Lion Krischer

Bio: Lion Krischer is an academic researcher from ETH Zurich. The author has contributed to research in topics: Waveform & Inversion (meteorology). The author has an hindex of 16, co-authored 42 publications receiving 2247 citations. Previous affiliations of Lion Krischer include Ludwig Maximilian University of Munich.

ObsPy: A Python Toolbox for Seismology

Abstract: The wide variety of computer platforms, file formats, and methods to access seismological data often requires considerable effort 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 and Thorsten 2009) or the exploration seismic software project MADAGASCAR (http://www.reproducibility.org). In ObsPy the following essential seismological processing routines are implemented and ready to use: reading and writing data only SEED/MiniSEED and Dataless SEED (http://www.iris.edu/manuals/SEEDManual_V2.4.pdf), XML-SEED (Tsuboi et al. 2004), GSE2 (http://www.seismo.ethz.ch/autodrm/downloads/provisional_GSE2.1.pdf) and SAC (http://www.iris.edu/manuals/sac/manual.html), as well as filtering, instrument simulation, triggering, and plotting. There is also support to retrieve data from ArcLink (a distributed data request protocol for accessing archived waveform data, see Hanka and Kind 1994) or a SeisHub database (Barsch 2009). Just recently, modules were added to read SEISAN data files (Havskov and Ottemoller 1999) and to retrieve data with the IRIS/FISSURES data handling interface (DHI) protocol (Malone 1997). Python gives the user all the features of a full-fledged programming language including a large collection of scientific open-source modules. ObsPy extends Python by providing direct access to the actual time series, allowing the use of powerful numerical array-programming modules like NumPy (http://numpy.scipy.org) or SciPy (http://scipy.org). Results can be visualized using modules such as matplotlib (2D) (Hunter 2007) or MayaVi (3D) (http://code.enthought.com/projects/mayavi/). This is an advantage over the most commonly used seismological analysis packages SAC, SEISAN, SeismicHandler (Stammler 1993), or PITSA (Scherbaum and Johnson 1992), which do not provide methods for general numerical array manipulation. Because Python and its previously mentioned modules are open-source, there …

ObsPy: A Python Toolbox for Seismology

Abstract: The wide variety of computer platforms, file formats, and methods to access seismological data often requires considerable effort 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 and Thorsten 2009) or the exploration seismic software project MADAGASCAR (http://www.reproducibility.org). In ObsPy the following essential seismological processing routines are implemented and ready to use: reading and writing data only SEED/MiniSEED and Dataless SEED (http:// www.iris.edu/manuals/SEEDManual_V2.4.pdf), XML-SEED (Tsuboi et al. 2004), GSE2 (http://www.seismo.ethz.ch/autodrm/downloads/provisional_GSE2.1.pdf) and SAC (http:// www.iris.edu/manuals/sac/manual.html), as well as filtering, instrument simulation, triggering, and plotting. There is also support to retrieve data from ArcLink (a distributed data request protocol for accessing archived waveform data, see Hanka and Kind 1994) or a SeisHub database (Barsch 2009). Just recently, modules were added to read SEISAN data files (Havskov and Ottemoller 1999) and to retrieve data with the IRIS/FISSURES data handling interface (DHI) protocol (Malone 1997). Python gives the user all the features of a full-fledged programming language including a large collection of scientific open-source modules. ObsPy extends Python by providing direct access to the actual time series, allowing the use of powerful numerical array-programming modules like NumPy (http://numpy.scipy.org) or SciPy (http://scipy.org). Results can be visualized using modules such as matplotlib (2D) (Hunter 2007) or MayaVi (3D) (http://code.enthought.com/ projects/mayavi/). This is an advantage over the most commonly used seismological analysis packages SAC, SEISAN, SeismicHandler (Stammler 1993), or PITSA (Scherbaum and Johnson 1992), which do not provide methods for general numerical array manipulation. Because Python and its previously mentioned modules are open-source, there are no restrictions due to licensing. This is a clear advantage over the proprietary product MATLAB (http://www.mathworks.com) in combination with MatSeis (Creager 1997) or CORAL (Harris and Young 1997), where the number of concurrent processes is limited by a costly and restricting license policy. Additionally, Python is known for its intuitive syntax. It is platform independent, and its rapidly growing popularity extends beyond the seismological community (see, e.g., Olsen and Ely 2009). Python is used in various fields because its comprehensive standard library provides tools for all kinds of tasks (e.g., complete Web servers can be written in a few lines with standard modules). It has excellent features for wrapping external shared C or FORTRAN libraries, which are used within ObsPy to access libraries for manipulating MiniSEED (libmseed; http://www.iris.edu/pub/programs) and GSE2 (gse_util; http://www.orfeus-eu.org/Software/softwarelib.html#gse) volumes. Similarly, seismologists may wrap their own C or FORTRAN code and thus are able to quickly develop powerful and efficient software. In the next section we will briefly introduce the capabilities of ObsPy by demonstrating the data conversion of SAC files to MiniSEED volumes, removing the instrument response, applying a low-pass filter, and plotting the resulting trace. We then give an overview on how to access an external C or FORTRAN library from within Python.

