The use of InSAR and Machine Learning to Understand Crustal Displacement from Low Magnitude Earthquakes

Low magnitude seismicitylike the M3.7 event in Gallina, New Mexico (NM) that occurred July 30, 2020is considered to be of low hazard and is thus poorly studied. Yet, such events still hold great importance for the potential to represent precursors to larger seismic events. Therefore, their study can lead to a better understanding of local fault geometries and identification of probable areas of crustal displacement from future earthquakes. We applied standard interferometric synthetic aperture radar (InSAR) time series processing techniques combined with a Machine Learning (ML) method to detect the magnitude and extent of crustal deformation from the Gallina, NM earthquake which lies in the vicinity of the Nacimiento-Gallina arch, adjacent to the earthquake epicenter. Deformation was not detectable from cross sections across both the fault and earthquake epicenter using standard InSAR processing techniques. However, application of the ML method revealed submillimeter-level displacement, which represents the lowest magnitude earthquake from which crustal displacement has been identified using InSAR. Development of this new technique will enable investigations to further test the lower limits of InSAR analysis and make possible additional studies of low magnitude earthquakes (M3 and M4) to understand the potential for seismic hazards from larger events should they occur.