A. T. Ringler

Strong-motion observations of the M 7.8 Gorkha, Nepal, earthquake sequence and development of the N-shake strong-motion network

We present and describe strong-motion data observations from the 2015 M 7.8 Gorkha, Nepal, earthquake sequence collected using existing and new Quake-Catcher Network (QCN) and U.S. Geological Survey NetQuakes sensors located in the Kathmandu Valley. A comparison of QCN data with waveforms recorded by a conventional strong-motion (NetQuakes) instrument validates the QCN data. We present preliminary analysis of spectral accelerations, and peak ground acceleration and velocity for earthquakes up to M 7.3 from the QCN stations, as well as preliminary analysis of the mainshock recording from the NetQuakes station. We show that mainshock peak accelerations were lower than expected and conclude the Kathmandu Valley experienced a pervasively nonlinear response during the mainshock. Phase picks from the QCN and NetQuakes data are also used to improve aftershock locations. This study confirms the utility of QCN instruments to contribute to ground-motion investigations and aftershock response in regions where conventional instrumentation and open-access seismic data are limited. Initial pilot installations of QCN instruments in 2014 are now being expanded to create the Nepal–Shaking Hazard Assessment for Kathmandu and its Environment (N-SHAKE) network. Online Material: Figures of Pg arrivals, earthquake locations, epicenter change vectors, and travel-time misfit vector residuals, and tables of QCN and NetQuake stations and relocated hypocenter timing, location, and magnitude.

Relative azimuth inversion by way of damped maximum correlation estimates

Horizontal seismic data are utilized in a large number of Earth studies. Such work depends on the published orientations of the sensitive axes of seismic sensors relative to true North. These orientations can be estimated using a number of different techniques: SensOrLoc (Sensitivity, Orientation and Location), comparison to synthetics (Ekstrom and Busby, 2008), or by way of magnetic compass. Current methods for finding relative station azimuths are unable to do so with arbitrary precision quickly because of limitations in the algorithms (e.g. grid search methods). Furthermore, in order to determine instrument orientations during station visits, it is critical that any analysis software be easily run on a large number of different computer platforms and the results be obtained quickly while on site. We developed a new technique for estimating relative sensor azimuths by inverting for the orientation with the maximum correlation to a reference instrument, using a non-linear parameter estimation routine. By making use of overlapping windows, we are able to make multiple azimuth estimates, which helps to identify the confidence of our azimuth estimate, even when the signal-to-noise ratio (SNR) is low. Finally, our algorithm has been written as a stand-alone, platform independent, Java software package with a graphical user interface for reading and selecting data segments to be analyzed.

Repeatability of Testing a Small Broadband Sensor in the Albuquerque Seismological Laboratory Underground Vault

Abstract Variability in seismic instrumentation performance plays a fundamental role in our ability to carry out experiments in observational seismology. Many such experiments rely on the assumed performance of various seismic sensors as well as on methods to isolate the sensors from nonseismic noise sources. We look at the repeatability of estimating the self‐noise, midband sensitivity, and the relative orientation by comparing three collocated Nanometrics Trillium Compact sensors. To estimate the repeatability, we conduct a total of 15 trials in which one sensor is repeatedly reinstalled, alongside two undisturbed sensors. We find that we are able to estimate the midband sensitivity with an error of no greater than 0.04% with a 99th percentile confidence, assuming a standard normal distribution. We also find that we are able to estimate mean sensor self‐noise to within ±5.6  dB with a 99th percentile confidence in the 30–100‐s‐period band. Finally, we find our relative orientation errors have a mean difference in orientation of 0.0171° from the reference, but our trials have a standard deviation of 0.78°. Electronic Supplement: Table of dates of the trials used as well as Q – Q plots for the statistics collected from the sensor tests.

Broadband Seismic Noise Attenuation versus Depth at the Albuquerque Seismological Laboratory

Abstract Seismic noise induced by atmospheric processes such as wind and pressure changes can be a major contributor to the background noise observed in many seismograph stations, especially those installed at or near the surface. Cultural noise such as vehicle traffic or nearby buildings with air handling equipment also contributes to seismic background noise. Such noise sources fundamentally limit our ability to resolve earthquake‐generated signals. Many previous seismic noise versus depth studies focused separately on either high‐frequency (>1  Hz) or low‐frequency (