ObsPy: a bridge for seismology into the scientific Python ecosystem

Abstract: The Python libraries NumPy and SciPy are extremely powerful tools for numerical processing and analysis well suited to a large variety of applications We developed ObsPy (http://obspyorg), a Python library for seismology intended to facilitate the development of seismological software packages and workflows, to utilize these abilities and provide a bridge for seismology into the larger scientific Python ecosystem Scientists in many domains who wish to convert their existing tools and applications to take advantage of a platform like the one Python provides are confronted with several hurdles such as special file formats, unknown terminology, and no suitable replacement for a non-trivial piece of software We present an approach to implement a domain-specific time series library on top of the scientific NumPy stack In so doing, we show a realization of an abstract internal representation of time series data permitting I/O support for a diverse collection of file formats Then we detail the integration and repurposing of well established legacy codes, enabling them to be used in modern workflows composed in Python Finally we present a case study on how to integrate research code into ObsPy, opening it to the broader community While the implementations presented in this work are specific to seismology, many of the described concepts and abstractions are directly applicable to other sciences, especially to those with an emphasis on time series analysis

ObsPy – What can it do for data centers and observatories?

Abstract: Data acquisition by seismic centers relies on real-time systems, like SeisComP3, Antelope and Earthworm. However, these are complex systems that are designed for fast and precisely defined standard real-time analyses. Therefore, it is not a simple task to access or modify internal routines, and to integrate them into custom-processing workflows or to perform in-depth data analyses. Often a library is necessary that provides convenient access to data and allows easy control over all of the operations that are to be performed on the data. ObsPy is such a library, which is designed to access and process seismological waveform data and metadata. We use short and simple examples here to demonstrate how effective it is to use Python for seismological data analysis. Then, we illustrate the general capabilities of ObsPy, and highlight some of its specific aspects that are relevant for seismological data centers and observatories, through presentation of real-world examples. Finally, we demonstrate how the ObsPy library can be used to develop custom graphical user interface applications.

Instaseis: instant global seismograms based on a broadband waveform database

Abstract: . We present a new method and implementation (Instaseis) to store global Green's functions in a database which allows for near-instantaneous (on the order of milliseconds) extraction of arbitrary seismograms. Using the axisymmetric spectral element method (AxiSEM), the generation of these databases, based on reciprocity of the Green's functions, is very efficient and is approximately half as expensive as a single AxiSEM forward run. Thus, this enables the computation of full databases at half the cost of the computation of seismograms for a single source in the previous scheme and allows to compute databases at the highest frequencies globally observed. By storing the basis coefficients of the numerical scheme (Lagrange polynomials), the Green's functions are 4th order accurate in space and the spatial discretization respects discontinuities in the velocity model exactly. High-order temporal interpolation using Lanczos resampling allows to retrieve seismograms at any sampling rate. AxiSEM is easily adaptable to arbitrary spherically symmetric models of Earth as well as other planets. In this paper, we present the basic rationale and details of the method as well as benchmarks and illustrate a variety of applications. The code is open source and available with extensive documentation at www.instaseis.net .

