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Seismic activity in the Northern Canadian Cordillera is characterized by diffuse earthquakes that extend hundreds of km northwest from the Yakutat collision zone. We use 25 months of broadband seismic data from Mackenzie Mountain Earthscope Project (MMEP), USArray Transportable Array (TA), and permanent Canadian National Seismic Network stations to present a local earthquake catalog with high sensitivity to small regional events. Deep learning techniques are adopted for both seismic phase detection and association. Event relocations are performed to provide well constrained estimates of earthquake depth distributions. Clusters of seismicity spanning the upper crust are located in the central Richardson Mountains, along the Tintina fault, and in the northeast Selwyn Basin. These clusters suggest that the core of the Richardson Anticlinorium is tectonically active and that the Tintina fault is a locus for low levels of active deformation. We interpret seismicity in the northeast Selwyn Basin as primarily occurring in the hanging wall of the Plateau thrust fault and suggest that some combination of localized duplex structures and lithological strength contrasts both within the Selwyn Basin and between abutting Paleozoic shelf sequences may be responsible for seismicity in the Mackenzie Mountain foreland. Plain Language Summary: The Northern Canadian Cordillera is situated inboard of a transition from a right‐lateral tectonic regime in coastal southeast Alaska to a subduction setting beneath southern Alaska. It presents a unique case study to examine the length scales over which tectonic deformation is able to permeate into a continental interior. In this study we use data from a relatively dense regional network of seismometers in order to present the most spatially complete catalog of earthquakes in the Northern Canadian Cordillera to date. We use machine learning techniques for the detection and classification of small magnitude earthquakes and use our results to analyze regional tectonics. Active faults that host earthquakes are identified and we use the earthquake depth distributions to further distinguish between mechanisms for regional stress transfer. We find evidence that the Tintina fault is presently active along a confined area at a low rate. We also find that low magnitude earthquakes do not generally occur within the Mackenzie Mountain Fold‐Thrust belt but are prevalent within the foreland Selwyn Basin. We also detect a pattern of relatively deep (∼30 km) earthquakes in the northern Richardson Mountains. Key Points: Small magnitude earthquakes extending to 25 km depth are detected throughout the Selwyn Basin in the Mackenzie Mountain forelandThe Tintina fault hosts a range of low magnitude earthquakes in the uppermost 15 km of crust within a 400 km zone along strikeDeformation in the Richardson Mountains is localized onto a narrow band of faults centered on the Richardson Anticlinorium [ABSTRACT FROM AUTHOR]

Earthquakes are one of nature's most devastating disasters. Earthquake prediction is critical in seismology since its success can save lives, property, and infrastructure. Numerous technologies have been proposed to address this issue, including mathematical analysis, artificial intelligence, and machine learning algorithms. Unfortunately, due to earthquakes' dynamic and spontaneous nature, they frequently fail to provide positive results. The study uses deep learning techniques to predict the magnitude of an impending earthquake using eight mathematically calculated seismic indicators derived from Japan, Indonesia, and the Hindu-Kush Karakoram Himalayan (HKKH) region's earthquake catalogs. Three deep learning techniques, including Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM), and self-attention-based transformer, have been implemented to model the associations between calculated seismic indicators and potential earthquake incidents. These models have been evaluated with well-known matrices such as the Mean Absolute Error (MAE), Mean Squared Error (MSE), log-cosh loss, and Mean Squared Logarithmic Error (MSLE). The value of these cost functions converges to a small number for all models, indicating that these models effectively predict earthquake magnitudes. When these models were fed with an unknown test dataset from Japan, the LSTM model performed best with the least deviation metrics (MAE = 0.060, MSE = 0.006, log cosh = 0.042 and MSLE = 0.003). Similarly, the Bi-LSTM model delivered the ideal result for the Indonesia earthquake catalog (MAE = 0.073, MSE = 0.009, log cosh = 0.016, and MSLE = 0.009), while the transformer model produced the optimal result for the HKKH region (MAE = 0.062, MSE = 0.006, log cosh = 0.043, and MSLE = 0.003). Predicting earthquake magnitude at various locations using these methodologies produces significant and positive results for magnitudes ranging from 3.5 M to 6.0 M, paving the way for the ultimate robust prediction mechanism, that has not yet been developed. [ABSTRACT FROM AUTHOR]

With fiber-optic seismic acquisition development, continuous dense seismic monitoring is becoming increasingly more accessible. Repurposing fiber cables in telecommunication conduits makes it possible to run seismic studies at low cost, even in locations where traditional seismometers are not easily installed, such as urban areas. However, due to the large volume of continuous streaming data, data collected in such a manner will go to waste unless we significantly automate the processing workflow. We train a convolutional neural network (CNN) for earthquake detection using 3000 events from a publicly available catalog and data acquired over three years by fiber cables in telecommunication conduits under the Stanford University campus. We performed a hyperparameter search both on the network architecture itself (e.g., number of layers, number of parameters) and on its training parameters, showing that CNNs with a small number of layers are sufficient for performing this detection task with high accuracy. We introduce a novel method for combining the deep learning results on fiber-optic and seismometer data to improve detection accuracy, dramatically reducing the false detection rate that is often a problem when processing large time-scale noisy continuous data. Consequently, we demonstrate that enhancing two sparse seismometer stations with an urban fiber system allows for the reliable detection of small earthquakes despite a low signal-to-noise ratio. We scale this processing method over three years of continuous data and show that this system reliably detects local small-amplitude earthquakes down to magnitudes as low as 0.5, leading to the discovery of previously uncataloged events. • We use fiber-optic cables in telecom conduits to record continuous seismic data. • We train a neural network to detect small local earthquakes with 93% accuracy. • We combine fiber-optic and seismometer data to further improve detection accuracy. • This is an extensive urban fiber seismic study with 3 years of continuous data. • Using our method, we detect small local earthquakes that were not cataloged before. [ABSTRACT FROM AUTHOR]