Analysis of Source Mechanism and Coulomb Stress Change (Case Study: Molucca Sea Earthquakes November 15th, 2014 and November 14th, 2019)

On November 15, 2014, and November 14, 2019, two major earthquakes occurred in the Molucca Sea with a moment magnitude of Mw 7.0 and Mw 7.1, respectively. These earthquakes were caused by the convergence activity between the Sunda Plate and the Philippine Sea Plate which form a double subduction zone in the Molucca Sea. We carried out the moment tensor inversion using Kiwi Tools to analyze the source mechanism for both of the earthquakes. The results show a thrust fault mechanism with the strike, dip, and rake of the ruptured fault planes are 187°, 63°, 85° and 196°, 43°, 83°, for the first and second events, respectively. We refine the location of the two mainshocks and their aftershocks by performing hypocenter relocation using the double difference method. This resulted in NE-SW aftershocks distribution for both events which occured close to the Molucca Sea Plate boundaries with the mainshocks location are relatively close to each other (± 50.32 km). Finally, we calculate the Coulomb stress changes to analyze the triggering effect between the two major events and between the mainshock and its aftershocks for each event. The results show that the hypocenter of the November 14, 2019 earthquake is in the increased zone of Coulomb stress changes produced by the November 15, 2014 earthquake with the value of 1.2 bar. The aftershocks of both events also occurred in the increased Coulomb stress changes with the range value of 0.5 - 1.8 bar for the first event and 0.2 - 0.8 bar for the second event.

The stop-start control of seismicity by fault bends along the Main Himalayan Thrust

The Himalayan megathrust accommodates most of the relative convergence between the Indian and Eurasian plates, producing cycles of blind and surface-breaking ruptures. Elucidating the mechanics of down-dip segmentation of the seismogenic zone is key to better determine seismic hazards in the region. However, the geometry of the Himalayan megathrust and its impact on seismicity remains controversial. Here, we develop seismic cycle simulations tuned to the seismo-geodetic data of the 2015 Mw 7.8 Gorkha, Nepal earthquake to better constrain the megathrust geometry and its role on the demarcation of partial ruptures. We show that a ramp in the middle of the seismogenic zone is required to explain the termination of the coseismic rupture and the source mechanism of up-dip aftershocks consistently. Alternative models with a wide décollement can only explain the mainshock. Fault structural complexities likely play an important role in modulating the seismic cycle, in particular, the distribution of rupture sizes. Fault bends are capable of both obstructing rupture propagation as well as behave as a source of seismicity and rupture initiation.

Can plasticity explain microseismic source mechanisms?

<p>The microseismic events can often be characterized by a complex non-double couple source mechanism. Recent laboratory studies recording the acoustic emission during rock deformation help connecting the components of the seismic moment tensor with the failure process. In this complementary contribution, we offer a mathematical model which can clarify these connections. We derive the seismic moment tensor based on classical continuum mechanics and plasticity theory. The moment tensor density can be represented by the product of elastic stiffness tensor and the plastic strain tensor. This representation of seismic sources has several useful properties: i) it accounts for incipient faulting as a microseismicity source mechanism, ii) it does not require a pre-defined fracture geometry, iii) it accounts for both shear and volumetric source mechanisms, iv) it is valid for general heterogeneous and anisotropic rocks, and v) it is consistent with elasto-plastic geomechanical simulators. We illustrate the new approach using 2D numerical examples of seismicity associated with cylindrical openings, analogous to wellbore, tunnel or fluid-rich conduit, and provide a simple analytic expression of the moment density tensor. We compare our simulation results with previously published data from laboratory and field experiments.  We consider three special cases corresponding to "dry" isotropic rocks, "dry" transversely isotropic rocks and "wet" isotropic rocks. The model highlights theoretical links between stress state, geomechanical parameters and conventional representations of the moment tensor such as Hudson source type parameters. </p>

Estimating Seismic Moment Tensors based on Bayesian Machine Learning

<p><span>Estimating fast earthquakes’ source mechanism is essential for near real time hazard assessments, which are based on shakemaps and further downstream analysis such as physics based aftershock probability calculations. The model and data uncertainties associated to the estimated source mechanism are also crucial. We propose a Baysian Machine Learning algorithm trained on normalized synthetic waveforms for estimating the full moment tensor of earthquakes almost instantaneously with associated source parameter uncertainties. </span></p><p><span>A prior assumption is an appropriate location of the earthquakes along with its associated uncertainties. Here, this is obtained by already established Machine learning based algorithms, where the training data set is computed by forward calculations of synthetic waveforms based on Green’s functions calculated for a specified 1-D velocity model using the Pyrocko software package. The </span><span>learned labels, </span><span>which are the information learned by the Machine Learning algorithm associated to the data,</span><span> are the moment tensor components, described with only five unique parameters. For predefined locations in an area of interest we train a full independent Bayesian Convolutional Neural Network (BNN). </span></p><p><span>With variational inference the weights of the network are not scalar but represent a distribution </span><span>of weights for the activation of neurons. </span><span> Each evaluation of input data into our BNN yields therefore to a set of predictions with associated probabilities. This allows us to evaluate an ensemble of possible source mechanisms for each evaluation of input waveform data. </span></p><p> <span>As a test set, we trained our models for an area south of the Coso geothermal field in California for a fixed set of broadband stations at maximum 150 km distance. We validate our approach with a subset of earthquakes from the Ridgecrest 2019-2020 sequence. For this data set we compare the results of the estimates of our Machine Learning based approach with independently determined focal mechanism and moment tensors. Overall, we benchmark our approach with</span><span> data </span><span>unseen during the training process </span><span>of the Machine Learning models</span><span> and show its capabilities for generating similar source mechanism estimations as independent studies within only a few seconds processing time per earthquake. </span><span>We finally apply the method to seismic data of a research network monitoring the area around two south-german geothermal power plants.</span><span> Our approach demonstrates the potential of Machine Learning for being implemented in operational frameworks for fast earthquake source mechanism estimation with associated uncertainties.</span></p><p><br><br></p>