Seismic signals discrimination based on instantaneous frequency

In this paper a problem of seismic vibration signals discrimination and clustering is investigated. We propose two criteria based on instantaneous frequency (IF) of the seismic signal. IF of a raw multicomponent signal is meaningless and a decomposition must be performed in order to obtain a monocomponent signal. One of the possible solutions incorporates the Hilbert-Huang transform. It is based on Empirical Mode Decomposition (EMD) algorithm. It is a data-driven procedure which calculates so called Intrinsic Mode Functions (IMFs) and a Residuum, which added all together give the raw signal. One of the proposed criteria quantifies distribution of the IF through the signal and provide limited information about volatility of IF throughout the entire signal (for a given monocomponent). The second criterion gives information about the most frequently occurring instantaneous frequency in the considered monocomponent. Usefulness of IF in discrimination of seismic vibration signals is validated by using considered criteria for clustering of seismic signals

Features based on instantaneous frequency for seismic signals clustering

Seismic signals discrimination is a multidimensional problem since recorded events may vary in terms of type, location, energy, etc. Recently, two discrimination features based on instantaneous frequency (IF) were proposed by the Authors. The first of these features is determined by distribution of the signals’ first Intrinsic Mode Function’s (IMF) IF. The second one is a particular simplification of the previous one as it gives information about the most frequently occurring instantaneous frequency in the considered first IMF. In order to exhibit features’ potential in distinguishing of seismic vibration signals, one has to use clustering algorithms. The features were already subjected to k-means algorithm. In this paper we show results of agglomerative hierarchical clustering (AHCA) and compare it with outcomes of k-means. In order to test optimal number of clusters, method based on average silhouette was accomplished. The results are illustrated by analysis of real seismic vibration signals from an underground copper ore mine

Obesity as a risk factor of in-hospital outcomes in patients with endometrial cancer treated with laparoscopic surgical mode

Objectives: Obesity has been suggested to have a negative influence on procedural outcomes of endometrial cancer laparoscopic treatment. Obesity and other possible risk factors of laparoscopic endometrial cancer treatment has not been precisely described in the literature. The aim of the study is to determine the factors that have the greatest influence on the course of laparoscopic surgery for endometrial cancer, with particular emphasis on the influence of obesity. Material and methods: The study included 75 females who were treated for endometrial cancer by laparoscopic surgery. Preoperative body-mass index (BMI), waist circumference(WC), waist to hip ratio(WHR), and selected anatomical indices were measured. The duration of surgery and hospitalization stay, loss of hemoglobin, and procedural-related complications served as parameters of in-hospital outcomes. Results: Multiple linear regression analysis indicate the body mass as most sensitive parameter of obesity which influence in-hospital outcomes in patients treated with laparoscopic procedure. Procedural-related complications occurred in the group of patients with significantly greater WC and BMI. Multiple linear regression indicates also histological grading (G1–G3), external conjugate, intertrochanteric distance as significant risk factors. The multiple linear regression analysis confirmed also that implementation of sentinel lymph node procedure is related with decreased hemoglobin loss in patients with cancer of endometrium compare to lymphadenectomy without sentinel node biopsy(Est.: 0.488; 95% CI: 0.083–0.892, p = 0.018). Conclusions: The most sensitive risk factor of in-hospital outcomes in laparoscopic treatment of endometrial cancer is body mass. The implementation of the sentinel node procedure is associated with reduced surgery time and reduced hemoglobin loss

Obesity as a risk factor of in-hospital outcomes in patients with endometrial cancer treated with laparoscopic surgical mode

