Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008)

S. Stein and M. Wysession's An Introduction to Seismology, Earthquakes, and Earth Structure is the textbook I've been waiting for. It combines the pedagogical strengths of Introduction to Seismology by P. Shearer (1999) and the breadth of coverage of Modern Global Seismology by T. Lay and T. Wallace (1995). The “price” of this combination is a rather lengthy text, but it is so well written that the length can be easily forgiven.

At first glance, An Introduction to Seismology Earthquakes, and Earth Structure appears to follow a very traditional path, beginning with a nice overview chapter on the relevance of seismology, followed by chapters on seismic waves that include stress and strain basics, Earth structure, earthquake sources, and seismology and plate tectonics. On closer inspection, though, the reader will find many chapter sections that are rather novel. Included among these are waves on a string, an excellent way to introduce students to the complexity of seismic waves; earthquake geodesy which establishes the important connection between seismology and deformation studies; and plate kinematics, a clearly presented “short-course” on plate motion studies. These sections, and others like them, significantly enhance the text. The latter two in particular help give the book a broader perspective that is rare in standard seismology texts.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2003EO220007

An Introduction to Seismology, Earthquakes, and Earth Structure

The rise of free-to-play and other service-based business models in the online gaming market brought to game publishers problems usually associated to markets like mobile telecommunications and credit cards, especially customer churn. Predictive models have long been used to address this issue in these markets, where companies have a considerable amount of demographic, economic, and behavioral data about their customers, while online game publishers often only have behavioral data. Simple time series’ feature representation schemes like RFM can provide reasonable predictive models solely based on online game players’ login records, but maybe without fully exploring the predictive potential of these data. We propose a frequency analysis approach for feature representation from login records for churn prediction modeling. These entries (from real data) were converted into fixed-length data arrays using four different methods, and then these were used as input for training probabilistic classifiers with the $k$ -nearest neighbors machine learning algorithm. The classifiers were then evaluated and compared using predictive performance metrics. One of the methods, the time-frequency plane domain analysis, showed satisfactory results, being able to theoretically increase the retention campaigns profits in more than 20% over the RFM approach.

Churn Prediction in Online Games Using Players’ Login Records: A Frequency Analysis Approach

One of the most crucial challenges in seismic data processing is the reduction of the noise in the data or improving the signal-to-noise ratio. In this study, the 1D undecimated discrete wavelet transform (UDWT) has been acquired to attenuate random noise and ground roll. Wavelet domain ground roll analysis (WDGA) is applied to find the ground roll energy in the wavelet domain. The WDGA will be a substitute method for thresholding in seismic data processing. To compare the effectiveness of the WDGA method, we apply the 1D double density discrete wavelet transform (DDDWT) using soft thresholding in the random noise reduction and ground roll attenuation processes. Seismic signals intersect with ground roll in the time and frequency domains. Random noise and ground roll have many undesirable effects on pre-stack seismic data, and result in an inaccurate velocity analysis for NMO correction. In this paper, the UDWT by using the WDGA technique and DDDWT (using the soft thresholding technique) and the regular Fourier based method as f?k transform will be used and compared for seismic denoising.

Seismic coherent and random noise attenuation using the undecimated discrete wavelet transform method with WDGA technique

Analyzing trends in temperature, precipitation and streamflow data over southern ontario and quebec using the discrete wavelet transform

This thesis considers the application of wavelets to problems involving multiple series
of temporal data. Wavelets have proven to be highly effective at extracting frequency
information from data. Their multi-scale nature enables the efficient description of both
transient and long-term signals. Furthermore, only a small number of wavelet coefficients
are needed to describe complicated signals and the wavelet transform is computationally
efficient.

In problems where frequency properties are known to be important, it is proposed that
a modelling approach which attempts to explain the response in terms of a multi-scale
wavelet representation of the explanatory series will be an improvement on standard
regression techniques. The problem with classical regression is that differing frequency
characteristics are not exploited and make the estimates of the model parameters less
stable. The proposed modelling method is presented with application to examples from
seismology and tomography.

In the first part of the thesis, we investigate the use of the non-decimated wavelet transform
in the modelling of data produced from a simulated seismology study. The fact that elastic
waves travel with different velocities in different rock types is exploited and wavelet
models are proposed to avoid the complication of predictions being unstable to small
changes in the input data, that is an inverse problem.

The second part of the thesis uses the non-decimated wavelet transform to model electrical
tomographic data, with the aim of process control. In industrial applications of electrical
tomography, multiple voltages are recorded between electrodes attached to the boundary
of, for example, a pipe. The usual first step of the analysis is then to reconstruct the
conductivity distribution within the pipe. The most commonly used approaches again lead
to inverse problems, and wavelet models are again used here to overcome this difficulty.
We conclude by developing the non-decimated multi-wavelet transform for use in the

modelling processes and investigate the improvements over scalar wavelets.

/pdf/wavelet-analysis-of-temporal-data-1y27yzk423.pdf

David Alexander Goodwin

Papers

Wavelet analysis of temporal data