Data Science

Modern trends in geophysical and seismic data analysis involve plowing through very large amounts of data to extract maximum information on Earth structures and processes. Our group’s work has always been geared toward improving existing tools and developing new ones to maximize information extraction from large data sets. Additional examples of recent work include: 1) Using unconventional sources of seismic energy (i.e., anthropogenic noise) to obtain accurate and reliable measurements of spatio-temporal seismic velocity models in underground mines; 2) Harnessing modern statistical learning algorithms to characterize the full seismic wave field, classify earthquakes and subduction zone behavior, and perform improved automatic earthquake detections and locations; and 3) Implementing Bayesian algorithms to obtain reliable uncertainty estimate in geophysical inversions.

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Pascal Audet
Associate Professor of Geophysics & University Research Chair

Publications

Automatic detection and location of seismic events from time-delay projection mapping and neural network classification

The past several decades have seen an exponential increase in the volume of available seismic data, and with it has come the need to …

Uncovering the physical controls of deep subduction zone slow slip using supervised classification of subducting plate features

Deep slow slip events (SSEs) at subduction zones have significantly contributed to refining our understanding of the megathrust …

Curie depth estimation from magnetic anomaly data: a re-assessment using multitaper spectral analysis and Bayesian inference

The maximum depth of magnetization in the Earth’s crust is generally thought to coincide with the Curie temperature of magnetite (580 …

Recovery of P Waves from Ambient‐Noise Interferometry of Borehole Seismic Data around the San Andreas Fault in Central California

Studies on the recovery and processing of surface waves from ambient‐noise interferometry are now standard practice in seismology. It …

Seismic Interferometry Using Persistent Noise Sources for Temporal Subsurface Monitoring

In passive source seismology, seismic interferometry typically refers to the cross correlation of ambient noise to construct an …

Interferometric methods for spatio temporal seismic monitoring in underground mines

In active underground mining environments, monitoring of the rockmass has important implications for both safety and productivity. …

Supervised machine learning on a network scale: application to seismic event classification and detection

A new method using a machine learning technique is applied to event classification and detection at seismic networks. This method is …

Directional wavelet analysis on the sphere: Application to gravity and topography of the terrestrial planets

The spectral relations (admittance and correlation) between gravity and topography are often used to obtain information on the density …