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Register a new userEvidences for higher nocturnal seismic activity at the Mt. Vesuvius
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We analyze hourly seismic data measured at the Osservatorio Vesuviano Ovest (OVO, 1972-2014) and at the Bunker Est (BKE, 1999-2014) stations on the Mt. Vesuvius. The OVO record is complete for seismic events with magnitude M > 1.9. We demonstrate that before 1996 this record presents a daily oscillation that nearly vanishes afterwards. To determine whether a daily oscillation exists in the seismic activity of the Mt. Vesuvius, we use the higher quality BKE record that is complete for seismic events with magnitude M > 0.2. We demonstrate that BKE confirms that the seismic activity at the Mt. Vesuvius is higher during nighttime than during day-time. The amplitude of the daily oscillation is enhanced during summer and damped during winter. We speculate possible links with the cooling/warming diurnal cycle of the volcanic edifice, with external geomagnetic field and with magnetostriction that should also stress the rocks. We find that the amplitude of the seismic daily cycle changes in time and has been increasing since 2008. Finally, we propose a seismic activity index to monitor the 24-hour oscillation that could be used to complement other methodologies currently adopted to determine the seismic status of the volcano and to prevent the relative hazard.
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Mazzarella and Scafetta (2016) showed that the seismic activity recorded at the Bunker-East (BKE) Vesuvian station from 1999 to 2014 suggests a higher nocturnal seismic activity. However, this station is located at about 50 m from the main road to the volcanos crater and since 2009 its seismograms also record a significant diurnal cultural noise due mostly to tourist tours to Mt. Vesuvius. Herein, we investigate whether the different seismic frequency between day and night times could be an artifact of the peculiar cultural noise that affects this station mostly from 9:00 am to 5:00 pm from spring to fall. This time-distributed cultural noise should evidently reduce the possibility to detect low magnitude earthquakes during those hours but not high magnitude events. Using hourly distributions referring to different magnitude thresholds from M = 0.2 to M = 2.0, the Gutenberg-Richter magnitude-frequency diagram applied to the day and night-time sub-catalogs and Montecarlo statistical modeling, we demonstrate that the day-night asymmetry persists despite an evident disruption induced by cultural noise during day-hours. In particular, for the period 1999-2017, and for earthquakes with M > 2 we found a Gutenberg-Richter exponent b = 1.66 +/- 0.07 for the night-time events and b = 2.06 +/- 0.07 for day-time events. Moreover, we repeat the analysis also for an older BKE catalog covering the period from 1992 to 2000 when cultural noise was not present. The analysis confirms a higher seismic nocturnal activity that is also characterized by a smaller Gutenberg-Richter exponent b for M > 2 earthquakes relative to the day-time activity. Thus, the found night-day seismic asymmetric behavior is likely due to a real physical feature affecting Mt. Vesuvius.
129 Long Period (LP) events, divided into two families of similar events, were recorded by the 50 stations deployed on Mount Etna in the second half of June 2008. During this period lava was flowing from a lateral fracture after a summit strombolian eruption. In order to understand the mechanisms of these events, we perform moment tensor
The effects of several nonlinear regularization techniques are discussed in the framework of 3D seismic tomography. Traditional, linear, $ell_2$ penalties are compared to so-called sparsity promoting $ell_1$ and $ell_0$ penalties, and a total variation penalty. Which of these algorithms is judged optimal depends on the specific requirements of the scientific experiment. If the correct reproduction of model amplitudes is important, classical damping towards a smooth model using an $ell_2$ norm works almost as well as minimizing the total variation but is much more efficient. If gradients (edges of anomalies) should be resolved with a minimum of distortion, we prefer $ell_1$ damping of Daubechies-4 wavelet coefficients. It has the additional advantage of yielding a noiseless reconstruction, contrary to simple $ell_2$ minimization (`Tikhonov regularization) which should be avoided. In some of our examples, the $ell_0$ method produced notable artifacts. In addition we show how nonlinear $ell_1$ methods for finding sparse models can be competitive in speed with the widely used $ell_2$ methods, certainly under noisy conditions, so that there is no need to shun $ell_1$ penalizations.
This paper introduces novel deep recurrent neural network architectures for Velocity Model Building (VMB), which is beyond what Araya-Polo et al 2018 pioneered with the Machine Learning-based seismic tomography built with convolutional non-recurrent neural network. Our investigation includes the utilization of basic recurrent neural network (RNN) cells, as well as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Performance evaluation reveals that salt bodies are consistently predicted more accurately by GRU and LSTM-based architectures, as compared to non-recurrent architectures. The results take us a step closer to the final goal of a reliable fully Machine Learning-based tomography from pre-stack data, which when achieved will reduce the VMB turnaround from weeks to days.
Incorporating prior knowledge on model unknowns of interest is essential when dealing with ill-posed inverse problems due to the nonuniqueness of the solution and data noise. Unfortunately, it is not trivial to fully describe our priors in a convenient and analytical way. Parameterizing the unknowns with a convolutional neural network (CNN), and assuming an uninformative Gaussian prior on its weights, leads to a variational prior on the output space that favors natural images and excludes noisy artifacts, as long as overfitting is prevented. This is the so-called deep-prior approach. In seismic imaging, however, evaluating the forward operator is computationally expensive, and training a randomly initialized CNN becomes infeasible. We propose, instead, a weak version of deep priors, which consists of relaxing the requirement that reflectivity models must lie in the network range, and letting the unknowns deviate from the network output according to a Gaussian distribution. Finally, we jointly solve for the reflectivity model and CNN weights. The chief advantage of this approach is that the updates for the CNN weights do not involve the modeling operator, and become relatively cheap. Our synthetic numerical experiments demonstrate that the weak deep prior is more robust with respect to noise than conventional least-squares imaging approaches, with roughly twice the computational cost of reverse-time migration, which is the affordable computational budget in large-scale imaging problems.
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