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Journal Article

ObsPy: A Python Toolbox for Seismology

Moritz Beyreuther, Robert Barsch, Lion Krischer, Tobias Megies, Yannik Behr, Joachim Wassermann
Published: 01 May 2010
Seismological Research Letters (2010) 81 (3): 530–533.
DOI: https://doi.org/10.1785/gssrl.81.3.530
... in preprocessing such data. Although preprocessing work-flows are mostly very similar, few software standards exist to accomplish this task. The objective of ObsPy is to provide a Python toolbox that simplifies the usage of Python programming for seismologists. It is conceptually similar to SEATREE ( Milner...
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Example Jupyter Notebook from unit 1: ObsPy. (a) The screenshot detailing the blank notebook provided to students and (b) the screenshot demonstrating an example student input. The color version of this figure is available only in the electronic edition.

Example Jupyter Notebook from unit 1: ObsPy. (a) The screenshot detailing t...

Published: 26 May 2021
Figure 2. Example Jupyter Notebook from unit 1: ObsPy. (a) The screenshot detailing the blank notebook provided to students and (b) the screenshot demonstrating an example student input. The color version of this figure is available only in the electronic edition.
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Probability power spectral densities (PPSDs) calculated using the ObsPy PPSD module, which follows the methods of McNamara and Buland (2004). Gray lines are new high‐noise model (NHNM) and new low‐noise model (NLNM) from Peterson (1993). Color bar saturates at 30%, meaning all values above 30% are red. PPSDs are smoothed every 0.5 octave. (a–e) Geophones are the least‐sensitive instrument. (f) The TPH is the most sensitive across most frequencies. (g–j) Silicon Audios on the lander perform comparably to (k–o) Silicon Audios on the ground and (p–s) those in the remote locations. The color version of this figure is available only in the electronic edition.

Probability power spectral densities (PPSDs) calculated using the ObsPy PPS...

Published: 17 March 2021
Figure 4. Probability power spectral densities (PPSDs) calculated using the ObsPy PPSD module, which follows the methods of McNamara and Buland (2004) . Gray lines are new high‐noise model (NHNM) and new low‐noise model (NLNM) from Peterson (1993) . Color bar saturates at 30%, meaning all
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Component 2 PPSDs calculated using the Obspy PPSD module, which follows the methods of McNamara and Buland (2004). Component 2 is generally pointing in the east direction. Gray lines are NHNM and NLNM from Peterson (1993). Color bar saturates at 30%, meaning, all probability values exceeding 30% are red. (a–e) Geophones are the least‐sensitive instrument. (f) The TPH is the most sensitive across the most frequencies. (g–j) Silicon Audios on the lander perform comparably to (k–o) Silicon Audios on the ground and (p–s) those in the remote locations. The color version of this figure is available only in the electronic edition.

Component 2 PPSDs calculated using the Obspy PPSD module, which follows the...

Published: 17 March 2021
Figure A2. Component 2 PPSDs calculated using the Obspy PPSD module, which follows the methods of McNamara and Buland (2004) . Component 2 is generally pointing in the east direction. Gray lines are NHNM and NLNM from Peterson (1993) . Color bar saturates at 30%, meaning, all probability values
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A script using Pisces and ObsPy to retrieve event‐windowed waveforms from a database.

A script using Pisces and ObsPy to retrieve event‐windowed waveforms from a...

Published: 01 July 2014
Figure 7. A script using Pisces and ObsPy to retrieve event‐windowed waveforms from a database.
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Illustration of the lsforce workflow. Boxes at left show the required input of an ObsPy Stream object (gray) and the three objects created by lsforce classes (black); arrows indicate how they depend on each other. The gray text to the right of the ObsPy Stream indicates what needs to be appended to the trace stats for each channel in the stream prior to creation of the LSData object. For the three lsforce objects, the methods available for each object once initialized are shown to the right. For LSForce, the setup() and invert() methods must be called prior to the use of the plotting methods.

Illustration of the lsforce workflow. Boxes at left show the required input...

