Evaluating factors influencing infrasonic signal detection and automatic processing performance utilizing a regional network.

Physical and deployment factors that influence infrasound signal detection and assess automatic detection performance for a regional infrasound network of arrays in the Western U.S. are explored using signatures of ground truth (GT) explosions (yields). Despite these repeated known sources, published infrasound event bulletins contain few GT events. Arrays are primarily distributed toward the south-southeast and south-southwest at distances between 84 and 458 km of the source with one array offering azimuthal resolution toward the northeast. Events occurred throughout the spring, summer, and fall of 2012 with the majority occurring during the summer months. Depending upon the array, automatic detection, which utilizes the adaptive F-detector successfully, identifies between 14% and 80% of the GT events, whereas a subsequent analyst review increases successful detection to 24%-90%. Combined background noise quantification, atmospheric propagation analyses, and comparison of spectral amplitudes determine the mechanisms that contribute to missed detections across the network. This analysis provides an estimate of detector performance across the network, as well as a qualitative assessment of conditions that impact infrasound monitoring capabilities. The mechanisms that lead to missed detections at individual arrays contribute to network-level estimates of detection capabilities and provide a basis for deployment decisions for regional infrasound arrays in areas of interest.

Detection of regional infrasound signals using array data: Testing, tuning, and physical interpretation.

This work quantifies the physical characteristics of infrasound signal and noise, assesses their temporal variations, and determines the degree to which these effects can be predicted by time-varying atmospheric models to estimate array and network performance. An automated detector that accounts for both correlated and uncorrelated noise is applied to infrasound data from three seismo-acoustic arrays in South Korea (BRDAR, CHNAR, and KSGAR), cooperatively operated by Korea Institute of Geoscience and Mineral Resources (KIGAM) and Southern Methodist University (SMU). Arrays located on an island and near the coast have higher noise power, consistent with both higher wind speeds and seasonably variable ocean wave contributions. On the basis of the adaptive F-detector quantification of time variable environmental effects, the time-dependent scaling variable is shown to be dependent on both weather conditions and local site effects. Significant seasonal variations in infrasound detections including daily time of occurrence, detection numbers, and phase velocity/azimuth estimates are documented. These time-dependent effects are strongly correlated with atmospheric winds and temperatures and are predicted by available atmospheric specifications. This suggests that commonly available atmospheric specifications can be used to predict both station and network detection performance, and an appropriate forward model improves location capabilities as a function of time.

Tele-infrasonic studies of hard-rock mining explosions.

The Lac-du-Bonnet infrasound station, IS-10, and the Minnesota iron mines 390 km to the southeast are ideally located to assess the accuracy of atmospheric profiles needed for infrasound modeling. Infrasonic data from 2003 associated with explosions at the iron mine were analyzed for effects of explosion size and atmospheric conditions on observations with well-constrained ground truth. Noise was the determining factor for observation; high noise conditions sometimes prevented unequivocal identification of infrasound arrivals. Observed arrivals had frequencies of 0.5 to 5 Hz, with a dominant frequency of 2 Hz, and generally had durations on the order of 10 s or less. There was no correlation between explosive amount and observability. Tele-infrasonic propagation distances (greater than 250 km) produce thermospheric ray paths. Modeling is based upon MSIS/HWM (Mass Spectrometer Incoherent Scatter/Horizontal Wind Model) and NRL-G2S (Naval Research Laboratory Ground to Space) datasets. The NRL-G2S dataset provided more accurate travel time predictions that the MSIS/HWM dataset. PE modeling for the NRL-G2S dataset indicates energy loss at higher frequencies (around 4 Hz). Additionally, applying the Sutherland/Bass model through the NRL-G2S realization of the atmosphere in InfraMAP results in predicted amplitudes too small to be observed.

Causal factors for seismicity near Azle, Texas.

In November 2013, a series of earthquakes began along a mapped ancient fault system near Azle, Texas. Here we assess whether it is plausible that human activity caused these earthquakes. Analysis of both lake and groundwater variations near Azle shows that no significant stress changes were associated with the shallow water table before or during the earthquake sequence. In contrast, pore-pressure models demonstrate that a combination of brine production and wastewater injection near the fault generated subsurface pressures sufficient to induce earthquakes on near-critically stressed faults. On the basis of modelling results and the absence of historical earthquakes near Azle, brine production combined with wastewater disposal represent the most likely cause of recent seismicity near Azle. For assessing the earthquake cause, our research underscores the necessity of monitoring subsurface wastewater formation pressures and monitoring earthquakes having magnitudes of ∼M2 and greater. Currently, monitoring at these levels is not standard across Texas or the United States.