Combining Seismology and Geodesy to Better Constrain Earthquake Source Parameters and Shallow Fault Behavior

Our current understanding of the Earth’s interior structure and processes is limited to observations made at the surface that are mapped to the subsurface using inverse methods. The complexity of geophysical inverse problems mainly arises from the existence of many free parameters that sometimes have traded off with each other. This can cause inaccuracies, low resolution and non-uniqueness problems in geophysical models. The main focus of my dissertation is on how we can use two independent geophysical data types – geodesy and seismology – to increase knowledge, resolution and accuracy of Earth’s structure, and of interseismic and coseismic processes in the earthquake cycle. For example, in my first project (Chapter 2) I search for repeating earthquakes (REs) using similarity search on recorded seismic waveforms from the northern San Francisco Bay Area. Evidence from the San Andreas fault and elsewhere indicates that REs are correlated with, and likely driven by, aseismic slip (creep) at depth. This is complementary knowledge to the geodetic observation of creep at the surface. The source information of REs can also be used to constrain the interseismic slip models inverted from geodetic data such as GPS and InSAR. By using a new fast similarity search algorithm, that I developed specifically for probing big seismic data sets (described in Chapter 3), we found 198 RE groups, including periodic and nonperiodic repeating earthquake 'families', and repeating event pairs. Our results can not only help us to map the depth and extent of creep on several major faults but also reveal previously unknown structural complexity – e.g. that subparallel strands of the Maacama fault are active and creep simultaneously. Source parameters and locations of these REs can be used to update seismic hazard models, by better constraining the creeping areas of faults in the region, and to improve community models of fault geometry.In a second major project (Chapter 4), I aim to reconcile earthquake source parameters and locations determined by long-period teleseismic source inversions with those obtained from InSAR data. The latter observes earthquakes in situ and thus, we presume, accurately locates them. Previous studies suggest that the discrepancies between these two catalogs arise from the existing inaccuracy in Earth models and are caused by the historic (and circular) problem that earthquake locations estimated using inaccurate velocity models are themselves inaccurate, and vice-versa.In several case studies of various locations (e.g. California, Iceland, central Italy) we observe and quantify the biases of the S40RTS Earth velocity model that cause a delay or early arrival of the predicted seismic waves to the seismic stations at certain azimuths. We gather these misestimations of predicted seismic wave arrivals as corrections that can be applied to teleseismic source inversions in order to improve location accuracy. The similarity of corrections that we observe for events in the same region suggests they could be used as regional corrections. We also show that these corrections not only can be used to accurately locate global events but also can help us to accurately obtain the source mechanisms of these events. In future, by gathering these corrections for all the events with existing InSAR source models (i.e. more than 120 global events so far) we might be able to increase accuracy of velocity models of the upper mantle, e.g. by using finite-frequency tomography

Widespread fault creep in the northern San Francisco Bay Area revealed by multi-station cluster detection of repeating earthquakes

We search for repeating earthquakes (REs) in the northern San Francisco Bay Area in 1984–2016. By comparing over 670,000 waveforms from ∼75,000 events, we identify candidate clusters of events whose waveforms have high cross-correlation coefficients at multiple stations. A key difference with our approach is that these “multistation clusters” do not require each event in a family be recorded at multiple common stations. We validate these candidate REs by estimating precise relative relocations for the events in each cluster. We identify 59 RE families whose relocated hypocenters are separated by less than one source radius. These are distributed throughout the Maacama fault zone and along the northern Rodgers Creek and central Bartlett Springs faults, implying that widespread, pervasive creep occurs on those faults, at rates of 1–6 mm/year. At either end of the Maacama fault, the RE pattern highlights structural complexity, suggesting that multiple subparallel strands may be active and creeping

A Python Code for Detecting True Repeating Earthquakes from Self‐Similar Waveforms (FINDRES)

Seismic data are generally scrutinized for repeating earthquakes (REs) to evaluate slip rates, changes in the mechanical properties of a fault zone, and accelerating nucleation processes in foreshock and aftershock sequences. They are also used to study velocity changes in the medium, earthquake physics and prediction, and for constraining creep rate models at depth. For a robust detection of repeaters, multiple constraints and different parameter configurations related to waveform similarity have been proposed to measure cross‐correlation values at a local seismic network and evaluate the location of overlapping sources. In this work, we developed a Python code to identify REs (FINDRES), inspired by previous literature, which combines both seismic waveform similarity and differential S‐P travel time measured at each seismic station. A cross‐spectral method is applied to evaluate precise differential arrival travel times between earthquake pairs, allowing a subsample precision and increasing the capacity to resolve an overlapping common source radius. FINDRES is versatile and works with and without P‐ and S‐wave phase pickings, and has been validated using synthetic and real data, and provides reliable results. It would contribute to the implementation of open‐source Python packages in seismology, supporting the activities of researchers and the reproducibility of scientific results