A neural network seismic detector

This experimental study focuses on a detection system at the seismic station level that should have a similar role to the detection algorithms based on the ratio STA/LTA. We tested two types of neural network: Multi-Layer Perceptrons and Support Vector Machines, trained in supervised mode. The universe of data consisted of 2903 patterns extracted from records of the PVAQ station, of the seismography network of the Institute of Meteorology of Portugal. The spectral characteristics of the records and its variation in time were reflected in the input patterns, consisting in a set of values of power spectral density in selected frequencies, extracted from a spectro gram calculated over a segment of record of pre-determined duration. The universe of data was divided, with about 60% for the training and the remainder reserved for testing and validation. To ensure that all patterns in the universe of data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. The best results, in terms of sensitivity and selectivity in the whole data ranged between 98% and 100%. These results compare very favorably with the ones obtained by the existing detection system, 50%.

On-line operation of an intelligent seismic detector

This study describes the on-line operation of a seismic detection system to act at the level of a seismic station providing similar role to that of a STA /LTA ratio-based detection algorithms. The intelligent detector is a Support Vector Machine (SVM), trained with data consisting of 2903 patterns extracted from records of the PVAQ station, one of the seismographic network's stations of the Institute of Meteorology of Portugal (IM). Records' spectral variations in time and characteristics were reflected in the SVM input patterns, as a set of values of power spectral density at selected frequencies. To ensure that all patterns of the sample data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. After having been trained, the proposed system was experimented in continuous operation for unseen (out of sample) data, and the SVM detector obtained 97.7% and 98.7% of sensitivity and selectivity, respectively. The same type of ANN presented 88.4 % and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM. © 2013 Springer-Verlag Berlin Heidelberg.

Study of sewage sludge aimed at agricultural soil amendment by thermogravimetric analysis and fourier-transform infrared spectroscopy

Thermal degradation and gaseous products evolving from the pyrolysis of sewage sludge, aimed at agricultural soil amendment, were investigated using Thermogravimetric Analysis in conjunction with Fourier Transform Infrared Analysis (TG-FTIR). The materials were studied in temperatures ranging from 30 to 800 ºC. Furthermore infrared spectra of sewage sludge samples were performed as a complementary technique. In parallel the sewage sludge was spiked with ibuprofen in order to test whether the mentioned techniques are able to detect the drug. Thermal analysis showed the range of 200-400ºC as the most characteristic for weight loss, corresponding with the organic matter volatilization, while the range of 500-800ºC was also characteristic and due to the volatilization of carbonates. On the other hand, ibuprofen-spiking tests identified at temperature range (150-250ºC) where the compound totally volatilizes, therefore, in this work, the detection of ibuprofen by TGA was established for concentrations higher than 0.5 g/kg sludge, concentration 102 times higher than the concentrations measured by other authors in regular sewage sludge (Martín, et al., 2010). A correlation has been found between the ibuprofen concentrations in the sludge and the intensity of the absorption bands, both for FT-IR spectra at the maximum emission temperature for ibuprofen (232ºC) as for the FT-IR spectra of the non-pyrolyzed samples.