Reinoud Sleeman

Robust automatic P-phase picking : an on-line implementation in the analysis of broadband seismogram recordings

Abstract The onset of a seismic signal is determined through joint AR modeling of the noise and the seismic signal, and the application of the Akaike Information Criterion (AIC) using the onset time as parameter. This so-called AR-AIC phase picker has been tested successfully and implemented on the Z-component of the broadband station HGN to provide automatic P-phase picks for a rapid warning system. The AR-AIC picker is shown to provide accurate and robust automatic picks on a large experimental database. Out of 1109 P-phase onsets with signal-to-noise ratio (SNR) above 1 from local, regional and teleseismic earthquakes, our implementation detects 71% and gives a mean difference with manual picks of 0.1 s. An optimal version of the well-established picker of Baer and Kradolfer [Baer, M., Kradolfer, U., An automatic phase picker for local and teleseismic events, Bull. Seism. Soc. Am. 77 (1987) 1437–1445] detects less than 41% and gives a mean difference with manual picks of 0.3 s using the same dataset.

Three-Channel Correlation Analysis: A New Technique to Measure Instrumental Noise of Digitizers and Seismic Sensors

This article describes a new method to estimate (1) the self-noise as a function of frequency of three-channel, linear systems and (2) the relative transfer functions between the channels, based on correlation analysis of recordings from a common, coherent input signal. We give expressions for a three-channel model in terms of power spectral densities. The method is robust, compared with the conven- tional two-channel approach, as both the self-noise and the relative transfer functions are extracted from the measurements only and do not require a priori information about the transfer function of each channel. We use this technique to measure and model the self-noise of digitizers and to identify the frequency range in which the digitizer can be used without precaution. As a consequence the method also reveals under which conditions the interpretation of data may be biased by the recording system. We apply the technique to a Quanterra Q4120 datalogger and to a Network of Autonomously Recording Seismographs (NARS) datalogger. At a sampling rate of 20 samples/sec, the noise of the Q4120 digitizer is modeled by superposition of a flat, 23.6-bit spectrum and a 24.7-bit spectrum with 1/f 1.55 noise. For the NARS datalogger the noise level is modeled by superposition of a 20.8-bit flat spectrum and a 23.0-bit spectrum with 1/f 1.0 noise. The measured gain ratios between the digi- tizers in the Q4120 datalogger, smoothed over a tenth of a decade between 0.01 Hz and 8 Hz for data sampled with 20 samples/sec, are within 1.6% (or 0.14 dB) of the values given by the manufacturer. Finally, we show an example of seismic background noise observations at station HGN as recorded by both an STS-1 and a STS-2 sensor. Between 0.01 and 0.001 Hz the vertical STS-2 noise levels are 10-15 dB above the STS-1 observations. The Quanterra Q4120 digitizer noise model enables us to exclude the contribution of the digitizer noise to be responsible for this difference.

The Engineering Strong‐Motion Database: A Platform to Access Pan‐European Accelerometric Data

This article describes the Engineering Strong‐Motion Database (ESM), developed in the framework of the European project Network of European Research Infrastructures for Earthquake Risk Assessment and Mitigation (NERA, see [Data and Resources][1]). ESM is specifically designed to provide end users only with quality‐checked, uniformly processed strong‐motion data and relevant parameters and has done so since 1969 in the Euro‐Mediterranean region. The database was designed for a large variety of stakeholders (expert seismologists, earthquake engineers, students, and professionals) with a user‐friendly and straightforward web interface. Users can access earthquake and station information and download waveforms of events with magnitude≥4.0 (unprocessed and processed acceleration, velocity, and displacement, and acceleration and displacement response spectra at 5% damping). Specific tools are also available to users to process strong‐motion data and select ground‐motion suites for code‐based seismic structural analyses. [1]: #sec-13

Introducing the European Rapid Raw Strong‐Motion Database

We present the European Rapid Raw Strong‐Motion database (RRSM), a new Europe‐wide system that provides parameterized earthquake ground‐motion information, as well as access to waveform data, within minutes of the occurrence of any earthquake with M ≥3.5 occurring in the European–Mediterranean region. The RRSM is different from traditional platforms for disseminating earthquake strong‐motion data in Europe, which focus on providing reviewed, processed strong‐motion parameters, typically with significant delays. The RRSM provides rapid open access to raw waveform data and metadata and does not rely on manual waveform processing. The RRSM targets seismologists and strong‐motion data analysts, earthquake and geotechnical engineers, international earthquake response agencies, and the educated general public. The database is accessible online (see [Data and Resources][1]). Users can query earthquake information, peak ground‐motion parameters, and select and download earthquake waveforms. The RRSM database is populated using the waveform processing module scwfparam , which is integrated in SeisComP3. Processing is triggered using earthquake parameters provided by the European–Mediterranean Seismological Center and uses all significant waveform data that are available in the European Integrated waveform Data Archive (EIDA). EIDA consists of broadband and strong‐motion data from across Europe, and the majority of these data are available in near real time. All relevant, on‐scale open EIDA data are processed for the RRSM. As the EIDA community is continually growing, the already significant number of strong‐motion stations is also increasing and the importance of the RRSM database is expected to grow further in time. Real‐time RRSM processing started in September 2014, whereas offline reprocessing was carried out for all M 4.5+ events that occurred since January 2005. [1]: #sec-7

WFCatalog: A catalogue for seismological waveform data

This paper reports advances in seismic waveform description and discovery leading to a new seismological service and presents the key steps in its design, implementation and adoption. This service, named WFCatalog, which stands for waveform catalogue, accommodates features of seismological waveform data. Therefore, it meets the need for seismologists to be able to select waveform data based on seismic waveform features as well as sensor geolocations and temporal specifications. We describe the collaborative design methods and the technical solution showing the central role of seismic feature catalogues in framing the technical and operational delivery of the new service. Also, we provide an overview of the complex environment wherein this endeavour is scoped and the related challenges discussed. As multi-disciplinary, multi-organisational and global collaboration is necessary to address todays challenges, canonical representations can provide a focus for collaboration and conceptual tools for agreeing directions. Such collaborations can be fostered and formalised by rallying intellectual effort into the design of novel scientific catalogues and the services that support them. This work offers an example of the benefits generated by involving cross-disciplinary skills (e.g. data and domain expertise) from the early stages of design, and by sustaining the engagement with the target community throughout the delivery and deployment process.