ISRM Suggested Method for Laboratory Acoustic Emission Monitoring

Acoustic emission (AE) is defined as high-frequency elastic waves emitted from defects such as small cracks (microcracks) within a material when stressed, typically in the laboratory. AE is a similar phenomenon to microseismicity (MS), as MS is induced by fracture of rock at an engineering scale (e.g., rockbursts in mines), that is, in the field. Thus, seismic monitoring can be applied to a wide variety of rock engineering problems, and AE is a powerful method to investigate processes of rock fracture by detecting microcracks prior to macroscopic failure and by tracking crack propagation. A basic approach involves using a single channel of data acquisition, such as with a digital oscilloscope, and analyzing the number and rate of AE events. Perhaps the most valuable information from AE is the source location, which requires recording the waveform at several sensors and determining arrival times at each. Thus, investing in a multichannel data acquisition system provides the means to monitor dynamics of the fracturing process. The purpose of this suggested method is to describe the experimental setup and devices used to monitor AE in laboratory testing of rock. The instrumentation includes the AE sensor, preamplifier, frequency (noise) filter, main amplifier, AE rate counter, and A/D (analog-to-digital) recorder, to provide fundamental knowledge on material and specimen behavior in laboratory experiments. When considering in situ seismic monitoring, the reader is referred to the relevant ISRM suggested method specifically addressing that topic (Xiao et al. 2016)

Mycobacteria Attenuate Nociceptive Responses by Formyl Peptide Receptor Triggered Opioid Peptide Release from Neutrophils

In inflammation, pain is regulated by a balance of pro- and analgesic mediators. Analgesic mediators include opioid peptides which are secreted by neutrophils at the site of inflammation, leading to activation of opioid receptors on peripheral sensory neurons. In humans, local opioids and opioid peptides significantly downregulate postoperative as well as arthritic pain. In rats, inflammatory pain is induced by intraplantar injection of heat inactivated Mycobacterium butyricum, a component of complete Freund's adjuvant. We hypothesized that mycobacterially derived formyl peptide receptor (FPR) and/or toll like receptor (TLR) agonists could activate neutrophils, leading to opioid peptide release and inhibition of inflammatory pain. In complete Freund's adjuvant-induced inflammation, thermal and mechanical nociceptive thresholds of the paw were quantified (Hargreaves and Randall-Selitto methods, respectively). Withdrawal time to heat was decreased following systemic neutrophil depletion as well as local injection of opioid receptor antagonists or anti-opioid peptide (i.e. Met-enkephalin, β-endorphin) antibodies indicating an increase in pain. In vitro, opioid peptide release from human and rat neutrophils was measured by radioimmunoassay. Met-enkephalin release was triggered by Mycobacterium butyricum and formyl peptides but not by TLR-2 or TLR-4 agonists. Mycobacterium butyricum induced a rise in intracellular calcium as determined by FURA loading and calcium imaging. Opioid peptide release was blocked by intracellular calcium chelation as well as phosphoinositol-3-kinase inhibition. The FPR antagonists Boc-FLFLF and cyclosporine H reduced opioid peptide release in vitro and increased inflammatory pain in vivo while TLR 2/4 did not appear to be involved. In summary, mycobacteria activate FPR on neutrophils, resulting in tonic secretion of opioid peptides from neutrophils and in a decrease in inflammatory pain. Future therapeutic strategies may aim at selective FPR agonists to boost endogenous analgesia

A Machine Learning Approach for Locating Acoustic Emission

This paper reports on the feasibility of locating microcracks using multiple-sensor measurements of the acoustic emissions (AEs) generated by crack inception and propagation. Microcrack localization has obvious application in non-destructive structural health monitoring. Experimental data was obtained by inducing the cracks in rock specimens during a surface instability test, which simulates failure near a free surface such as a tunnel wall. Results are presented on the pair-wise event correlation of the AE waveforms, and these characteristics are used for hierarchical clustering of AEs. By averaging the AE events within each cluster, "super" AEs with higher signal to noise ratio (SNR) are obtained and used in the second step of the analysis for calculating the time of arrival information for localization. Several feature extraction methods, including wavelet packets, autoregressive (AR) parameters, and discrete Fourier transform coefficients, were employed and compared to identify crucial patterns related to P-waves in time and frequency domains. By using the extracted features, an SVM classifier fused with probabilistic output is used to recognize the P-wave arrivals in the presence of noise. Results show that the approach has the capability of identifying the location of AE in noisy environments.</p