Abstract: Application of nonlinear resonance interferometry is introduced as a new geophysical approach to improve predictability in characterization of subsurface microseismic event analysis and propagation of fracture. In contrast to reflection methods that remove random information noise, nonlinear resonance interferometry exploits the full microseismic acquisition spectrum. In some examples, systems and techniques implement novel computational interactions between acquired microseismic wavefield attributes and a nonlinear system in software to amplify distortions in microseismic noise and exploits injection of synthetic noise, in software format, to fracture events.

Abstract: Incoming data from, for example, an array of detectors, may be received. A dynamical system may be initialized corresponding to a modality of the incoming data so that a measurement probe based on the initialized dynamical system may be generated. Such a measurement probe may be injected into a quantum mechanical system so that it may be determined whether the injection of the measurement probe into the quantum mechanical system results in a collapse of the quantum mechanical system. Thereafter, it may be determined that a signal is present within the incoming data if the quantum mechanical system collapses. Related methods, apparatuses, systems, and computer-program products are also described.

Abstract: Application of nonlinear resonance interferometry is introduced as a new geophysical approach to improve predictability in characterization of subsurface microseismic event analysis and propagation of fracture. In contrast to reflection methods that remove random information noise, nonlinear resonance interferometry exploits the full microseismic acquisition spectrum. In some examples, systems and techniques implement novel computational interactions between acquired microseismic wavefield attributes and a nonlinear system in software to amplify distortions in microseismic noise and exploits injection of synthetic noise, in software format, to fracture events.

Abstract: Application of nonlinear resonance interferometry is introduced as a new geophysical approach to improve predictability in characterization of subsurface porosity, rock and fluid properties. In contrast to reflection methods that remove random information noise, nonlinear resonance interferometry exploits the full seismic acquisition spectrum to assess how low frequency and high-frequency noise is differentially and directly modulated by varying levels of porosity and hydrocarbon content in the lithologies of interest. In some examples, systems and techniques implement novel computational interactions between acquired seismic wavefield attributes and a nonlinear system in software to amplify distortions in seismic noise and exploits injection of synthetic noise, in software format, to detect hydrocarbon traps and lithology changes at spatial scales below seismic resolution, thereby increasing the information value of low-resolution data.

Abstract: Incoming data from, for example, an array of detectors, may be received. A dynamical system may be initialized corresponding to a modality of the incoming data so that a measurement probe based on the initialized dynamical system may be generated. Such a measurement probe may be injected into a quantum mechanical system so that it may be determined whether the injection of the measurement probe into the quantum mechanical system results in a collapse of the quantum mechanical system. Thereafter, it may be determined that a signal is present within the incoming data if the quantum mechanical system collapses. Related methods, apparatuses, systems, and computer-program products are also described.

Abstract: Systems and apparatus related to quantum resonance interferometry for detecting signals are described. A first signal is interferometrically coupled with a first quantum mechanical function to generate a first tunneling rate; a second signal is interferometrically coupled with a second quantum mechanical function to generate a second tunneling rate; the first tunneling rate and the second tunneling rate are interferometrically coupled to generate a third tunneling rate; and upon determining that the third tunneling rate is greater than a threshold, the second signal is identified as corresponding to the first signal.

Abstract: Application of nonlinear resonance interferometry is introduced as a new geophysical approach to improve predictability in characterization of subsurface microseismic event analysis and propagation of fracture. In contrast to reflection methods that remove random information noise, nonlinear resonance interferometry exploits the full microseismic acquisition spectrum. In some examples, systems and techniques implement novel computational interactions between acquired microseismic wavefield attributes and a nonlinear system in software to amplify distortions in microseismic noise and exploits injection of synthetic noise, in software format, to fracture events.

Abstract: Application of nonlinear resonance interferometry is introduced as a new geophysical approach to improve predictability in characterization of subsurface porosity, rock and fluid properties. In contrast to reflection methods that remove random information noise, nonlinear resonance interferometry exploits the full seismic acquisition spectrum to assess how low frequency and high-frequency noise is differentially and directly modulated by varying levels of porosity and hydrocarbon content in the lithologies of interest. In some examples, systems and techniques implement novel computational interactions between acquired seismic wavefield attributes and a nonlinear system in software to amplify distortions in seismic noise and exploits injection of synthetic noise, in software format, to detect hydrocarbon traps and lithology changes at spatial scales below seismic resolution, thereby increasing the information value of low-resolution data.

Abstract: A computer-implemented method for signal analysis includes receiving a first signal, receiving a second signal, coupling the first signal with a first function generated from a first quantum mechanical system to generate a first tunneling rate, coupling the second signal with a second function generated from a second quantum mechanical system to generate a second tunneling rate, coupling the first tunneling rate with a third function generated from a third quantum mechanical system, coupling the second tunneling rate with the third function, obtaining a third tunneling rate, and upon determining that the third tunneling rate is greater than a threshold, identifying that the second signal corresponds to the first signal.

Abstract: Systems and apparatus related to quantum resonance interferometry for detecting signals are described. First and second signals are received and coupled with a first function and a second function, respectively, generated from a first quantum mechanical system and a second quantum mechanical system, respectively. By doing so, a first tunneling rate and a second tunneling rate are generated. The first tunneling rate is coupled with a third function generated from a third quantum mechanical system. The second tunneling rate is coupled with the third function to obtain a third tunneling rate. Upon determining that the third tunneling rate is greater than a threshold, it is identified that the second signal corresponds to the first signal.

Abstract: The current invention discloses a novel spectral transformation technique for characterizing digitized intensity output patterns from microarrays. This method yields improved sensitivity with reduced false positives and false negatives. Current microarray methods are overly sensitive to the detection of a visible distinction between pixels associated with probes and pixels associated with background. In one embodiment, a technique is disclosed that comprises the steps of: extracting pixels associated with an object of interest and transforming such pixels from an intensity representation to a spectral representation. In some embodiments, the extraction is based on a tessellated logarithmic spiral extraction that may yield a pixel core with a sampling of both foreground and background pixels. This core may then be computationally rescaled by 10×-10,000× to enhance spatial resolution.