Sequencing Geophysical Signals to Glean Structural Insights

Abstract:

Gleaning insights into earth structures and processes from geophysical datasets requires approaches flexible enough to detect robust patterns with little-to-no user supervision within and across different types of measurements. Often, observed phenomena are driven by a leading effect or parameter. In such a case, there should exist an order in which the data should be optimally viewed. This order reflects a one-dimensional manifold representing the underlying trend, even in the presence of complex behavior. Here, we apply a graph-based manifold learning algorithm called the Sequencer to reveal this leading trend from complex data, which is especially useful in the absence of theoretical guidance. We use the Sequencer to identify trends and anomalous signals across a diverse set of geophysical data. Specifically, we analyze receiver functions, surface wave dispersion, and seismic waveforms to constrain structures in the crust and deep mantle as well as geodetic timeseries to map out surface processes. We discuss the utility of sequencing for synthesizing geophysical information in maps and for parameterizing geophysical inversions. We compare sequencing to more commonly-used approaches such as tSNE and k-means clustering.