Machine learning for data-driven discovery in solid Earth geoscience

Abstract: BACKGROUND The solid Earth, oceans, and atmosphere together form a complex interacting geosystem. Processes relevant to understanding Earth’s geosystem behavior range in spatial scale from the atomic to the planetary, and in temporal scale from milliseconds to billions of years. Physical, chemical, and biological processes interact and have substantial influence on this complex geosystem, and humans interact with it in ways that are increasingly consequential to the future of both the natural world and civilization as the finiteness of Earth becomes increasingly apparent and limits on available energy, mineral resources, and fresh water increasingly affect the human condition. Earth is subject to a variety of geohazards that are poorly understood, yet increasingly impactful as our exposure grows through increasing urbanization, particularly in hazard-prone areas. We have a fundamental need to develop the best possible predictive understanding of how the geosystem works, and that understanding must be informed by both the present and the deep past. This understanding will come through the analysis of increasingly large geo-datasets and from computationally intensive simulations, often connected through inverse problems. Geoscientists are faced with the challenge of extracting as much useful information as possible and gaining new insights from these data, simulations, and the interplay between the two. Techniques from the rapidly evolving field of machine learning (ML) will play a key role in this effort. ADVANCES The confluence of ultrafast computers with large memory, rapid progress in ML algorithms, and the ready availability of large datasets place geoscience at the threshold of dramatic progress. We anticipate that this progress will come from the application of ML across three categories of research effort: (i) automation to perform a complex prediction task that cannot easily be described by a set of explicit commands; (ii) modeling and inverse problems to create a representation that approximates numerical simulations or captures relationships; and (iii) discovery to reveal new and often unanticipated patterns, structures, or relationships. Examples of automation include geologic mapping using remote-sensing data, characterizing the topology of fracture systems to model subsurface transport, and classifying volcanic ash particles to infer eruptive mechanism. Examples of modeling include approximating the viscoelastic response for complex rheology, determining wave speed models directly from tomographic data, and classifying diverse seismic events. Examples of discovery include predicting laboratory slip events using observations of acoustic emissions, detecting weak earthquake signals using similarity search, and determining the connectivity of subsurface reservoirs using groundwater tracer observations. OUTLOOK The use of ML in solid Earth geosciences is growing rapidly, but is still in its early stages and making uneven progress. Much remains to be done with existing datasets from long-standing data sources, which in many cases are largely unexplored. Newer, unconventional data sources such as light detection and ranging (LiDAR), fiber-optic sensing, and crowd-sourced measurements may demand new approaches through both the volume and the character of information that they present. Practical steps could accelerate and broaden the use of ML in the geosciences. Wider adoption of open-science principles such as open source code, open data, and open access will better position the solid Earth community to take advantage of rapid developments in ML and artificial intelligence. Benchmark datasets and challenge problems have played an important role in driving progress in artificial intelligence research by enabling rigorous performance comparison and could play a similar role in the geosciences. Testing on high-quality datasets produces better models, and benchmark datasets make these data widely available to the research community. They also help recruit expertise from allied disciplines. Close collaboration between geoscientists and ML researchers will aid in making quick progress in ML geoscience applications. Extracting maximum value from geoscientific data will require new approaches for combining data-driven methods, physical modeling, and algorithms capable of learning with limited, weak, or biased labels. Funding opportunities that target the intersection of these disciplines, as well as a greater component of data science and ML education in the geosciences, could help bring this effort to fruition. The list of author affiliations is available in the full article online.