Objectives: Obesity has been suggested to have a negative influence on procedural outcomes of endometrial cancer laparoscopic treatment. Obesity and other possible risk factors of laparoscopic endometrial cancer treatment has not been precisely described in the literature. The aim of the study is to determine the factors that have the greatest influence on the course of laparoscopic surgery for endometrial cancer, with particular emphasis on the influence of obesity. Material and methods: The study included 75 females who were treated for endometrial cancer by laparoscopic surgery. Preoperative body-mass index (BMI), waist circumference(WC), waist to hip ratio(WHR), and selected anatomical indices were measured. The duration of surgery and hospitalization stay, loss of hemoglobin, and procedural-related complications served as parameters of in-hospital outcomes. Results: Multiple linear regression analysis indicate the body mass as most sensitive parameter of obesity which influence in-hospital outcomes in patients treated with laparoscopic procedure. Procedural-related complications occurred in the group of patients with significantly greater WC and BMI. Multiple linear regression indicates also histological grading (G1–G3), external conjugate, intertrochanteric distance as significant risk factors. The multiple linear regression analysis confirmed also that implementation of sentinel lymph node procedure is related with decreased hemoglobin loss in patients with cancer of endometrium compare to lymphadenectomy without sentinel node biopsy(Est.: 0.488; 95% CI: 0.083–0.892, p = 0.018). Conclusions: The most sensitive risk factor of in-hospital outcomes in laparoscopic treatment of endometrial cancer is body mass. The implementation of the sentinel node procedure is associated with reduced surgery time and reduced hemoglobin loss

Seismic signals hard clipping overcoming

In signal processing the clipping is understand as the phenomenon of limiting the signal beyond certain threshold. It is often related to overloading of a sensor. Two particular types of clipping are being recognized: soft and hard. Beyond the limiting value soft clipping reduces the signal real gain while the hard clipping stiffly sets the signal values at the limit. In both cases certain amount of signal information is lost. Obviously if one possess the model which describes the considered signal and the threshold value (which might be slightly more difficult to obtain in the soft clipping case), the attempt of restoring the signal can be made. Commonly it is assumed that the seismic signals take form of an impulse response of some specific system. This may lead to belief that the sine wave may be the most appropriate to fit in the clipping period. However, this should be tested. In this paper the possibility of overcoming the hard clipping in seismic signals originating from a geoseismic station belonging to an underground mine is considered. A set of raw signals will be hard-clipped manually and then couple different functions will be fitted and compared in terms of least squares. The results will be then analysed

Comparison of recent S-wave indicating methods

Seismic event consists of surface waves and body waves. Due to the fact that the body waves are faster (P-waves) and more energetic (S-waves) in literature the problem of their analysis is taken more often. The most universal information that is received from the recorded wave is its moment of arrival. When this information is obtained from at least four seismometers in different locations, the epicentre of the particular event can be estimated [1]. Since the recorded body waves may overlap in signal, the problem of wave onset moment is considered more often for faster P-wave than S-wave. This however does not mean that the issue of S-wave arrival time is not taken at all. As the process of manual picking is time-consuming, methods of automatic detection are recommended (these however may be less accurate). In this paper four recently developed methods estimating S-wave arrival are compared: the method operating on empirical mode decomposition and Teager-Kaiser operator [2], the modification of STA/LTA algorithm [3], the method using a nearest neighbour-based approach [4] and the algorithm operating on characteristic of signals’ second moments. The methods will be also compared to wellknown algorithm based on the autoregressive model [5]. The algorithms will be tested in terms of their S-wave arrival identification accuracy on real data originating from International Research Institutions for Seismology (IRIS) database

Comparison of recent S-wave indicating methods

Seismic event consists of surface waves and body waves. Due to the fact that the body waves are faster (P-waves) and more energetic (S-waves) in literature the problem of their analysis is taken more often. The most universal information that is received from the recorded wave is its moment of arrival. When this information is obtained from at least four seismometers in different locations, the epicentre of the particular event can be estimated [1]. Since the recorded body waves may overlap in signal, the problem of wave onset moment is considered more often for faster P-wave than S-wave. This however does not mean that the issue of S-wave arrival time is not taken at all. As the process of manual picking is time-consuming, methods of automatic detection are recommended (these however may be less accurate). In this paper four recently developed methods estimating S-wave arrival are compared: the method operating on empirical mode decomposition and Teager-Kaiser operator [2], the modification of STA/LTA algorithm [3], the method using a nearest neighbour-based approach [4] and the algorithm operating on characteristic of signals’ second moments. The methods will be also compared to wellknown algorithm based on the autoregressive model [5]. The algorithms will be tested in terms of their S-wave arrival identification accuracy on real data originating from International Research Institutions for Seismology (IRIS) database

