On May 4th, 2022 the InSight seismometer SEIS recorded the largest marsquake ever observed, S1222a, with an initial magnitude estimate of Mw 4.7. Understanding the depth and source properties of this event has important implications for the nature of tectonic activity on Mars. Located ~37 degrees to the southeast of InSight, S1222a is one of the few non-impact marsquakes that exhibits prominent ratio surface waves. We use waveform modeling of body waves (P and S) and surface waves (Rayleigh and Love) to constrain the moment tensor and quantify the associated uncertainty. We find that S1222a likely resulted from dip-slip faulting in the mid-crust (source depth ~18 – 28 km) and estimate a scalar moment of 3.51015 – 5.01015 Nm (magnitude Mw 4.3 – 4.4). The best-fitting focal mechanism is sensitive to the choice of phase windows and misfit weights, as well as the structural model of Mars used to calculate Green’s functions. We find that an E-W to SE-NW striking thrust fault can explain the data well, although depending on the choice of misfit weighting, a normal fault solution is also permissible. The orientation of the best-fitting fault plane solutions suggests that S1222a takes place on a fault system near the martian crustal dichotomy accommodating relative motion between the northern lowlands and southern highlands. Independent constraints on the event depth and improved models of the (an)isotropic velocity structure of the martian crust and mantle could help resolve the ambiguity inherent to single-station moment tensor inversions of S1222a and other marsquakes.

Receiver functions, an important tool in understanding sub-surface interfaces, can be analysed through carefully implemented neural networks. We demonstrate this approach. Previously, we introduced our receiver function tool set, Pythonic Global Lithospheric Imaging using Earthquake Recordings (PyGLImER). PyGLImER enables us to: [1] create a database of teleseismic event displacement records at worldwide seismic stations, [2] compute receiver functions from these records, and [3] compute volumetric common conversion point (CCP) stacks from the receiver functions and their conversion points. CCP stacking is a standard tool to image the subsurface using receiver functions. The CCP stacks represent rich but large, three-dimensional volumes of data that contain information about discontinuities in Earth’s crust and upper mantle. One goal of the interpretation of CCPs is the identification of such discontinuities. Automated picking routines reduce discontinuities to singular peaks and troughs, thus discarding the wealth of information available over the whole depth range, such as integrated discontinuity impedance and regional geometry. However, the obvious alternative, manual picking, is not feasible for large data volumes. Here, we explore the possibility of fully-automated segmentation of 3D CCP volumes through the application of image processing routines and machine learning to successive volume cross-sections. With our picking tool, we manually label discontinuities in CCP slices to serve as training and validation sets.We use these labeled datasets as input to train a convolutional neural network (CNN) to perform pixel-wise identifications in subsurface images. When applied to all slices of the CCP stack, the CNN outputs a fully-segmented 3D model, which furnishes quantitative exploration of subsurface discontinuity morphology. Specifically, we can investigate the thickness/width, intensity, and topography of discontinuities across continents. This information has the potential to improve our understanding of, e.g., mantle transition zone phase transitions and, therefore, mantle dynamics.