Accounting for uncertain fault geometry in earthquake source inversions – II: application to the Mw 6.2 Amatrice earthquake, central Italy

SUMMARY Our understanding of earthquake sources is limited by the availability and the quality of observations and the fidelity of our physical models. Uncertainties in our physical models will naturally bias our inferences of subsurface fault slip. These uncertainties will always persist to some level as we will never have a perfect knowledge of the Earth’s interior. The choice of the forward physics is thus ambiguous, with the frequent need to fix the value of several parameters such as crustal properties or fault geometry. Here, we explore the impact of uncertainties related to the choice of both fault geometry and elastic structure, as applied to the 2016 Mw 6.2 Amatrice earthquake, central Italy. This event, well instrumented and characterized by a relatively simple fault morphology, allows us to explore the role of uncertainty in basic fault parameters, such as fault dip and position. We show that introducing uncertainties in fault geometry in a static inversion reduces the sensitivity of inferred models to different geometric assumptions. Accounting for uncertainties thus helps infer more realistic and robust slip models. We also show that uncertainties in fault geometry and Earth’s elastic structure significantly impact estimated source models, particularly if near-fault observations are available.

Remarks on a Discussion of Balint’s Paper on Technique

Winnicott looks at Balint’s contrasting of verbal interpretation in the clinical analytic setting, with what cannot be done by verbal interpretation alone. Winnicott elaborates why a patient may not be able to use verbal interpretation. For him a patient needs to be able to function at the level of the self and two others, the parents, but some patients may have experienced what Balint calls the area of the basic fault, an early environmental failure that reduces this capacity in the patient.

Gearbox Fault Identification and Classification with Convolutional Neural Networks

Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time domain signal such as standard deviation, skewness, and kurtosis. In the frequency domain, the spectrum obtained with FFT is divided into multiple bands, and the root mean square (RMS) value is calculated for each one so the energy maintains its shape at the spectrum peaks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Comparing with peer algorithms, the present method exhibits the best performance in the gearbox fault diagnosis.

A hybrid approach for aircraft fault diagnosis based on fault inference and fault identification

A cockpit instrumentation system provides various elements of information for pilots. However, logical inference based on a cockpit instruments fault tree (FT) and reliability sometimes cannot give a correct diagnosis of failures. In addition, in flight control systems (FCS), a fault identification method based on the multiple-model (MM) estimator cannot find the basic fault cause. To deal with these problems, a hybrid approach which is capable of integrating inference and fault identification is proposed. In this approach, the event nodes of the FT which have correlations to the FCS are separated into modules. Each module corresponds to a fault mode of the FCS. To use these correlations, fault inference and the MM estimator can share fault diagnosis information. Simulation results show that the proposed approach is helpful in detecting the root cause of failure and is more correct than single fault diagnosis method.