Search

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

One of the largest induced seismic events attributed to hydraulic-fracturing operations, with a moment magnitude (M w) of 4.1, occurred on 12 January 2016 in Alberta, Canada. The hydraulic-fracturing treatment was monitored using a dense array of 93 shallow wellbores, each containing three geophones, producing a high-resolution data set that is presented here as a case study. Over 9700 smaller events were observed both before and after the event, with a magnitude of completeness of approximately M w −1.9. The use of perforation shots with known locations enabled refinement of the velocity model, such that event hypocentres could be located with high accuracy and precision. Most of the hypocentres of induced events were located within the sedimentary section, 100s of metres above the treatment zone. Event locations of the foreshocks and aftershocks reveal fault activation along both small and large structures. The mechanisms and locations suggest a complex north–south oriented strike-slip fault system that we interpret to be part of a regional flower structure. The b -value for the data set is close to unity, as expected for fault activation events; however, the three largest magnitude events deviate significantly from the fit, suggesting that it would be difficult to forecast these larger events based on the Gutenberg–Richter relation. This has significant implications for the mitigation of hydraulic-fracturing induced seismicity. The three largest events appear to be located on fault segments that are less optimally oriented for slip than those imaged by other event clusters, suggesting that larger events require a larger perturbation to the stress state in order to nucleate. Monitoring setups with the capability of the system presented here, if operated in real time, could be of great value to discern fault activation due to seismicity aligning on planar structures prior to a main shock. This approach may have considerable potential to improve short-term forecasts of induced seismicity. [ABSTRACT FROM AUTHOR]