Published: 28 April 2021
Figure 2. Illustration of the lsforce workflow. Boxes at left show the required input of an ObsPy Stream object (gray) and the three objects created by lsforce classes (black); arrows indicate how they depend on each other. The gray text to the right of the ObsPy Stream indicates what needs
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Read times in milliseconds for all benchmarks in Table 2 that were testable in at least two of ObsPy, SAC, and SeisIO. A missing bar with text label NR indicates no reader. Most read times fall in the 10–100 ms range. SeisIO Ad Hoc (AH) and Seismic Unified Data System (SUDS) benchmarks are labeled with their respective values because the bars themselves are difficult to see. ObsPy sample list (SLIST) benchmark is labeled with its value because the full bar vastly exceeds the upper bound of the y axis.

Read times in milliseconds for all benchmarks in Table  2 that were testab...

Published: 29 April 2020
Figure 3. Read times in milliseconds for all benchmarks in Table  2 that were testable in at least two of ObsPy, SAC, and SeisIO. A missing bar with text label NR indicates no reader. Most read times fall in the 10–100 ms range. SeisIO Ad Hoc (AH) and Seismic Unified Data System (SUDS
Journal Article

PhasePApy: A Robust Pure Python Package for Automatic Identification of Seismic Phases

Chen Chen, Austin A. Holland
Published: 31 August 2016
Seismological Research Letters (2016) 87 (6): 1384–1396.
DOI: https://doi.org/10.1785/0220160019
...Chen Chen; Austin A. Holland ABSTRACT We developed a Python phase identification package: the PhasePApy for earthquake data processing and near‐real‐time monitoring. The package takes advantage of the growing number of Python libraries including Obspy. All the data formats supported by Obspy can...
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Onset examples of (a) two teleseisms, (b) two regional earthquakes, and (c) two local earthquakes. The red markers indicate the P‐wave onset obtained using the ObsPy automatic picker.

Onset examples of (a) two teleseisms, (b) two regional earthquakes, and (c)...

Published: 31 May 2017
Figure 3. Onset examples of (a) two teleseisms, (b) two regional earthquakes, and (c) two local earthquakes. The red markers indicate the P ‐wave onset obtained using the ObsPy automatic picker.
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Waveforms of (a) a teleseism, (b) a regional earthquake, and (c) a local earthquake. The red markers indicate the P‐wave onset obtained using the ObsPy automatic picker. Blue marker indicates the trigger‐off marker.

Waveforms of (a) a teleseism, (b) a regional earthquake, and (c) a local ea...

Published: 31 May 2017
Figure 2. Waveforms of (a) a teleseism, (b) a regional earthquake, and (c) a local earthquake. The red markers indicate the P ‐wave onset obtained using the ObsPy automatic picker. Blue marker indicates the trigger‐off marker.
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The technical architecture used in our cloud‐based streaming workflow. We used Amazon EC2 to build the cluster, Kubernetes for cluster management, Helm for software installation, Dask for distributed parallelism in Python, and ObsPy for data acquisition and processing.

The technical architecture used in our cloud‐based streaming workflow. We u...

Published: 18 March 2020
Figure 2. The technical architecture used in our cloud‐based streaming workflow. We used Amazon EC2 to build the cluster, Kubernetes for cluster management, Helm for software installation, Dask for distributed parallelism in Python, and ObsPy for data acquisition and processing.
Journal Article

Follow the Trace: Becoming a Seismo‐Detective with a Campus‐Based Raspberry Shake Seismometer

Eric Löberich, Maureen D. Long
Published: 28 May 2024
Seismological Research Letters (2024) 95 (4): 2538–2553.
DOI: https://doi.org/10.1785/0220230365
.... In particular, we highlight a series of ObsPy‐based exercises that will be used in courses taught in our department, including our upper‐level Introduction to Seismology course and our undergraduate classes on Natural Disasters and Forensic Geoscience. Figure  3 shows overlays of lfbf‐ and hfbf‐filtered time...
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Memory use and overhead for all benchmarks in Table 2 that were testable in at least two of ObsPy, Seismic Analysis Code (SAC), and SeisIO. (a) Memory usage and file sizes on disk; (b) memory overhead. The y axis is logarithmic. A missing bar with text label NR indicates no reader.

Memory use and overhead for all benchmarks in Table  2 that were testable ...

Published: 29 April 2020
Figure 2. Memory use and overhead for all benchmarks in Table  2 that were testable in at least two of ObsPy, Seismic Analysis Code (SAC), and SeisIO. (a) Memory usage and file sizes on disk; (b) memory overhead. The y axis is logarithmic. A missing bar with text label NR indicates no reader.
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Synthetic data for the geometry shown in Figure 3. The stations are enumerated with increasing distance from the source location. (a) Raw synthetic seismograms. (b) Low-pass frequency filtered data. The filter is a zero-phase Butterworth low-pass of order 4 with a corner frequency of 0.1 Hz (calculated using ObSpy; Beyreuther et al., 2010).