ObsPy: a bridge for seismology into the scientific Python ecosystem

Abstract: The Python libraries NumPy and SciPy are extremely powerful tools for numerical processing and analysis well suited to a large variety of applications We developed ObsPy (http://obspyorg), a Python library for seismology intended to facilitate the development of seismological software packages and workflows, to utilize these abilities and provide a bridge for seismology into the larger scientific Python ecosystem Scientists in many domains who wish to convert their existing tools and applications to take advantage of a platform like the one Python provides are confronted with several hurdles such as special file formats, unknown terminology, and no suitable replacement for a non-trivial piece of software We present an approach to implement a domain-specific time series library on top of the scientific NumPy stack In so doing, we show a realization of an abstract internal representation of time series data permitting I/O support for a diverse collection of file formats Then we detail the integration and repurposing of well established legacy codes, enabling them to be used in modern workflows composed in Python Finally we present a case study on how to integrate research code into ObsPy, opening it to the broader community While the implementations presented in this work are specific to seismology, many of the described concepts and abstractions are directly applicable to other sciences, especially to those with an emphasis on time series analysis

PhaseNet: a deep-neural-network-based seismic arrival-time picking method

Abstract: As the number of seismic sensors grows, it is becoming increasingly difficult for analysts to pick seismic phases manually and comprehensively, yet such efforts are fundamental to earthquake monitoring. Despite years of improvements in automatic phase picking, it is difficult to match the performance of experienced analysts. A more subtle issue is that different seismic analysts may pick phases differently, which can introduce bias into earthquake locations. We present a deep-neural-network-based arrival-time picking method called "PhaseNet" that picks the arrival times of both P and S waves. Deep neural networks have recently made rapid progress in feature learning, and with sufficient training, have achieved super-human performance in many applications. PhaseNet uses three-component seismic waveforms as input and generates probability distributions of P arrivals, S arrivals, and noise as output. We engineer PhaseNet such that peaks in probability provide accurate arrival times for both P and S waves, and have the potential to increase the number of S-wave observations dramatically over what is currently available. This will enable both improved locations and improved shear wave velocity models. PhaseNet is trained on the prodigious available data set provided by analyst-labeled P and S arrival times from the Northern California Earthquake Data Center. The dataset we use contains more than seven million waveform samples extracted from over thirty years of earthquake recordings. We demonstrate that PhaseNet achieves much higher picking accuracy and recall rate than existing methods.

Machine Learning in Seismology: Turning Data into Insights

Abstract: This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground‐motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data‐driven ML with traditional physical modeling.

Fiber‐Optic Network Observations of Earthquake Wavefields

Abstract: Author(s): Lindsey, NJ; Martin, ER; Dreger, DS; Freifeld, B; Cole, S; James, SR; Biondi, BL; Ajo-Franklin, JB | Abstract: Our understanding of subsurface processes suffers from a profound observation bias: seismometers are sparse and clustered on continents. A new seismic recording approach, distributed acoustic sensing (DAS), transforms telecommunication fiber-optic cables into sensor arrays enabling meter-scale recording over tens of kilometers of linear fiber length. We analyze cataloged earthquake observations from three DAS arrays with different horizontal geometries to demonstrate some possibilities using this technology. In Fairbanks, Alaska, we find that stacking ground motion records along 20nm of fiber yield a waveform that shows a high degree of correlation in amplitude and phase with a colocated inertial seismometer record at 0.8–1.6nHz. Using an L-shaped DAS array in Northern California, we record the nearly vertically incident arrival of an earthquake from The Geysers Geothermal Field and estimate its backazimuth and slowness via beamforming for different phases of the seismic wavefield. Lastly, we install a fiber in existing telecommunications conduits below Stanford University and show that little cable-to-soil coupling is required for teleseismic P and S phase arrival detection.