Seismic signals hard clipping overcoming

In signal processing the clipping is understand as the phenomenon of limiting the signal beyond certain threshold. It is often related to overloading of a sensor. Two particular types of clipping are being recognized: soft and hard. Beyond the limiting value soft clipping reduces the signal real gain while the hard clipping stiffly sets the signal values at the limit. In both cases certain amount of signal information is lost. Obviously if one possess the model which describes the considered signal and the threshold value (which might be slightly more difficult to obtain in the soft clipping case), the attempt of restoring the signal can be made. Commonly it is assumed that the seismic signals take form of an impulse response of some specific system. This may lead to belief that the sine wave may be the most appropriate to fit in the clipping period. However, this should be tested. In this paper the possibility of overcoming the hard clipping in seismic signals originating from a geoseismic station belonging to an underground mine is considered. A set of raw signals will be hard-clipped manually and then couple different functions will be fitted and compared in terms of least squares. The results will be then analysed

Algorithm Indicating Moment of P-Wave Arrival Based on Second-Moment Characteristic

The moment of P-wave arrival can provide us with many information about the nature of a seismic event. Without adequate knowledge regarding the onset moment, many properties of the events related to location, polarization of P-wave, and so forth are impossible to receive. In order to save time required to indicate P-wave arrival moment manually, one can benefit from automatic picking algorithms. In this paper two algorithms based on a method finding a regime switch point are applied to seismic event data in order to find P-wave arrival time. The algorithms are based on signals transformed via a basic transform rather than on raw recordings. They involve partitioning the transformed signal into two separate series and fitting logarithm function to the first subset (which corresponds to pure noise and therefore it is considered stationary), exponent or power function to the second subset (which corresponds to nonstationary seismic event), and finding the point at which these functions best fit the statistic in terms of sum of squared errors. Effectiveness of the algorithms is tested on seismic data acquired from O/ZG “Rudna” underground copper ore mine with moments of P-wave arrival initially picked by broadly known STA/LTA algorithm and then corrected by seismic station specialists. The results of proposed algorithms are compared to those obtained using STA/LTA

Obesity as a risk factor of in-hospital outcomes in patients with endometrial cancer treated with traditional surgical mode

Objectives: Abdominal obesity is a risk factor for endometrial cancer. The negative impact of individual parameters of obesity on the procedural effects of endometrial cancer surgical treatment has been suggested. The aim of the current study was to estimate the relationship of particular parameters of obesity and in-hospital outcomes in patients treated surgically due to endometrial cancer. Material and methods: The study included 70 women treated surgically for endometrial cancer. Pre-operatively, mass, body mass index (BMI), waist circumference, waist-hip ratio and selected anatomical indices were measured. The duration of surgery, hospitalisation, and the loss of haemoglobin served as parameters of in-hospital procedure success. Also, procedural-related complications were estimated. Results: There were 37 (52.8%) obese females in the current study. They were obese patients presenting more advanced clinical stages of endometrial cancer before operation. The duration of operation (94.9 ± 21.6 min. vs. 76.1 ± 13.5 min., p < 0.0001), hospitalisation (12.4 ± 3.4 days vs. 10 ± 2.3 days, p = 0.0009) and haemoglobin loss (2.5 ± 0.9 g/dL vs. 1.9 ± 0.8 g/dL, p = 0.004) were significantly greater in obese patients. Multivariate analysis, among the independent predictors of the duration of operation, has confirmed the correlation between BMI, waist circumference and weight and the duration of hospitalisation. Waist and hip circumference and BMI coupled with external conjugate dimension and intertrochanteric distance have been linked with haemoglobin loss. The strongest correlation for the duration of operation, hospitalisation and haemoglobin loss was noticed for waist circumference (r = 0.7, r = 0.57 and r = 0.59). Conclusions: Waist circumference and BMI are strong predictors of in-hospital outcomes among patients with endometrial cancer treated via traditional surgical operation