The present disclosure includes a method for combining controlled and uncontrolled seismic data. The method includes accessing one or more controlled signals, each controlled signal associated with a respective receiver of a plurality of receivers. The method also includes accessing one or more uncontrolled signals, each uncontrolled signal associated with a respective receiver of the plurality of receivers. The method also includes generating one or more reconstructed signals based on the one or more uncontrolled signals. The method also includes generating a composite image based at least on the one or more controlled signals and the one or more reconstructed signals. The present disclosure may also include associated systems and apparatuses.

The present invention discloses a sensing-acquisition-wireless transmission integrated microseismic monitoring system, comprising a sensing unit, wherein the system further comprises an acquisition-wireless transmission unit. The acquisition-wireless transmission unit comprises a flameproof enclosure, an acquisition instrument, a battery, a wireless transmitter and a transmitting antenna. A push nut is arranged at an open end of the flameproof enclosure. A support stage is sheathed on an outer wall of the flameproof enclosure. A connection ring is movably sheathed on the open end of the flameproof enclosure. The push nut is connected to the connection ring. Multiple inner wing elastic plates are circumferentially arranged on the connection ring. The inner wing elastic plates are connected to corresponding expandable plate outer wings, respectively. The present invention further discloses a sensing-acquisition-wireless transmission integrated microseismic monitoring method.

A method may include obtaining an initial velocity model regarding a subterranean formation of interest. The method may further include generating various seismic migration gathers with different cross-correlation lag values based on a migration-velocity analysis and the initial velocity model. The method may further include selecting a predetermined cross-correlation lag value automatically using the seismic migration gathers and based on a predetermined criterion. The method may further include determining various velocity boundaries within the initial velocity model using a trained model, wherein the trained model is trained by human-picked boundary data and augmented boundary data. The method may further include updating, by the computer processor, the initial velocity model using the velocity boundaries, the automatically-selected cross-correlation lag value, and the migration-velocity analysis to produce an updated velocity model. The method may further include generating an image of the subterranean formation of interest using the updated velocity model.

Machine-learning methods and apparatus are disclosed to determine frictional state or other parameters in an earthquake zone or other failing medium, using acoustic emission, seismic waves, or other detectable indicators of microscopic processes. Predictions of future failures are demonstrated in different regimes. A classifier is trained using time series of acoustic emission data along with historic data of frictional state or failure events. In disclosed examples, random forests and gradient boost trees are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to testing or live data in order to assess a frictional state, assess seismic hazard, or make predictions regarding a future failure event. The technology has been developed in a double direct shear apparatus, but can be widely applied to seismic faults, other terrestrial failures, or failures in man-made structures. Variations are disclosed.

A method can include receiving microseismic data of microseismic events as acquired by sensors during hydraulic fracturing of a geologic region; jointly calibrating sensor orientation of the sensors and a velocity model of the geologic region via an objective function and the microseismic data; and, based at least in part on the jointly calibrating, determining one or more locations of the one or more microseismic events.

Sound signal when a wave generated by a geo-acoustic event source reaches any monitoring point (S1), constructing a theoretical propagation difference model and an observed propagation difference model of the waveform characterization quantity between monitoring points, to calculate a waveform characterization quantity difference value between two monitoring points (S2); and constructing an objective function based on the theoretical propagation difference model and the observed propagation difference model, and obtaining the location of the geo-acoustic event by means of inversion based on the objective function (S3). According to the geo-acoustic event location method, the arrival time, time domain parameters, spectral information, and waveform shape of the geo-acoustic signal when the wave generated by the geo-acoustic event source reaches any monitoring point are considered, then the non-uniformity of a propagation medium is comprehensively reflected, and the inversion precision of geo-acoustic event location is finally improved.

A stereoscopic identification method and apparatus for disturbance stress evolution process of underground cave surrounding rock perform a numerical inverse analysis of three-dimensional stereoscopic disturbance stress of a surrounding rock of the target underground cave on the basis of measurement values of three-component microseismometers and disturbance stress gauges. During the analysis, the overall prediction values are corrected from point to volume based on the measurement values to achieve a high-precision identification effect of the evolution process of the stereoscopic disturbance stress of the surrounding rock.

A fracturing system includes an energy storage having a battery and a switch, a switch cabinet, a plurality of transformers, a plurality of rectifiers, and a plurality of inverters respectively corresponding to and connected with a plurality of fracturing apparatuses. The switch cabinet is connected with the plurality of transformers. The plurality of transformers are respectively connected with the plurality of rectifiers. Each of the plurality of rectifiers is directly connected with a DC bus and the energy storage. The DC bus is directed connected to the plurality of inverters. The energy storage is directly electrically connected with the DC bus or each of the plurality of inverters. The energy storage is configured to power the plurality of fracturing apparatuses.

The invention disclosed provides a network of master controller and node arrays which all communicate with a system server, a client device and an administrator device. Each of the master controller and node arrays is equipped with an ERT system, and various seismic sensors to monitor a geographic perimeter for surface and sub-surface trigger events. Upon detection of a trigger event, each of the master controller and node arrays executes a sensor monitoring routine to determine the approximate path of travel, velocity, acceleration of the trigger event. The master controller and node arrays further conduct an ERT survey to determine the presence of anomalies which may indicate sub-surface activity related to the trigger event.

According certain aspects, embodiments of the invention consider the problem of microseismic event localization from a parameter estimation perspective, and include a method and system for computing and displaying characteristics of event localization errors. According to certain other aspects, embodiments of the invention include techniques for deriving aggregate statistics from a set of event location estimates, including methods for computing and displaying the probability that an event occurred in any given volume, and methods for describing and displaying the smallest volume that contains a specified percentage of the event probability or expected to contain the specified percentage of the events.