Microseismic events are very small earthquakes (typically −3 to 0 Mw) that correspond to brittle failure mainly attributed to the reduction in effective stress. Microearthquakes illuminate earth processes with high resolution. Moreover, they have industrial applications and are commonly used to monitor a fluid front and estimate parameters such as fluid pressure, fracture length, and spacing in hydraulic fracturing. My works mainly focus on the development of techniques that facilitate the detection and characterization of smaller events - that occur much more frequently but often have weaker and noisier signals - using typical seismic data recorded on the surface.
Grigoli, F., Ellsworth, W.L, Zhang, M., Mousavi, S. M., Cesca, S., Satriano, C., Beroza, G.C., and Wiemer, S., (2020). Relative earthquake location procedure for clustered seismicity with a single station, Geophysical Journal International.
Earthquake location is one of the oldest problems in seismology, yet remains an active research topic. With dense seismic monitoring networks, it is possible to obtain reliable locations for microearthquakes; however, in many cases, dense networks are lacking, limiting the location accuracy, or preventing location when there are too few observations. For small events in all settings, recording may be sparse and location may be difficult due to the low signal-to-noise ratio. In this work, we introduce a new, distance-geometry-based method to locate seismicity clusters using only one or two seismic stations. A distance geometry problem consists in determining the location of sets of points based only on the distances between member pairs. Applied to seismology, our approach allows earthquake location using the interevent distance between earthquake pairs, which can be estimated using only one or two seismic stations. We first validate the method with synthetic data that resemble common cluster shapes and then test the method with two seismic sequences in California: the August 2014 Mw 6.0 Napa earthquake and the July 2019 Mw 6.4 Ridgecrest earthquake sequence. We demonstrate that our approach provides robust and reliable results even for a single station. When using two seismic stations, the results capture the same structures recovered with high-resolution double-difference locations based on multiple stations. The proposed method is particularly useful for poorly monitored areas, where only a limited number of stations are available.
Mohammadigheymasi, H., Tavakolizadeh, N., Matias, L., Mousavi, S. M., Silveira, G., Custódio, S., and Moradichaloshtori, Y. (2022). Application of deep learning for seismicity analysis in Ghana, Geosystems and Geoenvironment.
We present the characterization of regional seismicity in Ghana by processing the Ghana Digital Seismic Network (GHDSN) data set recorded between September 2012 and April 2014, implementing deep learning (DL). Local earthquakes are detected in this dataset using EQTransformer, a DL model with a hierarchical attentive mechanism (HAM) for simultaneous earthquake detection and P- and S-phase picking. A Conservative Strategy (CS) is devised to detect the missing phases and to associate the detected phases to circumvent the false-negative issue of EQTrans-former processing low signal-to-noise ratio (SNR) seismograms. We performed a joint inversion by grid search in 1D velocity model space and simultaneous inversion for the hypocentral parameters, incorporating 559 detected arrival times (292 P and 267 S phases). The results obtained by velocity inversion contain thicknesses of 1, 13, 8, 13, and 10 km, from the surface to a depth of 45 km, with Vp = 5.9, 6.1, 6.3, 6.5, 6.9, and 7.2 km/s, respectively. The updated velocities for the first and last layers are 6% and 26% and the Vp/Vs (=1.70) is 3.03% higher than the previously reported values. A total number of 73 earthquakes with a local magnitude of 2.5 < Ml < 3.9 are located, comprising four main clusters of events, showing a high correlation with the mapped fault zones. The hypocentral depth distribution is mainly in the range of 7-15 km, confined to the upper crust in the region. No specific seismic activity in the eastern branch of the Coastal Boundary Fault (CBF) and the continuation of the Romanche Fracture Zone (RFZ) in the study period was observed, casting further doubt on the activity of this branch and the hypothesis of stress transfer from RFZ to southern Ghana. The results reinforce the intraplate nature of the tectonic activities in the region. Finally, an updated seismic catalog up to April 2022 is presented for Ghana by incorporating all reported catalogs and combining the newly detected events.
Park, Y., Mousavi, S. M., Zhu, W., Ellsworth, W.L., and Beroza, G.C., (2020). Machine learning based analysis of the Guy-Greenbrier, Arkansas earthquakes: a tale of two sequences, Geophysical Research Letters.
Finding small earthquake signals from long duration continuous seismic data is a time-consuming task, but machine learning algorithms have the potential to accelerate the workflow and improve the results. We reprocessed the seismic data from the area spanning Guy and Greenbrier in central Arkansas in 2010 and 2011 using a machine learning algorithm to reexamine this well-studied earthquake sequence, which is thought to be caused by injection of wastewater from unconventional hydrocarbon production into deep disposal wells. Even using conservative postprocessing steps, we were able to locate nearly 90,000 earthquake events. The improved catalog illuminates previously unseen aspects of this earthquake sequence that give new insights into its behavior.
Zhu, W., Hou, AB., Yang, R., Mousavi, S. M., Ellsworth, W., and Beroza, G. C., (2022). QuakeFlow: A Scalable Machine-learning-based Earthquake Monitoring Workflow with Cloud Computing, Geophysical Journal International.
Earthquake monitoring workflows are designed to detect earthquake signals and to determine source characteristics from continuous waveform data. Recent developments in deep learning seismology have been used to improve tasks within earthquake monitoring workflows that allow the fast and accurate detection of up to orders of magnitude more small events than are present in conventional catalogs. To facilitate the application of machine-learning algorithms to large-volume seismic records at scale, we developed a cloud-based earthquake monitoring workflow, QuakeFlow, which applies multiple processing steps to generate earthquake catalogs from raw seismic data. QuakeFlow uses a deep learning model, PhaseNet, for picking P/S phases and a machine learning model, GaMMA, for phase association with approximate earthquake location and magnitude. Each component in QuakeFlow is containerized, allowing straightforward updates to the pipeline with new deep learning/machine learning models, as well as the ability to add new components, such as earthquake relocation algorithms. We built QuakeFlow in Kubernetes to make it auto-scale for large data sets and to make it easy to deploy on cloud platforms, which enables large-scale parallel processing. We used QuakeFlow to process three years of continuously archived data from Puerto Rico within a few hours and found more than a factor of ten more events that occurred on much the same structures as previously known seismicity. We applied Quakeflow to monitoring earthquakes in Hawaii and found over an order of magnitude more events than are in the standard catalog, including many events that illuminate the deep structure of the magmatic system. We also added Kafka and Spark streaming to deliver real-time earthquake monitoring results. QuakeFlow is an effective and efficient approach both for improving real-time earthquake monitoring and for mining archived seismic data sets.
Mousavi, S. M., Ellsworth, W.L., Zhu, W., and Beroza, G.C., (2020). Earthquake Transformer: An Attentive Deep-learning Model for Simultaneous Earthquake Detection and Phase Picking, Nature Communication.
Earthquake signal detection and seismic phase picking are challenging tasks in the processing of noisy data and the monitoring of microearthquakes. Here we present a global deep-learning model for simultaneous earthquake detection and phase picking. Performing the two related tasks in tandem improves model performance in each individual task by combining information in phases and in the full waveform of earthquake signals by using an ahierarchical attention mechanism. We show that our model outperforms previous deep-learning and traditional phase-picking and detection algorithms. Applying our model to5 weeks of continuous data recorded during the 2000 Tottori earthquakes in Japan, we were able to detect and locate two times more earthquakes using only a portion (less than 1/3) of seismic stations. Our model picks P and S phases with precision close to manual picks by human analysts; however, its high efficiency and higher sensitivity can result in detecting and characterizing more and smaller events.