We present a new 3-D seismic structural model of the eastern Indonesian region and its surroundings from full-waveform inversion (FWI) that exploits seismic data filtered at periods between 15 - 150 s. SASSY21 - a recent 3-D FWI tomographic model of Southeast Asia - is used as a starting model, and our study region is characterized by particularly good data coverage, which facilitates a more refined image. We use the spectral-element solver Salvus to determine the full 3-D wavefield, accounting for the fluid ocean explicitly by solving a coupled system of acoustic and elastic wave equations. This is computationally more expensive but allows seismic waves within the water layer to be simulated, which becomes important for periods ≤ 20 s. We investigate path-dependent effects of surface elevation (topography and bathymetry) and the fluid ocean on synthetic waveforms, and compare our final model to the tomographic result obtained with the frequently used ocean loading approximation. Furthermore, we highlight some of the key features of our final model - SASSIER22 - after 34 L-BFGS iterations, which reveals detailed anomalies down to the mantle transition zone, including a convergent double-subduction zone along the southern segment of the Philippine Trench, which was not evident in the starting model. A more detailed illumination of the slab beneath the North Sulawesi Trench reveals a pronounced positive wavespeed anomaly down to 200 km depth, consistent with the maximum depth of seismicity, and a more diffuse but aseismic positive wavespeed anomaly that continues to the 410 km discontinuity.

Detecting and locating microearthquakes from continuous waveform records is the fundamental step in microseismic processing. Dense local networks and arrays have introduced the possibility to detect large numbers of far weaker events, but when viewed on seismic records from individual stations their waveforms are often obscured by noise. Furthermore, areas of interest for microseismic monitoring often feature extremely high event rates, highlighting the limitations of traditional techniques based on phase picking and association. In order to maximise the new insights gained, we require fully automated techniques which can exploit modern recordings to produce highly complete earthquake catalogues containing few artefacts. QuakeMigrate is a new modular, open-source python package providing a framework to efficiently, automatically and robustly detect and locate microseismicity. The user inputs continuous seismic data, a velocity model or pre-calculated look-up table and list of station locations. Instead of reducing the raw waveforms to discrete time picks, they are transformed (by amplitude, frequency and/or polarisation analysis) to continuous functions representing the probability of a particular phase arrival through time. These ‘onset functions’ from stations across the network are then migrated according to a travel-time look-up table and stacked to perform a grid-search for coherent sources of energy in the subsurface. This enables detection of earthquakes at close to or below the signal-to-noise ratio at individual stations, and implicitly associates phase arrivals even at very small inter-event times. We demonstrate the flexibility and power of this approach with examples of basal icequakes detected at the Rutford Ice Stream, Antarctica, dike- and caldera-collapse induced seismicity at Bárðarbunga central volcano, Iceland, and the aftershock sequence from a M5 earthquake at Mt. Kinabalu, North Borneo. The modular nature of the workflow and wide range of automatic plotting options makes parameter choice straightforward, and robust event location uncertainty statistics facilitate filtering to produce a robust catalogue. QuakeMigrate also outputs phase picks and local magnitude estimates, with an architecture designed to promote further community-driven extension in future.