Summary Ambient noise cross-correlations can be used as self-consistent observables, opening novel possibilities for investigating ambient noise sources. To optimise the forward-modelling of global ambient noise cross-correlations for any given distribution of noise sources in the microseismic frequency range up to 0.2 Hz, we implement (i) pre-computed wavefields and (ii) spatially variable grids. This enables rapid inversions for microseismic noise sources based on finite-frequency source sensitivity kernels. We use this advancement to perform regional and global gradient-based iterative inversions of the logarithmic energy ratio in the causal and acausal branches of micro-seismic noise cross-correlations. Synthetic inversions show promising results, with good recovery of the main dominant noise sources of the target model. Data inversions for several consecutive days at the beginning of October 2019 demonstrate the capability of inverting for the spatio-temporal variations of the sources of secondary microseisms in the ocean. This paves the way for daily ambient noise source inversions which could help improve full-waveform ambient noise tomography and subsurface monitoring methods.

Interest in measuring displacement gradients, such as rotation and strain, is growing in many areas of geophysical research. This results in an urgent demand for reliable and field-deployable instruments measuring these quantities. In order to further establish a high-quality standard for rotation and strain measurements in seismology, we organized a comparative sensor test experiment that took place in November 2019 at the Geophysical Observatory of the Ludwig-Maximilians University Munich in Fürstenfeldbruck, Germany. More than 24 different sensors, including three-component and single-component broadband rotational seismometers, six-component strong-motion sensors and Rotaphone systems, as well as the large ring laser gyroscopes ROMY and a Distributed Acoustic Sensing system, were involved in addition to 14 classical broadband seismometers and a 160 channel, 4.5 Hz geophone chain. The experiment consisted of two parts: during the first part, the sensors were co-located in a huddle test recording self-noise and signals from small, nearby explosions. In a second part, the sensors were distributed into the field in various array configurations recording seismic signals that were generated by small amounts of explosive and a Vibroseis truck. This paper presents details on the experimental setup and a first sensor performance comparison focusing on sensor self-noise, signal-to-noise ratios, and waveform similarities for the rotation rate sensors. Most of the sensors show a high level of coherency and waveform similarity within a narrow frequency range between 10 Hz and 20 Hz for recordings from a nearby explosion signal. Sensor as well as experiment design are critically accessed revealing the great need for reliable reference sensors.

Abstract. We introduce open-source tool noisi for the forward and inverse modeling of ambient seismic cross-correlations with spatially varying source spectra. It utilizes pre-computed databases of Green’s functions to represent seismic wave propagation between ambient seismic sources and seismic receivers, which can be obtained from existing repositories or imported from the output of wave propagation solvers. The tool was built with the aim of studying ambient seismic sources while accounting for realistic wave propagation effects. Furthermore, it may be used to guide the interpretation of ambient seismic auto- and cross-correlations, which have become pre-eminent seismological observables, in light of non-uniform ambient seismic sources. Written in the Python language, it is both accessible for usage and further development, as well as efficient enough to conduct ambient seismic source inversions for realistic scenarios. Here, we introduce the concept and implementation of the tool, compare its model output to the output of cross-correlations computed with SPECFEM3D_globe, and demonstrate its capabilities on selected use cases: A comparison of observed cross-correlations of the Earth’s hum to a forward model based on hum sources from oceanographic models, and a synthetic noise source inversion using full waveforms and signal energy asymmetry.

Abstract. We introduce the open-source tool noisi for the forward and inverse modeling of ambient seismic cross-correlations with spatially varying source spectra. It utilizes pre-computed databases of Green's functions to represent seismic wave propagation between ambient seismic sources and seismic receivers, which can be obtained from existing repositories or imported from the output of wave propagation solvers. The tool was built with the aim of studying ambient seismic sources while accounting for realistic wave propagation effects. Furthermore, it may be used to guide the interpretation of ambient seismic auto- and cross-correlations, which have become preeminent seismological observables, in light of nonuniform ambient seismic sources. Written in the Python language, it is accessible for both usage and further development and efficient enough to conduct ambient seismic source inversions for realistic scenarios. Here, we introduce the concept and implementation of the tool, compare its model output to cross-correlations computed with SPECFEM3D_globe, and demonstrate its capabilities on selected use cases: a comparison of observed cross-correlations of the Earth's hum to a forward model based on hum sources from oceanographic models and a synthetic noise source inversion using full waveforms and signal energy asymmetry.

Seismic ambient noise sources have been studied thoroughly over the last few decades with studies and theories about the noise source locations and mechanisms as early as the 19th century (