The traditional method in determining first arrival time of earthquake dataset is time consuming process due to waveform manual inspection. Additional waveform attributes can help determine events detection. One of the widely used attribute is The Short Term Averaging/Long Term Averaging (STA/LTA) which is simply division moving average of waveform amplitude between short time and longer time. Alternatively, waveform attribute can also be built using kurtosis and skewness. The kurtosis attribute is defined as sharpness of data distribution. By definition, the maximum signal should be at or close to the P wave arrival. The skewness is typically used to show normal distribution of the data. The uniqueness of this method is that it has an ability to determine whether the first P wave arrival has positive of negative number. The skewness calculation is similar to kurtosis but it uses the power of 3 instead of 4. With the objective of generating efficient scheme to pick first time arrival, we explore use artificial neural network and a combination of kurtosis and skewness attributes. We use a collection of magnitude events with moment magnitude larger than 3 located close to Moluccas island, Indonesia. We collected all events information from the Indonesian Agency of Meteorology, Climatology and Geophysics. The process is started with manually pick all P wave arrivals using manual inspection. Next, we trained the artificial neural network with attributes numbers as inputs and arrival time we manually picked as the output. In total we used 100 regional events that has clear P wave phases. Then, we validated the results until reaching 0.99 accuracy. In the last step, we tested the once trained procedures on new waveforms contained events. Current result shows an average of 0.4s different between manual pick and trained picked from machine learning. The accuracy can be improved by applying a band pass 0.1-2 Hz filtering with an average of 0.2s.
The Short Term Averaging/Long Term Averaging (STA/LTA) has been widely used to detect earthquake arrival time. The method simply calculates the ratio of moving average of the waveform amplitude at short and long-time windows. However, although STA/LTA signals can distinguish between real events and noise, we still recognize some lack of accuracies in first P wave arrival pickings. In this study, we attempt to implement one machine learning method popularly, Artificial Neural Network (ANN) that employ input, hidden and output layer similar as human brain works. Note that in this study, we also try to add input parameters with another derivative signal attributes such as Recursive STA/LTA and Carl STA/LTA. The processing step started by collecting event waveforms from the Agency of Meteorology, Climatology and Geophysics. We chose regional events with moment magnitude higher than 3 in the Maluku region Indonesia. Next, we apply all STA/LTA attributes to the input waveforms. We also tested our STA/LTA with synthetic data and additional noise. Further step, we manually picked the arrival of P wave events and used this as the output for ANN. In total, we used 100 events for arrival data training in P wave phases. In the validation process, an accuracy of more than 0.98 can be obtained after 200 iterations. Final outputs showed, that in average, the difference between manual picking and automatic picking from ANN is 0.45 s. We are able to increase the accuracy by band pass filter (0.1 – 3 Hz) all signal and improve the mean into 0.15s difference between manual picking and ANN picks.
Canine facial eosinophilic furunculosis (FEF) is a hyperacute dermatopathy especially of the nasal bridge of dogs and is probably associated with type I hypersensitivity secondary to arthropod bites. The aim of this study is to report on a FEF case in a four-year-old female free-roaming mixed-breed dog showing papules on the nasal bridge that evolved to an ulcerated plaque. No other clinical, hematological, or biochemical alterations were detected. Cytology revealed eosinophilic and neutrophilic inflammation associated with bacterial infection. Punch biopsies were obtained for histopathological and microbiological analysis. Histopathology revealed marked, acute, multifocal to coalescent granulomatous eosinophilic furunculosis, and mild, acute, multifocal eosinophilic folliculitis. Microbiology revealed growth of coagulase-positive Staphylococcus sp. Clinical and histopathological findings were suggestive of facial eosinophilic folliculitis and furunculosis. Complete remission of the lesions was obtained after treatment. This condition is hyperacute, progressive, with a papular and erosive to ulcerative pattern, good prognosis, and its development is linked to arthropod bites. Furthermore, anti-inflammatory therapy is effective in treating the disease.
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