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Register a new userStatistical analysis of fireballs: Seismic signature survey
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Fireballs are infrequently recorded by seismic sensors on the ground. If recorded, they are usually reported as one-off events. This study is the first seismic bulk analysis of the largest single fireball data set, observed by the Desert Fireball Network (DFN) in Australia in the period 2014-2019. The DFN typically observes fireballs from cm-m scale impactors. We identified 25 fireballs in seismic time series data recorded by the Australian National Seismograph Network (ANSN). This corresponds to 1.8% of surveyed fireballs, at the kinetic energy range of 10$^6$ to 10$^{10}$ J. The peaks observed in the seismic time series data were consistent with calculated arrival times of the direct airwave or ground-coupled Rayleigh wave caused by shock waves by the fireball in the atmosphere (either due to fragmentation or the passage of the Mach cone). Our work suggests that identification of fireball events in the seismic time series data depends both on physical properties of a fireball (such as fireball energy and entry angle in the atmosphere) and the sensitivity of a seismic instrument. This work suggests that fireballs are likely detectable within 200 km direct air distance between a fireball and seismic station, for sensors used in the ANSN. If each DFN observatory had been accompanied by a seismic sensor of similar sensitivity, 50% of surveyed fireballs could have been detected. These statistics justify the future consideration of expanding the DFN camera network into the seismic domain.
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Detecting earthquake events from seismic time series has proved itself a challenging task. Manual detection can be expensive and tedious due to the intensive labor and large scale data set. In recent years, automatic detection methods based on machine learning have been developed to improve accuracy and efficiency. However, the accuracy of those methods relies on a sufficient amount of high-quality training data, which itself can be expensive to obtain due to the requirement of domain knowledge and subject matter expertise. This paper is to resolve this dilemma by answering two questions: (1) provided with a limited number of reliable labels, can we use them to generate more synthetic labels; (2) Can we use those synthetic labels to improve the detectability? Among all the existing generative models, the generative adversarial network (GAN) shows its supreme capability in generating high-quality synthetic samples in multiple domains. We designed our model based on GAN. In particular, we studied several different network structures. By comparing the generated results, our GAN-based generative model yields the highest quality. We further combine the dataset with synthetic samples generated by our generative model and show that the detectability of our earthquake classification model is significantly improved than the one trained without augmenting the training set.
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