The application of machine learning techniques in seismology has greatly advanced seismological analysis, especially for earthquake detection and seismic phase picking. However, machine learning approaches still face challenges in generalizing to datasets that differ from their original setting. Previous studies focused on retraining or transfer-training models for these scenarios, though restricted by the availability of high-quality labeled datasets. This paper demonstrates a new approach for augmenting already trained models without the need for additional training data. We propose four strategies - rescaling, model aggregation, shifting, and filtering - to enhance the performance of pre-trained models on out-of-distribution datasets. We further devise various methodologies to ensemble the individual predictions from these strategies to obtain a final unified prediction result featuring prediction robustness and detection sensitivity. We develop an open-source Python module quakephase that implements these methods and can flexibly process input continuous seismic data of any sampling rate. With quakephase and pre-trained ML models from SeisBench, we perform systematic benchmark tests on data recorded by different types of instruments, ranging from acoustic emission sensors to distributed acoustic sensing, and collected at different scales, spanning from laboratory acoustic emission events to major tectonic earthquakes. Our tests highlight that rescaling is essential for dealing with small-magnitude seismic events recorded at high sampling rates as well as larger magnitude events having long coda and remote events with long wave trains. Our results demonstrate that the proposed methods are effective in augmenting pre-trained models for out-of-distribution datasets, especially in scenarios with limited labeled data for transfer learning.

The application of absolutely calibrated piezoelectric (PZT) sensors is increasingly used to help interpret the information carried by radiated elastic waves of laboratory/in situs acoustic emissions (AEs) in nondestructive evaluation. In this paper, we present the methodology based on the finite element method (FEM) to characterize PZT sensors. The FEM-based modelling tool is used to numerically compute the true Green’s function between a ball impact source and an array of PZT sensors to map active source to theoretical ground motion. Physical-based boundary conditions are adopted to better constrain the problem of body wave propagation, reflection and transmission in/on the elastic medium. The modelling methodology is first validated against the reference approach (generalized ray theory) and is then extended down to 1 kHz where body wave reflection and transmission along different types of boundaries are explored. We find the Green’s functions calculated using physical-based boundaries have distinct differences between commonly employed idealized boundary conditions, especially around the anti-resonant and resonant frequencies. Unlike traditional methods that use singular ball drops, we find that each ball drop is only partially reliable over specific frequency bands. We demonstrate, by adding spectral constraints, that the individual instrumental responses are accurately cropped and linked together over 1 kHz to 1 MHz after which they overlap with little amplitude shift. This study finds that ball impacts with a broad range of diameters as well as the corresponding valid frequency bandwidth, are necessary to characterize broadband PZT sensors from 1 kHz to 1 MHz.

The water adsorption into pore spaces in brittle rocks affects wave velocity and transmitted amplitude of elastic waves. Experimental and theoretical studies have been performed to characterize moisture-induced elastodynamic variations due to macroporous effects; however, little attention has been paid to the manner in which wetting of nanopores affect elastic wave transmission. In this work, we extend our understanding of moisture-induced elastic changes in a microcracked nanopore-dominated medium (80 \% of the surface area exhibits pore diameters below 10 nm). We studied acousto-mechanical response resulting from a gradual wetting on a freestanding intact Herrnholz granite specimen over 98 hours using time-lapse ultrasonic and digital imaging techniques. Linkages between ultrasonic attributes and adsorption-induced stress/strain are established during the approach of wetting front. We found that Gassmann theory, previously validated in channel-like nanoporous media, breaks down in predicting P-wave velocity increase of microcracked nanopore-dominated media. However, squirt flow – a theory recognized to characterize wave velocity increase and attenuation in microcracked macropore-dominated media at pore scale – also accounts for the observed increase of P-wave velocity in microcracked nanopore-dominated media. The transmitted amplitude change in direct P waves are explained and predicted by the elastic wave propagation within P-wave first Fresnel zone and reflection/refraction on the wetting front.

We develop a rate- and state-dependent friction (RSF) model to investigate a compendium of recent experiments performed in the laboratory. In the documented experiments, a fault was sheared until macroscopic stick-slip frictional failure. Before macro-failure, small precursor seismicity nucleated from regions that also experienced aseismic slow slip. This behavior requires heterogeneity and is defined in our model as local variation in frictional parameters inferred from the roughness. During sliding wear introduced a smooth-polished surface onto a previously rough surface and was quantified using a bimodal Gaussian distribution of surface heights. We used spatial distribution of the smooth and rough sections to impose binary partitioning in critical slip distance $D_{c}$ to a planar frictional model. Simulations revealed that local seismicity nucleated on the “smooth’ sections, while the larger “rough’ section hosted aseismic slip. As the level of heterogeneity between smooth and rough sections increased, the model transitioned from a predominantly stick-slip to creeping. The simulations produced a dominant asperity, which appeared to control aspects of rupture nucleation: ($i$) weak heterogeneity caused the dominant asperity to generate foreshocks but also “ignite’ cascade-up fault-wide event, while ($ii$) strong heterogeneity led to constrained repeaters. Seismic source properties: average slip $\delta$, seismic moment $M_{0}$, stress drop $\Delta \tau$ and fracture energy $G^{’}$, were determined for each event and agreed with separate kinematic estimates made independently from seismic measurements. Our numerical calculations provide insight into rate-dependent cascade-up nucleation theory where frictional heterogeneity here was associated with wear of solid frictional contacts in the laboratory.

We investigate experimental results from a direct shear friction apparatus, where a fault was formed by pressing mature, worn surfaces of two polymethyl methacrylate (PMMA) samples on top of each other in a dry environment. The fault was sheared until macroscopic stick-slip frictional failure occurred. Before the macro-failure small precursory seismicity nucleated from regions that also experienced aseismic slow slip. These precursory events did not cascade-up into gross fault rupture and arrested locally. Reasons as to why ruptures arrested are investigated using a 1-D rate and state friction (RSF) model. Surface profilometry of the fault surface taken \textit{a posteriori} revealed wear in the form of a bimodal Gaussian distribution of surface height. In our model, this unique distribution of surface roughness is determined to be a proxy for the heterogeneous spatial description of the critical slip distance $D_{c}$. We assume that smooth (polished) sections of fault exhibited lower $D_{c}$ than rougher sections of the bimodal Gaussian roughness profile. We used a quasi-dynamic RSF model that determined localized seismicity initiated at the smooth sections. Source properties: average slip $\delta$, seismic moment $M_{0}$, stress drop $\Delta \tau$ and fracture energy $G^{’}$, were determined for each event. We compare the numerically modeled source properties to experimental source characteristics inferred from seismological estimates using an array of acoustic emission sensors from a concerted study. We discuss similarities, discrepancies and assumptions between these two independent models (kinematic and dynamic) used to study earthquakes for the first time in the laboratory.