LASIF: LArge-scale Seismic Inversion Framework, an updated version

Abstract

Recent methodological advances and increases in computational power have made it feasible to perform full-waveform inversions (FWI) of large domains while using more sources. This trend, along with the increasing availability of seismic data has led to an explosion of the data volumes that can, and should, be used within an inversion. Similar to machine learning problems, the incorporation of more data can result in more robust and higher quality models. In this contribution, we present the new version of LASIF, an open-source LArge-scale Seismic Inversion Framework, which helps to automate many of the historically labor-intensive tasks that were bottlenecks in earlier FWI workflows and prevented the use of the larger datasets. Among other things, the framework automates data selection, data acquisition from public web services, and data processing. It also defines an inversion project structure that organizes the data and documents the progress of the inversion. The code is open-source and available on Github. Features are available through a graphical user interface (GUI), a command-line interface (CLI), and an application programming interface (API). While we will show examples for use of LASIF with the Salvus wave equation solver, the API makes it possible to use the features of LASIF for any type of wave equation solver as long as the LASIF file formats are adhered to.

Full Waveform Inversion Beneath the Central Andes: Insight Into the Dehydration of the Nazca Slab and Delamination of the Back‐Arc Lithosphere

“…Model updates were carried out outside LASIF based on our own implementation of the CG and L‐BFGS algorithms (Figure 4). Furthermore, in order to lower the effects of the uneven coverage of seismic stations, we integrate the station weightings into the inversion, as implemented in LASIF (Krischer, Fichtner, et al., 2015; Thrastarson et al., 2021). The station weighting scheme takes fully account of the distances between neighboring stations and the number of the neighboring stations for every station.…”

Data-adaptive global full-waveform inversion

“…Most often, local optimization schemes are used in FWI, although recently global, probabilistic methods have been developed for comparatively small problems (e.g. Käufl et al 2013;Thurin et al 2019;Gebraad et al 2020;Zhang & Curtis 2020).…”

“…We stored the data in the Adaptable Seismic Data Format (ASDF, Krischer et al 2016), and preprocessed the data using ObsPy (Beyreuther et al 2010;Krischer et al 2015b). The downloading, processing and organization of data are encapsulated within the LArge-scale Seismic Inversion Framework (LASIF, Thrastarson et al 2021c), which creates a well-structured framework for the inversion, and computes misfits and adjoint sources. All forward and adjoint simulations, L-BFGS optimization, and smoothing were done using the spectral-element solver Salvus (Afanasiev et al 2019), run on the high-performance computing (HPC) cluster Piz Daint, operated by the Swiss National Supercomputing Center (CSCS).…”

“…All data is publically available and can be accessed through the FDSN web services (Romanowicz & Dziewonski 1986). We accessed FDSN via LASIF (Thrastarson et al 2021c) and ObsPy (Beyreuther et al 2010;Krischer et al 2015b).…”

Mitigating the effect of errors in source parameters on seismic (waveform) inversion

“…This strategy is also helpful to mitigate the effects of finite source rupture effects. As such, it is a procedure often followed in waveform tomography, which is for example included in the automated window picking algorithm of the LASIF waveform inversion management toolbox (Krischer et al 2015;Thrastarson et al 2021). It should be noted, however, that depending on the frequency content and event depth, the area over which this needs to happen can also get quite large, with the consequent exclusion of a large proportion of stations available for an event (Figure 8).…”