Synthetic data for the geometry shown in Figure  3 . The stations are enume...

Published: 27 February 2015
of 0.1 Hz (calculated using ObSpy; Beyreuther et al., 2010 ).
Journal Article

MsPASS: A Data Management and Processing Framework for Seismology

Yinzhi Wang, Gary L. Pavlis, Weiming Yang, Jinxin Ma
Published: 15 September 2021
Seismological Research Letters (2022) 93 (1): 426–434.
DOI: https://doi.org/10.1785/0220210182
... heavily on the widely used ObsPy toolkit. It automates many database operations and provides a mechanism to automatically save the processing history for reproducibility. The synthesis of these components can provide flexibility to adapt to a wide range of data processing workflows. We demonstrate...
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Journal Article

Learning in a Crisis: Online Skill Building Workshop Addresses Immediate Pandemic Needs and Offers Possibilities for Future Trainings

Michael Brudzinski, Michael Hubenthal, Shannon Fasola, Em Schnorr
Published: 07 July 2021
Seismological Research Letters (2021) 92 (5): 3215–3230.
DOI: https://doi.org/10.1785/0220200472
..., and an optional final project. Every other week for 12 weeks, a module with ∼ 6 assignments was released to build skills with Linux, Generic Mapping Tools, Seismic Analysis Code, webservices, seismic network processing, Python, ObsPy, and Jupyter notebooks. A final module focused on competitiveness for graduate...
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Journal Article

Common‐Reflection‐Point‐Based Prestack Depth Migration for Imaging Lithosphere in Python: Application to the Dense Warramunga Array in Northern Australia

Weijia Sun, Brian L. N. Kennett
Published: 15 July 2020
Seismological Research Letters (2020) 91 (5): 2890–2899.
DOI: https://doi.org/10.1785/0220200078
..., which builds on the widely used the seismological Obspy toolbox. The ACC package is written in the open‐source and free Python programming language (3.0 or newer) and has been extensively tested in an Anaconda Python environment. The package is simple and friendly to use and runs on all major operating...
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Ray paths corresponding to the waveform examples shown in Figure 9, as calculated using the ObsPy TauP package. P‐wave velocities at the depths of 4, 6, 10, 16, and 26 km (dashed lines) are given for reference. Note that converted phase depths were calculated along the horizontal and vertical components of the ray paths. The color version of this figure is available only in the electronic edition.

Ray paths corresponding to the waveform examples shown in Figure  9 , as ca...

Published: 17 September 2019
Figure 10. Ray paths corresponding to the waveform examples shown in Figure  9 , as calculated using the ObsPy TauP package. P ‐wave velocities at the depths of 4, 6, 10, 16, and 26 km (dashed lines) are given for reference. Note that converted phase depths were calculated along the horizontal
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Synthetic data for the OBS geometry shown in Figure 6. The stations are enumerated with increasing distance from the source location. (a) Synthetic data, output from the E3D code. (b) Low-pass frequency-filtered data. The filter is a zero-phase Butterworth low-pass of order 4 with a corner frequency of 0.1 Hz (calculated using ObSpy; Beyreuther et al., 2010).

Synthetic data for the OBS geometry shown in Figure  6 . The stations are e...

Published: 27 February 2015
with a corner frequency of 0.1 Hz (calculated using ObSpy; Beyreuther et al., 2010 ).
Journal Article

Improving the Detection of Low‐Magnitude Seismicity Preceding the M w 6.3 L’Aquila Earthquake: Development of a Scalable Code Based on the Cross Correlation of Template Earthquakes

Alessandro Vuan, Monica Sugan, Giorgio Amati, Aitaro Kato
Published: 09 January 2018
Bulletin of the Seismological Society of America (2018) 108 (1): 471–480.
DOI: https://doi.org/10.1785/0120170106
...Alessandro Vuan; Monica Sugan; Giorgio Amati; Aitaro Kato Abstract A scalable procedure to detect microseismicity based on the cross correlation of well‐located template events is developed using Python, Numpy, and ObsPy toolkits. With this technique, we investigate the spatiotemporal evolution...
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