Seismometer Self-Noise and Measuring Methods

Seismometer self-noise is usually not considered when selecting and using seismic waveform data in scientific research as it is typically assumed that the self-noise is negligibly small compared to seismic signals. However, instrumental noise is part of the noise in any seismic record, and in particular, at frequencies below a few mHz, the instrumental noise has a frequency-dependent character and may dominate the noise. When seismic noise itself is considered as a carrier of information, as in seismic interferometry (e.g., Chaput et al. 2012), it becomes extremely important to estimate the contribution of instrumental noise to the recordings. Noise in seismic recordings, commonly called seismic background noise or ambient Earth noise, usually refers to the sum of the individual noise sources in a seismic recording in the absence of any earthquake signal. Site noise (e.g., cultural sources, nearby tilt signals, etc.) and noise introduced by the sensitivity of an instrument to non-seismic signals (e.g., temperature and pressure variations, magnetic field changes, etc.) both contribute to the ambient seismic noise levels. The background noise ultimately defines a lower limit for the ability to detect and characterize various seismic signals of interest. Background noise levels have also been found to introduce a systematic bias in arrival times because the amplitude of the seismic phase must rise above the station’s noise levels (Rӧhm et al. 1999). The upper limit of useful signals is governed by the clip level of the recording system (the point at which a recording system’s output is no longer a linearly time-invariant representation of the input). Site noise can be reduced by careful site selection (e.g., hard rock far from strong noise sources) and by emplacing instruments in good vaults or boreholes. It is also possible to reduce sensitivity to non-seismic signals by thermal insulation and appropriate shielding such as pressure chambers (Hanka 2000). At quiet sites with well-installed instrumentation, instrument noise may be the dominant noise source (Berger et al. 2004); this is especially true for long-period seismic data (>100 s period) on very broadband instruments (e.g., Streckeisen STS-1 seismometer). The interpretation of such data only makes sense if the instrumental noise level is known. Also, research on noise levels in seismic recordings, the effect of noise reduction by the installation technique, and the nature and contribution of different noise sources to the recordings require knowledge of instrumental self-noise to rule out bias from the instrumentation self-noise.

Detection and Characterization of Pulses in Broadband Seismometers

Abstract Pulsing—caused either by mechanical or electrical glitches, or by microtilt local to a seismometer—can significantly compromise the long‐period noise performance of broadband seismometers. High‐fidelity long‐period recordings are needed for accurate calculation of quantities such as moment tensors, fault‐slip models, and normal‐mode measurements. Such pulses have long been recognized in accelerometers, and methods have been developed to correct these acceleration steps, but considerable work remains to be done in order to detect and correct similar pulses in broadband seismic data. We present a method for detecting and characterizing the pulses using data from a range of broadband sensor types installed in the Global Seismographic Network. The technique relies on accurate instrument response removal and employs a moving‐window approach looking for acceleration baseline shifts. We find that pulses are present at varying levels in all sensor types studied. Pulse‐detection results compared with average daily station noise values are consistent with predicted noise levels of acceleration steps. This indicates that we can calculate maximum pulse amplitude allowed per time window that would be acceptable without compromising long‐period data analysis.

Obtaining Changes in Calibration‐Coil to Seismometer Output Constants Using Sine Waves

The midband sensitivity of a broadband seismometer is one of the most commonly used parameters from station metadata. Thus, it is critical for station operators to robustly estimate this quantity with a high degree of accuracy. We develop an in situ method for estimating changes in sensitivity using sine‐wave calibrations, assuming the calibration coil and its drive are stable over time and temperature. This approach has been used in the past for passive instruments (e.g., geophones) but has not been applied, to our knowledge, to derive sensitivities of modern force‐feedback broadband seismometers. We are able to detect changes in sensitivity to well within 1%, and our method is capable of detecting these sensitivity changes using any frequency of sine calibration within the passband of the instrument.

Sensor Suite: The Albuquerque Seismological Laboratory Instrumentation Testing Suite

In order to allow the casual user (geophysicists without expertise in instrumentation) to quickly and consistently determine several parameters critical to determining seismometer health, we have developed a new seismometer testing software package called: Albuquerque Seismological Laboratory (ASL) Sensor Test Suite. The package is written in Java and makes use of Seismological Exchange for Earthquake Data (SEED) format. The sensor tests, which include computing sensor self-noise, relative gain, azimuth, and processing calibrations to determine poles and zeros, can be calculated in a standardized way so that results can be directly compared between tests and between different groups. For the self-noise and the relative azimuth, we also include three component versions of these tests to allow for the case of sensors with potentially different orientations (e.g. boreholes). Our goal is to focus on a few of the instrumentation tests we view as critical when verifying a sensor’s performance. The package is extremely flexible so that it can be used to troubleshoot issues with a single sensor or to compute multi-component self-noise of several sensors in a laboratory setting. The software has been made available on GitHub (https://github.com/usgs/asl-sensor-suite) with the hope that it will be useful for other seismologists who need to quickly verify various sensor parameters without having to write their own versions of the algorithms. Furthermore, by using a common platform and processing algorithms it becomes possible to compare results between different tests and between different groups with similar processing methods being used for both. CAPTION: Upper Panel Power Spectral Density (PSD) estimates (solid lines) for the vertical components of a Nanometrics Trillium 360 sensor (green), as well as the primary KS-54000 (red, location code 00) at IRIS/USGS network (network code IU) station ANMO (Albuquerque, New Mexico), and the secondary sensor at ANMO (location code 10) a Nanometrics Trillium 120. The selfnoise estimates are shown as dashed spectra of slightly darker color. We have included the Peterson (1993) New Low/High-Noise Model (NLNM/NHNM) in black for reference. Lower Panel Azimuth estimate of the IRIS/USGS (network code IU) station WVT (Waverly, Tennessee). The azimuth of the primary Streckeisen STS-6 sensor (location code 00) horizontal components (LH1, red; LH2, blue) were estimated using a co-located Trillium compact (green) where the sensor was oriented to North using a gyroscopic compass. The azimuth of the STS-6 was found to be 340 degrees (left). The time windows used for this estimate are shown on the right.