Disclosed are systems and methods for processing broadband acoustic signals acquired by a plurality of acoustic sensors, using an array-signal-processing technique to compute fused-signal maps in the frequency domain for a plurality of frequency bins. In accordance with various embodiments, the fused-signal maps are combined across the frequency bins, with respective weightings that are based on eigenvalues of covariance matrices computed for the plurality of frequency bins. The combined maps can be used to locate an acoustic source in a wellbore.

Methods and apparatus are described for using ambient acoustic energy as a passive source in marine seismic surveys. An example embodiment includes (a) accessing signals that were recorded by sensors in the presence of acoustic energy that was emitted by a passive source during a marine seismic survey; (b) identifying a point to represent a location from which the acoustic energy was emitted; (c) isolating, from the recorded signals, a direct wavefield arriving at the sensors from a direction of the identified point; and (d) generating an estimated passive source wavefield at the identified point by backpropagating the isolated direct wavefield to the identified point. The estimated passive source wavefield may be used, together with signals recorded by the sensors, to generate an image of a subsurface earth volume without the use of active seismic sources.

Deployable smart acoustic resonance particles have dense cores, compliant matrixes surrounding the cores and stiff outer shells surrounding the matrixes. The particles have mechanical stress sensitivities that provide unique band gap shifts when compressed. Groups of similar particles with similar stress sensitivities and similar band gap shifts are added at different times to hydraulic fluids, as circulated through wells with the fluid and pushed into fractures. A plural, sonic monopole well logging tool is lowered into the well to determine locations and depth of fractures and local pressures by distinct resonance of individual groups.

An automatic microseismic monitoring-intelligent rockburst early warning integrated method is further provided.

A system is provided. The system includes a plurality of seismic sensors and a computer device. The computer device is programmed to a) store a plurality of distances between each of the plurality of seismic sensors; b) store one or more fingerprints of a signal to be detected; c) receive a first signal transmitted from a first seismic sensor of the plurality of seismic sensors; d) receive the first signal transmitted from a second seismic sensor of the plurality of seismic sensors; e) compare the first signal to the one or more fingerprints of the signal to be detected; and f) determine a direction of travel of the first signal based on the distance between the first seismic sensor and the second seismic sensor, the first time, and the second time.

Embodiments of the present disclosure provide a method and system for recognizing a mine microseismic event, and belong to the field of mine data processing. The method includes: converting historical microseismic data monitored by a mine microseismic monitoring system into a microseismic waveform image, and then, converting the microseismic waveform image into a four-neighborhood microseismic waveform graph structure; performing area defining on the microseismic waveform graph structure, and extracting a similar feature layer of any node in the microseismic waveform graph structure based on the defined area; and taking the microseismic waveform image as an input layer of an improved convolutional neural network model, and sequentially connecting the input layer with the similar feature layer as well as a convolutional layer, a pooling layer, a fully connected layer and an output layer which are pre-configured for the improved convolutional neural network model to form a recognition model for recognizing the mine microseismic event. By using the recognition model designed in the present disclosure, the similar feature layer can be extracted, so that the mine microseismic event is effectively recognized.

A method for monitoring hydraulic fracturing range of a surface vertical shaft is provided by the present disclosure, belonging to the technical field of ultrahigh-pressure hydraulic fracturing monitoring of the coal mine vertical shafts. The method comprises the following steps: connecting, by an eight-thread communication cable, a high-precision portable micro-seismic monitoring acquisition instrument to a high-sensitivity deep hole sensor, and performing uphole-crosshole-downhole monitoring simultaneously, specifically as follows: providing uphole-crosshole-downhole monitoring holes respectively, and installing deep hole geophones in the monitoring holes; then laying communication cables uphole-crosshole-downhole to connect the geophones to the portable high-precision micro-seismic acquisition instrument respectively; then performing high-precision positioning on the fissure development range by monitoring recorded events and time, thus determining the directions and ranges of a main fracture and secondary induced fractures of hydraulic fractures.

A method for identifying a seismic event includes extracting a portion of a plurality of seismic data signals based on energy levels in the plurality, comparing the extracted portion to a known pattern and determining a correlation, and identifying the seismic event based on the correlation. A computer program product and devices for implementing the method are provided.

A buried hydrophone may be configured to identify real-time changes in pipeline acoustics and vibrations during a flood event. Monitoring vibrations may allow predicting the likelihood of pipeline exposure and suspension.

The present disclosure includes a method including determining a spatial region for analysis and selecting a segment of time for analysis, analyzing and correcting a plurality of traces from a plurality of receivers using an iterative non-linear inversion algorithm, wherein each iteration of the non-linear algorithm corrects the plurality of traces using at least one set of parameters defining a microseismic event, determining whether a final stack value of the plurality of traces corrected based on the at least one set of parameters of a final iteration of the iterative non-linear inversion algorithm exceeds a predetermined threshold and upon a determination that the final stack value exceeds the predetermined threshold, detecting a microseismic event defined by the at least one set of parameters of final iteration. The present disclosure also includes associated systems and computer-readable media.