dorsal/arxiv
View SchemaModification of the pattern informatics method for forecasting large earthquake events using complex eigenvectors
| Authors | James R. Holliday, John B. Rundle, Kristy F. Tiampo, Bill Klein, Andrea Donnellan |
|---|---|
| Categories | |
| ArXiv ID | physics/0508154 |
| URL | https://arxiv.org/abs/physics/0508154 |
| DOI | 10.1016/j.tecto.2005.10.008 |
| Journal | Tectonophysics 413 (2006) 87- 91 |
Abstract
Recent studies have shown that real-valued principal component analysis can be applied to earthquake fault systems for forecasting and prediction. In addition, theoretical analysis indicates that earthquake stresses may obey a wave-like equation, having solutions with inverse frequencies for a given fault similar to those that characterize the time intervals between the largest events on the fault. It is therefore desirable to apply complex principal component analysis to develop earthquake forecast algorithms. In this paper we modify the Pattern Informatics method of earthquake forecasting to take advantage of the wave-like properties of seismic stresses and utilize the Hilbert transform to create complex eigenvectors out of measured time series. We show that Pattern Informatics analyses using complex eigenvectors create short-term forecast hot-spot maps that differ from hot-spot maps created using only real-valued data and suggest methods of analyzing the differences and calculating the information gain.
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"abstract": "Recent studies have shown that real-valued principal component analysis can\nbe applied to earthquake fault systems for forecasting and prediction. In\naddition, theoretical analysis indicates that earthquake stresses may obey a\nwave-like equation, having solutions with inverse frequencies for a given fault\nsimilar to those that characterize the time intervals between the largest\nevents on the fault. It is therefore desirable to apply complex principal\ncomponent analysis to develop earthquake forecast algorithms. In this paper we\nmodify the Pattern Informatics method of earthquake forecasting to take\nadvantage of the wave-like properties of seismic stresses and utilize the\nHilbert transform to create complex eigenvectors out of measured time series.\nWe show that Pattern Informatics analyses using complex eigenvectors create\nshort-term forecast hot-spot maps that differ from hot-spot maps created using\nonly real-valued data and suggest methods of analyzing the differences and\ncalculating the information gain.",
"arxiv_id": "physics/0508154",
"authors": [
"James R. Holliday",
"John B. Rundle",
"Kristy F. Tiampo",
"Bill Klein",
"Andrea Donnellan"
],
"categories": [
"physics.geo-ph"
],
"doi": "10.1016/j.tecto.2005.10.008",
"journal_ref": "Tectonophysics 413 (2006) 87- 91",
"title": "Modification of the pattern informatics method for forecasting large earthquake events using complex eigenvectors",
"url": "https://arxiv.org/abs/physics/0508154"
},
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