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Cenozoic
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Pliocene
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Paleogene
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Dongying Formation (2)
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middle Eocene
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Mirador Formation (1)
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upper Eocene
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Oligocene
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Frio Formation (3)
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lower Oligocene
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Rupelian (1)
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upper Oligocene (1)
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Vicksburg Group (2)
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Paleocene
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lower Paleocene (1)
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upper Paleocene (2)
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Paleocene-Eocene Thermal Maximum (1)
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Wilcox Group (2)
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Shahejie Formation (2)
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-
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Mesozoic
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Cretaceous
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Colorado Group (1)
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Lower Cretaceous
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Albian (1)
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Mancos Shale (1)
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Upper Cretaceous
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Almond Formation (3)
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Cenomanian
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Coniacian (1)
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Ferron Sandstone Member (3)
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Gulfian
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Austin Chalk (2)
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Lewis Shale (2)
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Turonian (2)
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Wall Creek Member (1)
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Williams Fork Formation (2)
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Viking Formation (1)
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Franciscan Complex (1)
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Jurassic
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Heather Formation (1)
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Upper Jurassic
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-
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Nugget Sandstone (1)
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Triassic
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Lower Triassic (2)
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Upper Triassic
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Yanchang Formation (2)
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Vaca Muerta Formation (1)
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Paleozoic
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Arbuckle Group (3)
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Cambrian
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Rome Formation (1)
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Upper Cambrian
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Eau Claire Formation (1)
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-
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Carboniferous
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Mississippian
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Barnett Shale (6)
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Upper Mississippian
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Meramecian
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Sainte Genevieve Limestone (1)
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Namurian (1)
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Pennsylvanian
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Middle Pennsylvanian
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Atokan (2)
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Desmoinesian
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Hartshorne Sandstone (1)
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Morrow Formation (3)
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Red Fork Sandstone (2)
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Upper Carboniferous (2)
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Devonian
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Keg River Formation (1)
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Middle Devonian
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Mahantango Formation (1)
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Marcellus Shale (5)
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Onondaga Limestone (3)
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Slave Point Formation (1)
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Traverse Group (1)
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Upper Devonian
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Frasnian
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Leduc Formation (2)
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-
-
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Hunton Group (2)
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lower Paleozoic (2)
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Ordovician
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Lower Ordovician
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Ellenburger Group (5)
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Middle Ordovician
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Trenton Group (2)
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Upper Ordovician
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Yeoman Formation (1)
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Viola Limestone (2)
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Permian
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Ecca Group (1)
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Fort Riley Limestone (1)
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Guadalupian
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Brushy Canyon Formation (1)
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-
Khuff Formation (6)
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Lower Permian
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GeoRef Categories
Era and Period
Epoch and Age
Book Series
Date
Availability
amplitude
Prediction of reservoir properties using the RDQ crossplot workflow
Calibration of the Strain Amplitude Recorded with DAS Using a Strainmeter Array
Examining the Role of Elevated and Sustained Strain in Dynamically Triggering Earthquakes on the Anza Section of the San Jacinto Fault
Source Parameter Analysis Using Maximum Amplitudes in the Time Domain
NaNDC: Full Moment Tensor Inversion and Uncertainty Analysis for Large‐ N Monitored Small Earthquakes
Integration of FWI-derived reflectivity in seismic interpretation
A quantitative analysis of acoustic field directionality from a seismic airgun array
Modeling a pure qP wave in attenuative transverse isotropic media using a complex-valued fractional viscoacoustic wave equation
Experimental study on identifying anomaly azimuth based on acoustic vector array of borehole acoustic reflection imaging
Horizontal Transverse Isotropy Studied Using Tools for Full-Azimuth Seismic Data Processing and an Advanced Well Logging Complex on the Example of Famennian Deposits of the Yugomashevskoye Field
Usability of Community Seismic Network recordings for ground-motion modeling
Novel approach for energy-spectrum-based probabilistic seismic hazard analysis in regions with limited strong earthquake data
Response spectrum and modal dynamic analyses of gravity dams using ground motion accelerations modified to account for hydrodynamic effects
Ground-motions site and event specificity: Insights from assessing a suite of simulated ground motions in the San Francisco Bay Area
Maximize the value of DAS VSP using full wavefields
Detection of microseismic events in continuous DAS recordings using semantic image segmentation
The success of exploration and development in the oil and gas industry largely depends on the seismic method, which in turn is dependent on seismic interpretation. This success is because seismic data provide a detailed image of the subsurface, providing not only an image of the current reflector geometries, but through the geoscientist's understanding of geologic processes, of the geologic past. Whether analyzing migrated gathers or stacked traces, the processed seismic data are in the form of reflection amplitudes. Skilled interpreters identify the presence of hydrocarbons by recognizing anomalous high, low, or dim amplitudes. They identify lateral changes in thickness or lithology of thin layers by recognizing lateral changes in amplitude, frequency, and phase of the seismic wavelet. Interpreters also identify faults by recognizing abrupt lateral changes in wavelet alignment and/or reflector orientation, and they differentiate seismic noise by applying their understanding of seismic data acquisition and processing techniques. Not all interpreters are highly skilled in understanding the limitations of seismic acquisition and processing. Nor are all interpreters comfortable with how the seismic data respond to subtle changes in stratigraphy, to architectural elements of depositional systems, to structural components of tectonic deformation, to changes in reflection pattern due to diagenesis, or the amplitude response to hydrocarbons or CO 2 . Seismic attributes quantitively capture effects that a skilled interpreter sees in reflector amplitude, frequency, phase, bandwidth, continuity, orientation, and signal-to-noise ratio. These attributes are not magical; rather they quantitatively map predefined families of patterns seen in the seismic amplitude data. In 2024, seismic attributes and impedance inversion provide excellent images of subtle features in good-quality data that are easy to overlook, especially given the time constraints for interpretation projects. In contrast, attributes have limited value in the analysis of very noisy or poorly imaged data. Here, the human interpreter's ability in pattern recognition along with an understanding of geologic processes and artifacts in seismic data processing is superior to the fixed patterns programmed into today's attribute and impedance inversion algorithms. Seismic attributes extend the concepts of seismic geomorphology, enabling the mapping of structural and stratigraphic features. In combination with impedance, they also facilitate the recognition of seismic facies, and when calibrated with well data, they help predict lithology, porosity, and fluid content. The successful application of seismic attributes requires an understanding of not only their physical principles but also a grasp of seismic data quality and geologic processes. An interpreter may encounter hundreds of attributes ranging from RMS amplitude, spectral components, coherence, curvature, AVO, to impedance and attenuation. Many of these attributes are the same but masquerade with different names, while others are redundant, providing nearly identical results. Still others may prove to be of little practical value. The authors' aim is to provide clarity and organization to this overwhelming list of attributes and guide interpreters in selecting the most appropriate ones for specific objectives.