Changepoint detection in seismic double-difference data: application of a trans-dimensional algorithm to data-space exploration

Double-difference (DD) seismic data are widely used to define elasticity distribution in the Earth's interior and its variation in time. DD data are often pre-processed from earthquake recordings through expert opinion, whereby pairs of earthquakes are selected based on some user-defined criteria and DD data are computed from the selected pairs. We develop a novel methodology for preparing DD seismic data based on a trans-dimensional algorithm, without imposing pre-defined criteria on the selection of event pairs. We apply it to a seismic database recorded on the flank of Katla volcano (Iceland), where elasticity variations in time have been indicated. Our approach quantitatively defines the presence of changepoints that separate the seismic events in time windows. Within each time window, the DD data are consistent with the hypothesis of time-invariant elasticity in the subsurface, and DD data can be safely used in subsequent analysis. Due to the parsimonious behaviour of the trans-dimensional algorithm, only changepoints supported by the data are retrieved. Our results indicate the following: (a) retrieved changepoints are consistent with first-order variations in the data (i.e. most striking changes in the amplitude of DD data are correctly reproduced in the changepoint distribution in time); (b) changepoint locations in time correlate neither with changes in seismicity rate nor with changes in waveform similarity (measured through the cross-correlation coefficients); and (c) the changepoint distribution in time seems to be insensitive to variations in the seismic network geometry during the experiment. Our results demonstrate that trans-dimensional algorithms can be effectively applied to pre-processing of geophysical data before the application of standard routines (e.g. before using them to solve standard geophysical inverse problems).

kNN-based Gas-bearing Prediction Using Local Waveform Similarity Gas-indication Attribute - An Application to a Tight Sandstone Reservoir

Gas-bearing prediction of tight sandstone reservoirs is significant but challenging due to the relationship between the gas-bearing property and its seismic response being nonlinear and complex. Although machine learning (ML) methods provide potential for solving the issue, the major challenge of ML applications to gas-bearing prediction is that of generating accurate and interpretable intelligent models with limited training sets. The k Nearest neighbor ( kNN) method is a supervised ML method classifying an unlabeled sample according to its k neighboring labeled samples. We have introduced a kNN-based gas-bearing prediction method. The method can automatically extract a gas-sensitive attribute called the gas-indication local waveform similarity attribute (GLWSA) combining prestack seismic gathers with interpreted gas-bearing curves. GLWSA uses the local waveform similarity among the predicting samples and the gas-bearing training samples to indicate the existence of an exploitable gas reservoir. GLWSA has simple principles and an explicit geophysical meaning. We use a numerical model and field data to test the effectiveness of our method. The result demonstrates that GLWSA is good at characterizing the reservoir morphology and location qualitatively. When the method applies to the field data, we evaluate the performance with a blind well. The prediction result is consistent with the geologic law of the work area and indicates more details compared to the root-mean-square attribute.

Clinical Validation of a Soft Wireless Continuous Blood Pressure Sensor During Surgery

We test a new wireless soft capacitance sensor (CAP) based on applanation tonometry at the radial and dorsalis pedis arteries against the gold standard, invasive arterial line (A-Line), for continuous beat to beat blood pressure (BP) measurements in the Operating Room during surgical procedures under anesthesia in 17 subjects with the mean age and body mass index (BMI) of 57. 35 ± 18.72 years and 27.36 ± 4.20 kg/m2, respectively. We have identified several parameters to monitor in order to compare how well the CAP sensor tracks the entire hemodynamic waveform as compared to the A-Line. This includes waveform similarity, heart rate (HR), absolute systolic BP (SBP), diastolic BP (DBP), and temporal response to a vasopressor. Overall, the CAP sensor shows good correlations with A-Line with respect to hemodynamic shape (r > 0.89), HR (mean bias = 0.0006; SD = 0.17), absolute SBP, and DBP in a line of best fit (slope = 0.98 in SBP; 1.08 in DBP) and the mean bias derived from Bland-Altman method to be 1.92 (SD = 12.55) in SBP and 2.38 (SD = 12.19) in DBP across body habitus and age in OR patients under general anesthesia. While we do observe drifts in the system, we still obtain decent correlations with respect to the A-Line as evidenced by excellent linear fit and low mean bias across patients. When we post-process using a different calibration method to account for the drift, the mean bias and SD improve dramatically to −1.85 and 7.19 DBP as well as 1.43 and 7.43 SBP, respectively, indicating a promising potential for improvement when we integrate strategies to account for movement identified by our integrated accelerometer data.

Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings

Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.

Repeating Earthquakes During Multiple Phases of Unrest and Eruption at Mount Agung, Bali, Indonesia, 2017

In 2017, Mount Agung produced a small (VEI 2) eruption that was preceded by an energetic volcano-tectonic (VT) swarm (>800 earthquakes per day up to M4.9) and two months of declining activity. The period of decreased seismic activity complicated forecasting efforts for scientists monitoring the volcano. We examine the time history of earthquake families at Mount Agung in search of additional insight into the temporal changes in the shallow crust prior to eruption. Specifically, we analyze the period of declining seismic activity about five weeks prior to the eruption when forecasting uncertainty was greatest. We use REDPy (Hotovec-Ellis and Jeffries, 2016) to build a catalog of 6,508 earthquakes from 18 October 2017–15 February 2018 and group them into families of repeating earthquakes based on waveform similarity using a cross-correlation coefficient threshold of 0.8. We show that the evolution of earthquake families provides evidence that Mount Agung was progressing toward eruption even though overall earthquake rates and seismic-energy-release declined. We find that earthquake families that dominated seismicity during the beginning of the crisis ceased near the onset of tremor on 12 November 2017. Then, earthquake families took on characteristics commonly observed during effusive phases of eruptions on 15 November—a full six days before the first phreatomagmatic eruption on 21 November 2017 and a full ten days before the actual onset of lava effusion on 25 November 2017. We interpret the transitions in seismicity as the manifestation of a three-phase physical model including an Intrusion Phase, a Transition Phase, and a Eruptive Phase. During the Intrusion Phase, seismicity was dominated by VT earthquakes with a relatively high percentage of repeaters (59%) grouped into numerous (65) simultaneous families. During the Eruptive Phase, seismicity included both VT and low frequency earthquakes that grouped into relatively long-lived families despite a low overall percentage of repeaters (14%). The Transition Phase exhibited characteristics of earthquake families between the Intrusion Phase and Eruptive Phase. We conclude that the time history of earthquake families provides insight into the evolution of the stress distribution in the volcanic edifice, the development of the volcanic conduit, and seismogenesis of magma effusion. Finally, we discuss the role that repeating earthquakes could play in real-time monitoring at restless volcanoes. Our work suggests eruption forecasts can be improved by incorporating automatic processing codes to assist seismologists during sustained periods of high earthquake rates, even at sparsely monitored volcanoes.