Numerous studies have reported that surface hydrological loading can seasonally modulate seismicity rates at crustal depths. For example, substantial winter snow accumulation occurs across the Japanese Islands, and these snowy regions appear to have seasonally modulated the occurrence of previous large inland earthquakes. Therefore, it is important to investigate the impact of seasonal stress changes on crustal seismicity to deepen our understanding of earthquake generation. Here we constrain seasonal changes in the surface load across northeastern Japan using Global Navigation Satellite System surface displacements and evaluate the potential relationship between temporal trends in inland seismicity and estimated seasonal stress changes. The spatial distribution of the seasonal surface load is consistent with snow depth along the Sea of Japan. The inland seismicity beneath northeastern Japan is modestly modulated by the seasonal stress changes that are induced by the annual snow load. However, this seasonal response is weaker than that in other regions. This weak modulation may be due to the small surface-load-induced stress perturbation relative to the long-term-averaged stressing rate and/or the limited presence of crustal fluids to trigger seismicity in Japan.

Abrupt frictional fault failure is normally accompanied by acoustic emission (AE)—impulsive elastic wave broadcast—with amplitude proportional to particle velocity. The cumulative sum of the fault particle velocities is a slip displacement. In laboratory shear experiments described here, measurements of a sequence of laboratory earthquakes includes local measurement of fault displacement and AE. Using these measurements we illuminate the connections between “cumulative sum of AE” and “slip displacement“. Additionally, the composition of the AE broadcasts reveals inhomogeneity in the fault mechanical structure from which they arise. This inhomogeneity, leading to a time invariant white AE component and an articulated AE, indicates that the articulated cumulative sum of the AE reveals a fault “state of the mechanical structure” diagnostic, that follows a distinctive pattern to frictional failure. This pattern explains why the continuous AE map to fault displacement as well as fault friction, shear stress, etc., as shown in many recent studies.

Machine learning models using seismic emissions can predict instantaneous fault characteristics such as displacement in laboratory experiments and slow slip in Earth. Here, we address whether the acoustic emission (AE) from laboratory experiments contains information about near-future frictional behavior. The approach uses a convolutional encoder-decoder containing a transformer layer. We use as input progressively larger AE input time windows and progressively larger output friction time windows. The attention map from the transformer is used to interpret which regions of the AE contain hidden information corresponding to future frictional behavior. We find that very near-term predictive information is indeed contained in the AE signal, but farther into the future the predictions are progressively worse. Notably, information for predicting near future frictional failure and recovery are found to be contained in the AE signal. This first effort predicting future fault frictional behavior with machine learning will guide efforts for applications in Earth.

Low frequency earthquakes (LFEs) originating below the central San Andreas Fault are associated with slow-slip within the more ductile portion of the crust beneath the seismogenic zone. Monitoring efforts over 15 years recorded >1 million LFEs with >70 per day. We apply machine learning (ML) to statistical features describing the seismic waveforms and estimate the LFE daily intensity. Using 4 years of independent data, the ML model produces a 0.68 correlation. The burst-like LFE behavior is reproduced and the largest misfit occurs during the low-amplitude daily undulations. The ability to continuously monitor LFE activity provides insight to when geodetic measurements of slow slip are possible, without the need for developing a computational-intensive template-matching catalog. Similarities are found between detecting LFEs and tremors, which provides evidence tremors are composed of LFEs. The approach reveals by ML the rich information contained in the features of continuous seismic waveforms.

Earthquake detection is critical for tracking fracture networks and fault zone deformation, particularly microseismicity that produces weak ground motions. We develop deep learning models to detect seismic phase arrivals and first motion polarities. The detection model is a convolutional encoder-decoder with a multi-head attention latent space that assigns a softmax value to each data point in continuous seismic records for classifying earthquake waveforms and the phase arrivals. The multi-output classification model utilizes weighted categorical cross entropy for the different softmax predictions to account for the unbalanced number of signal points compared to noise. The model training uses a benchmark data set of global seismic waveforms and the events are augmented using various techniques to reduce the signal-to-noise ratio, simulate multiple events arrivals, and channel failures. Detected p-waves are passed through a second model to obtain the first motion polarity. The phase arrivals, first motions, arrival waveforms, and additional metrics needed for catalog development are saved in a detection table. A neural network phase associator is used with the detection table to build an event arrival table. Locations are calculated and double difference locations are produced using correlation metrics from the waveforms retained in the detection table. The analysis is wrapped in a multiprocessing workflow to efficiently analyze large data sets. As a case study the workflow is applied to southern Kansas, a region with increased seismic activity related to hydrocarbon-production and waste water injection. The deep learning seismicity and focal mechanism catalogs show immensely more seismic activity than standard processing.