicity Bredenkamp, Ben Aldrich, C. University of Stellenbosch. Faculty of Engineering. Dept. of Process Engineering. Dissertations -- Process engineering Theses -- Process engineering Seismology Seismic prospecting Mining engineering Process Engineering 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. Masters 2008-01-24T12:51:55Z 2010-06-01T08:44:34Z 2008-01-24T12:51:55Z 2010-06-01T08:44:34Z 2006-12 Thesis http://hdl.handle.net/10019.1/2257 en University of Stellenbosch 4288699 bytes application/pdf application/pdf Stellenbosch : University of Stellenbosch |
| spellingShingle | Dissertations -- Process engineering Theses -- Process engineering Seismology Seismic prospecting Mining engineering Process Engineering Bredenkamp, Ben Analysis and modelling of mining induced seismicity |
| title | Analysis and modelling of mining induced seismicity |
| title_full | Analysis and modelling of mining induced seismicity |
| title_fullStr | Analysis and modelling of mining induced seismicity |
| title_full_unstemmed | Analysis and modelling of mining induced seismicity |
| title_short | Analysis and modelling of mining induced seismicity |
| title_sort | analysis and modelling of mining induced seismicity |
| topic | Dissertations -- Process engineering Theses -- Process engineering Seismology Seismic prospecting Mining engineering Process Engineering |
| url | http://hdl.handle.net/10019.1/2257 |
| work_keys_str_mv | AT bredenkampben analysisandmodellingofmininginducedseismicity |