A method of determining the presence and/or extent of an event comprises determining a plurality of temperature features from a temperature sensing signal, determining one or more frequency domain features from an acoustic signal, and using at least one temperature feature of the plurality of temperature features and at least one frequency domain feature of the one or more frequency domain features to determine the presence and/or extent of the event at one or more locations.

Systems and methods for surveying a seafloor utilize two or more of seismic data, acoustic data and electrical potential or resistivity data to identify the locations of objects on or beneath the seafloor. The methods involve moving survey equipment over a geographic area of the seafloor and conducting a plurality of sensing or detecting operations while moving the survey equipment over the geographic area. The plurality of operations include two or more of: (1) a seismic operation that emits seismic energy toward the seafloor and collects seismic data based on seismic energy that returns from the seafloor, (2) an acoustic operation that emits acoustic energy toward the seafloor and collects acoustic data based on acoustic energy that returns from the seafloor, and/or (3) an electrical operation that supplies electrical power into seawater and that collects electric potential data indicative of electric potential that is induced into the seawater.

A neural network is utilized to improve the resolution of subsurface inversion. The neural network leverages posterior distribution of samples and adds high frequency components to the inversion by utilizing the data in both the time domain and the frequency domain. The improved resolution of the subsurface inversion enables more accurate prediction of subsurface characteristics (e.g., reservoir architecture).

A symmetry attribute is described that may be used for determining seismic stratigraphic features in a formation. In one example, seismic input data from a formation is processed to determine an attribute by selecting a center trace, assigning a first cluster of the traces to a left image and a second cluster of the traces to a right image, and determining symmetry about the center trace between the left and the right images.

The present invention is a method to determine an azimuthal orientation of a borehole seismometer sensor performed by a computing device using a control server having a database and an arithmetic function, the computing device performing a method to determine the azimuthal orientation of a borehole seismometer sensor using long-period surface waves in microseisms, including step S100 in which a data collection unit 100 collects continuous waveform data recorded by a borehole seismometer and a reference seismometer; step S200 in which a frequency band setting unit 200 sets a frequency band to be analyzed in the collected continuous waveform data; step S300 in which a filtering unit 300 performs bandpass filtering on the frequency band to be analyzed; step S400 in which a waveform dividing unit 400 divides seismic waveform into waveform segments with preset time units; step S500 in which a phase shift unit 500 shifts the phase of the divided vertical component waveforms by 90°; step S600 in which a waveform calculation unit 600 combines the divided N′ and E′ component seismic waveforms to calculate horizontal components for rotation angles waveform between 0 and 360° from the N′ orientation; step S700 in which a correlation calculation unit 700 calculates a correlation coefficient between the horizontal and vertical component waveforms; step S800 in which a Rayleigh wave orientation determination unit 800 repeats steps S500 to S700 for each divided time domain; step S900 in which an orientation comparison unit 900 performs steps S400 to S800, respectively, with respect to the borehole seismometer data for which the sensor orientation is to be determined and the reference seismometer data for which the sensor orientation is already known; and step S1000 in which a result calculation unit 1000 averages 0 determined for each time period to calculate a final result.

A method is described for seismic amplitude analysis including receiving a seismic dataset representative of a subsurface volume of interest wherein the seismic dataset includes an angle or angle stack dimension; select a plurality of sets of sub-cubes in the seismic dataset wherein each set of sub-cubes includes a plurality of the angles or the angle stacks; compute standard score statistics for each of the plurality of sub-cubes; identify amplitude variation with angle (AVA) anomalies based on the standard score statistics for each of the set of sub-cubes; classify the AVA anomalies to generate classified AVA anomalies; and displaying, on a user interface, the classified AVA anomalies as a graphical display. The method is executed by a computer system.

A method of seismic acquisition using a dispersed-source array (DSA) comprising two or more sources. The method comprises determining, for each of the two or more sources of the DSA, an individual spectrally-banded waveform. For each of the two or more sources, a primary waveform is formed by repeating the individual spectrally-banded waveform. For each of the two or more sources, a secondary waveform is formed based on the primary waveform. The secondary waveform is spectrally shifted relative to the primary waveform such that secondary waveforms of any two of the two or more sources are spectrally non-overlapping. The blending operator based on the secondary waveform of each of the two or more sources is provided to the DSA. The method also includes performing deblended-data reconstruction of acquired seismic data using one or more properties of the blending operators of the two or more sources.

A method of analysing seismic data to detect possible hydrocarbons includes determining a set of data tiles from a seismic data cube of seismic data and testing each data tile in the set of data tiles to determine whether it corresponds to a possible fluid contact.

Method for automated and quantitative assessment of multiple direct hydrocarbon indicators (“DHI's”) extracted from seismic data. DHI's are defined in a quantitative way (33), making possible a method of geophysical prospecting based on quantification of DHI anomalies. Instead of working in a particular spatial region of seismic data pre-defined as a hydrocarbon opportunity, the present invention works on entire data volumes derived from the measured seismic data (31), and identifies opportunities based on quantified DHI responses. In some embodiments, a series of algorithms utilizes the geophysical responses that cause DHI's to arise in seismic data to search entire data sets and identify hydrocarbon leads based on the presence of individual and/or combinations of DHI's (34).

A method is herein presented to statistically combine multiple seismic attributes for generating a map of the spatial density of fractures. According to an embodiment a first step involves interpreting the formation of interest in 3D seismic volume first to create its time structure map. The second step is creating depth structure of the formation of interest from its time structure map. In this application geostatistical methods have been used for depth conversional, although other methods could be used instead. The third step is extraction of a number of attributes, such as phase, frequency and amplitudes, from the time structure map. The next step is to project the fracture density onto the top of the target formation. The final step is to combine these attributes using a statistical method known as Multi-variant non-linear regression to predict fracture density.