Applying Haar and Mexican hat wavelets to significantly improve the performance of the New Zealand GeoNet P-phase picker for the 2008 Matata region swarm
MetadataShow full item record
The New Zealand GeoNet P-phase picker's performance can be significantly improved, by up to 70%, when Mexican hat and Haar wavelet scale thresholding (WST) is implemented in the preprocessing stage of manually picked events. In total, 811 events with the required spatial and quality criteria from the 2008 Matata swarm were studied. The WST was applied to manually processed waveforms recorded by key stations, including 48 waveforms recorded by URZ; 47 by stations LIRZ, MARZ, and OPRZ; 64 by EDRZ; 33 by KARZ; and 47 by OPRZ. The signal-to-noise ratios of these key stations were dominated by additive noise rather than weaknesses introduced by the radiation pattern. We found that the Mexican hat WST produced the largest amount of retrieved and revised P onsets for station EDRZ (70.3%). The next best station was KARZ, which had 66.7% of picks showing improvement and only 3% of picks adversely affected. The main best scales (i.e., the wavelet's degree of compression) for stations EDRZ and KARZ were scales 1 and 2, respectively. Haar WST for the OPRZ recordings resulted in a total improvement greater than 50%, with the lowest adversely affected picked onsets of 2.1%. Similarly, the Haar WST retrieved a considerable number of missed onsets at the TGRZ and LIRZ stations (25.5% and 20%, respectively), with a minor effect on the performance. The results confirmed that WST served to significantly enhance the performance of the GeoNet P picker for the chosen Matata earthquakes due to the frequency localization of the WSR compared to the current Butterworth filtering scheme.
Showing items related by title, author, creator and subject.
A Detailed Spatiotemporal Wavelet Study to Improve the P‐Phase Picking Performance for the 2007–2010 Shallow Earthquake Swarms near Matata, New ZealandJafarzadeh Rastin, Sepideh; Gledhill, K.; Unsworth, C. (2018)We report a 45% overall improvement by incorporating wavelet scale thresholding (WST) into the GeoNet P-phase picker. We study the spatiotemporal effect of the WST on performance of the automatic phase picking using 6471 ...
Sarukkalige, Priyantha Ranjan; Badrzadeh, Honey (2014)Accurate river flow forecasts play a key role in sustainable water resources and environmental management. Recently, computational intelligence approaches have become increasingly popular due to minimum information ...
Badrzadeh, Honey; Sarukkalige, Priyantha Ranjan; Jayawardena, A. (2018)In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS ...