Análisis comparativo de diferentes métodos de picado automático de fases en terremotos registrados en la estación sismológica de La Plata (LPA)

Analizamos ocho métodos de detección y picado automático de fases internas de terremotos. Entre ellos, tres son métodos convencionales, dos son métodos nuevos desarrollados a partir de mejoras a los métodos convencionales, y los otros tres son adaptaciones a métodos originalmente concebidos para picado de primeros arribos en datos de sísmica de exploración. Para analizar los métodos, seleccionamos once registros de una hora de duración obtenidos en la Estación Sismológica de la Plata (LPA) correspondientes a terremotos regionales y telesísmicos con magnitudes mayores a 6 (Mw) y diversas distancias epicentrales y profundidades de foco. Los registros elegidos fueron previamente picados manualmente por un analista. Se buscó picar de manera automática un total de 30 fases, de las cuales 25 habían sido originalmente observadas por el analista. Las otras 5 fases, fueron detectadas a partir de la aplicación de los métodos automáticos, y luego corroboradas mediante el modelo IASP91. Se estudió la eficiencia de los métodos a partir del porcentaje de detecciones obtenidas y del porcentaje de picados falsos generados. Se analizaron también los errores en los picados desde un enfoque estadístico para estimar las exactitudes y precisiones de los métodos propuestos. Este análisis permitió realizar una valoración relativa de los ocho métodos de picado automático propuestos. Los resultados obtenidos revelan que algunos de los métodos exhiben ventajas significativas por sobre otros. Estos métodos que muestran mejor desempeño serían apropiados para realizar sistemáticamente el picado automático, tanto para estudios de sismología global como para estudios de sismicidad, especialmente cuando se cuenta con un gran volumen de datos.”We analyzed eight methods of detection and automatic body phase picking. Three of them are conventional methods, two are new methods developed from improvements to conventional methods, and the other three are methods originally designed for first-break picking of seismic exploration data. To analyze the methods, we selected eleven one-hour records obtained at La Plata Seismological Station (LPA) corresponding to regional and teleseismic earthquakes with magnitudes greater than 6 (Mw) and diverse epicentral distances and focus depths. The records were previously manually picked by an analyst. We attempted to pick a total of 30 phases automatically, 25 of which had originally been observed by the analyst. The other 5 phases, were identified from the application of the automatic methods, and then corroborated by using the IASP91 model. The efficiency of the methods was studied based on the detection percentage obtained and the false phase percentage generated. The picking errors were also analyzed from a statistical point of view in order to estimate the accuracy and precision of the proposed methods. This analysis allowed us to make a relative assessment of the eight automatic picking methods proposed. The results show that some of the methods exhibit significant advantages over the others. These methods that show better performance would be appropriate to systematically carry out the automatic picking, both for global seismology studies and seismicity studies, especially when a large volume of data is availableKeywords: event detection, earthquake phases, automatic picking, false alarms.Asociación Argentina de Geofísicos y Geodesta

A new signal processing method for acoustic emission/microseismic data analysis

The acoustic emission/microseismic technique (AE/MS) has emerged as one of the most important techniques in recent decades and has found wide applications in different fields. Extraction of seismic event with precise timing is the first step and also the foundation for processing AE/MS signals. However, this process remains a challenging task for most AE/MS applications. The process has generally been performed by human analysts. However, manual processing is time consuming and subjective. These challenges continue to provide motivation for the search for new and innovative ways to improve the signal processing needs of the AE/MS technique. This research has developed a highly efficient method to resolve the problems of background noise and outburst activities characteristic of AE/MS data to enhance the picking of P-phase onset time. The method is a hybrid technique, comprising the characteristic function (CF), high order statistics, stationary discrete wavelet transform (SDWT), and a phase association theory. The performance of the algorithm has been evaluated with data from a coal mine and a 3-D concrete pile laboratory experiment. The accuracy of picking was found to be highly dependent on the choice of wavelet function, the decomposition scale, CF, and window size. The performance of the algorithm has been compared with that of a human expert and the following pickers: the short-term average to long-term average (STA/LTA), the Baer and Kradolfer, the modified energy ratio, and the short-term to long-term kurtosis. The results show that the proposed method has better picking accuracy (84% to 78% based on data from a coal mine) than the STA/LTA. The introduction of the phase association theory and the SDWT method in this research provided a novelty, which has not been seen in any of the previous algorithms --Abstract, page iii

Développement d'une nouvelle technique de pointé automatique pour les données de sismique réfraction

Accurate picking of first arrival times plays an important role in many seismic studies, particularly in seismic tomography and reservoirs or aquifers monitoring. A new adaptive algorithm has been developed based on combining three picking methods (Multi-Nested Windows, Higher Order Statistics and Akaike Information Criterion). It exploits the benefits of integrating three properties (energy, gaussianity, and stationarity), which reveal the presence of first arrivals. Since time uncertainties estimating is of crucial importance for seismic tomography, the developed algorithm provides automatically the associated errors of picked arrival times. The comparison of resulting arrival times with those picked manually, and with other algorithms of automatic picking, demonstrates the reliable performance of this algorithm. It is nearly a parameter-free algorithm, which is straightforward to implement and demands low computational resources. However, high noise level in the seismic records declines the efficiency of the developed algorithm. To improve the signal-to-noise ratio of first arrivals, and thereby to increase their detectability, double stacking in the time domain has been proposed. This approach is based on the key principle of the local similarity of stacked traces. The results demonstrate the feasibility of applying the double stacking before the automatic picking.Un pointé précis des temps de premières arrivées sismiques joue un rôle important dans de nombreuses études d’imagerie sismique. Un nouvel algorithme adaptif est développé combinant trois approches associant l’utilisation de fenêtres multiples imbriquées, l’estimation des propriétés statistiques d’ordre supérieur et le critère d’information d’Akaike. L’algorithme a l’avantage d’intégrer plusieurs propriétés (l’énergie, la gaussianité, et la stationnarité) dévoilant la présence des premières arrivées. Tandis que les incertitudes de pointés ont, dans certains cas, d’importance équivalente aux pointés eux-mêmes, l’algorithme fournit aussi automatiquement une estimation sur leur incertitudes. La précision et la fiabilité de cet algorithme sont évaluées en comparant les résultats issus de ce dernier avec ceux provenant d’un pointé manuel, ainsi que d’autres pointeurs automatiques. Cet algorithme est simple à mettre en œuvre et ne nécessite pas de grandes performances informatiques. Cependant, la présence de bruit dans les données peut en dégrader la performance. Une double sommation dans le domaine temporel est alors proposée afin d’améliorer la détectabilité des premières arrivées. Ce processus est fondé sur un principe clé : la ressemblance locale entre les traces stackées. Les résultats montrent l’intérêt qu’il y a à appliquer cette sommation avant de réaliser le pointé automatique