Bio


I am a geophysicist with an engineering background. My work includes:(1) statistical seismology (seismicity analyses, seismic hazard, and risk analyses), (2) signal processing (time-frequency analyses and denoising), (3) machine learning/deep learning (seismic signal processing and discrimination), (4) observational seismology (microseismic monitoring and induced seismicity).

Academic Appointments


All Publications


  • Separating Signal from Noise and from Other Signal Using Nonlinear Thresholding and Scale-Time Windowing of Continuous Wavelet Transforms BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA Langston, C. A., Mousavi, S. 2019; 109 (5): 1691–1700

    View details for DOI 10.1785/0120190073

    View details for Web of Science ID 000487813300009

  • CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection. Scientific reports Mousavi, S. M., Zhu, W., Sheng, Y., Beroza, G. C. 2019; 9 (1): 10267

    Abstract

    Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. CRED uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on individual stations. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. We also apply the model to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 800 microearthquakes as small as -1.3ML induced during hydraulic fracturing far away than the training region. We compare the performance of the model with the STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise for lowering the detection threshold while minimizing false positive detection rates.

    View details for DOI 10.1038/s41598-019-45748-1

    View details for PubMedID 31311942

  • Lateral Variation of Crustal Lg Attenuation in Eastern North America. Scientific reports Zhao, L. F., Mousavi, S. M. 2018; 8 (1): 7285

    Abstract

    We perform Q Lg tomography for the northeastern part of North America. Vertical broadband seismograms of 473 crustal earthquakes recorded by 302 stations are processed to extract the Lg amplitude spectra. Tomographic inversions are independently conducted at 58 discrete frequencies distributed evenly in log space between 0.1 and 20.0 Hz. This relatively large dataset with good ray coverage allows us to image lateral variation of the crustal attenuation over the region. Obtained Q Lg maps at broadband and individual frequencies provide new insights into the crustal attenuation of the region and its relationship to geological structures and past tectonic activity in the area. The Q Lg shows more uniform values over the older, colder, and drier Canadian Shield, in contrast to higher variations in the younger margins. Results confirm the correlation of large-scale variations with crustal geological features in the area. Existence of low-velocity anomalies, thick sediments, volcanic rocks, and thin oceanic crust are potential sources of observed anomalies. The mean Q values are inversely correlated with average heat flow/generation for main geological provinces.

    View details for DOI 10.1038/s41598-018-25649-5

    View details for PubMedID 29740108

    View details for PubMedCentralID PMC5940689