Abstract: The invention describes a tsunami warning system utilizing properties of light in optical fibers within submarine telecommunication cables. Enhanced by microwave frequency modulation and High-Loss Loop Back (HLLB) techniques, this system measures photoelastic changes for more stable, long-period tsunami wave detection.

Abstract: The present invention infers the first-motion polarities using the relative polarities measured from cross-correlations. We demonstrate two real-world applications on two DAS arrays in the manuscript to illustrate the effectiveness of our method and improvements to the focal mechanism inversion.

Abstract: A computer-implemented method useful for performing seismic tomography, including obtaining seismic data comprising distributed acoustic sensing (DAS) data, comprising at least one of P-wave or S-wave travel times from a plurality of seismic events to a plurality of recording stations coupled to a network of optical fibers in a geological structure; and generating tomography data from the DAS data, wherein tomography data is useful for characterizing the geological structure. We employ distributed acoustic sensing data recorded along a 100-km fiber-optic cable traversing the caldera to image its subsurface structure. Our images highlight a definite separation between the shallow hydrothermal system and the large magma chamber located at ˜12 km depth. The combination of the geological evidence with our results shows how fluids exsolved through second boiling provide the source of the observed uplift and seismicity.

Abstract: A computer-implemented method and system provide the ability to detect an earthquake. Distributed acoustic sensing (DAS) data is obtained. A deep neural network model that picks seismic phase arrival times on the DAS data is acquired. A semi-supervised learning approach is utilized to train the deep neural network model. The semi-supervised learning approach utilizes existing labels from a defined seismic dataset to generate pseudo labels on the DAS data. An earthquake is detected by applying the trained deep neural network model to new DAS data.

Abstract: A sensor system including an optical fiber comprising a first spatial channel and a second spatial channel, wherein the spatial channels are coupled and electromagnetic radiation propagates with different group velocities in each of the spatial channels. The sensor system further includes a source inputting the electromagnetic radiation into the first spatial channel at an input; and a detection system coupled to an output of the fiber, the detection system including a detector detecting transmission of the electromagnetic radiation coupled into the second spatial channel at different positions along the fiber between the input to the output, and generating a signal in response thereto; and a computer processing the signal to obtain a localized measurement of a physical parameter at one or more of the different positions along the fiber.