Automatic S-Wave Picker for Local Earthquake Tomography

Abstract

High-resolution seismic tomography at local and regional scales requires large and consistent sets of arrival-time data. Algorithms combining accurate picking with an automated quality classification can be used for repicking waveforms and compiling large arrival-time data sets suitable for tomographic inversion. S-wave velocities represent a key parameter for petrological interpretation, improved hypocenter determination, as well as for seismic hazard models. In our approach, we combine three commonly used phase detection and picking methods in a robust S-wave picking procedure. Information from the different techniques provides an in situ estimate of timing uncertainty and of the reliability of the automatic phase identification. Automatic picks are compared against manually picked reference picks of selected earthquakes in the Alpine region. The average accuracy of automatic picks and their classification is comparable with the reference picks, although a higher number of picks is downgraded to lower quality classes by the automatic picker. In the production-mode, we apply the picker to a data set of 552 earthquakes in the Alps recorded at epicentral distances ≤ 150 km. The resulting data set includes about 2500 S phases with an upper error bound of 0.27 sec.

Geometry of the Pamir‐Hindu Kush intermediate‐depth earthquake zone from local seismic data

“…picks in two confusion matrices ( Figure A3). The hit rates we obtain are relatively low (56.4% of the human analyst for P, 49.9% for S), which is not only due to the rather conservative nature of the two picking algorithms' weighting schemes [see Di Stefano et al, 2006;Diehl et al, 2009b], but also reflects the loss of some picks in the multiple relocations along our processing chain. Our automated classification is shown to be conservative, as we observe nearly no picks being upgraded from the lowest visually assigned quality classes to the highest automatic ones (red fields in the confusion matrices).…”

“…Confusion matrices for P (left) and S (right) picks, comparing handpicks to the ones obtained by the automatic procedure described in the text. A confusion matrix is a way of representing the performance of any kind of classification algorithm that is often used in machine learning [for definition, see Kohavi and Provost, 1998] and has been recently adopted for the evaluation of automatic seismic arrival time picking algorithms [e.g., Di Stefano et al, 2006;Diehl et al, 2009b;Küperkoch et al, 2010]. Its columns here refer to the weighting classes as assigned by the two picking algorithms (whose performance is to be evaluated), and its rows represent the weighting scheme applied by the human analyst (which is the reference here).…”

“…[46] Subsequently, S onsets were automatically determined using the software spicker [Diehl et al, 2009b], which combines three different strategies of identifying S picks:…”

Local and regional minimum 1D models for earthquake location and data quality assessment in complex tectonic regions: application to Switzerland

“…Diehl et al 2009a;Haslinger et al 1999;Husen et al 2003;Husen et al 1999;Husen and Smith 2004;Imposa et al 2009;Kissling and Lahr 1991). Nevertheless, the use of a simple 1D velocity structure in combination with station delays raises the question how useful the minimum 1D model concept can be when applied to complex tectonic regions with significant three-dimensional (3D) variations in seismic velocities.…”

Integration of Arrival-Time Datasets for Consistent Quality Control: A Case Study of Amphibious Experiments along the Middle America Trench

“…Though it has been shown that human and automatic picks are comparable (Sleeman and van Eck, 1999;Leonard, 2000;, typical automatic picking does not provide a tangible assessment of the actual quality of the automatic pick. Significant improvement in resolution and reliability of local to regional tomographic studies can be made by automatically repicking and weighting data (Di Stefano et al, 2006;Diehl et al, 2009), resulting in either adjusted picked onsets or increased accuracy differential times. Picking error can additionally be reduced using cross correlation (CC) of similar events (Got et al, 1994;Dodge et al, 1995;Shearer, 1997;Rubin et al, 1998;Waldhauser and Ellsworth, 2000;Rowe et al, 2002).…”