The devastating 2010 Haiti earthquake (M_w 7.0) was caused by rupture of the Léogâne, blind, thrust fault located 5 km north of the 1,200‐km‐long, left‐lateral, Enriquillo‐Plantain Garden fault zone (EPGFZ). Unexpectedly, the EPGFZ remained largely quiescent or slightly reactivated during the 2010 earthquake. Nevertheless, the EPGFZ still formed a major, crustal boundary between a coseismically uplifted lowland north of the EPGFZ and a subsided area in the highlands south of the fault. Here we use high‐resolution sonar data from two Haitian Lakes that straddle the EPGFZ to demonstrate the presence of a 10‐ to 15‐km‐wide, 120‐km‐long, late Holocene fold‐thrust belt which deforms clastic, lowland basins along the northern edge of the EPGFZ. In the eastern part of the study area, sonar results from Lake Azuey show that the linear trace of the EPGFZ cutting the Holocene lake bed is more deeply buried and less active than the adjacent, newly discovered, northwest striking, northeast dipping Jimani thrust fault that is part of the adjacent, transpressional belt of en echelon thrusts and folds. This structural relationship between a less active EPGFZ and more recently active, transpression‐related Jimani thrust is remarkably similar to the 2010 epicentral area 70 km to the west between the less active EPGFZ and seismogenic, northeast‐dipping, Léogâne thrust during the 2010 Haiti earthquake. In this complex transpressional zone, we propose that coseismic deformation alternates at recurrence intervals of centuries between oblique, transpression‐related structures (Léogâne, Jimani, and Trois Baies thrusts) and the main strike‐slip, plate boundary fault zone (EPGFZ).

3D printing techniques (additive manufacturing) using different materials and structures provide opportunities to understand porous or fractured materials and fluid effects on their elastic properties. We used a 3D printer (Stratasys Dimension SST 768) to print one “solid” cube model and another with penny-shaped inclusions. The 3D printing process builds materials, layer by layer, producing a slight “bedding” plane, somewhat similar to a sedimentary process. We used ultrasonic transducers (500 kHz) to measure the P- and S-wave velocities. The input printing material was thermoplastic with a density of $1.04\mathrm{g}/\mathrm{cc}$, P-wave velocity of $2167\mathrm{m}/\mathrm{s}$, and S-wave velocity of $885\mathrm{m}/\mathrm{s}$. The solid cube had a porosity of approximately 6% and a density of $0.98\mathrm{g}/\mathrm{cc}$. Its P-wave velocity was $1914\mathrm{m}/\mathrm{s}$ in the bedding direction and $1830\mathrm{m}/\mathrm{s}$ normal to bedding. We observed S-wave splitting with fast and slow velocities of 879 and $835\mathrm{m}/\mathrm{s}$, respectively. Quality factors for P- and S-waves were estimated using the spectral-ratio method with $Q_{\mathrm{P}}$ ranging from 15 to 17 and $Q_{\mathrm{S}}$ from 24 to 27. By introducing penny-shaped inclusions along the bedding direction in a 3D printed cube, we created a more porous volume with density of $0.79\mathrm{g}/\mathrm{cc}$ and porosity of 24%. The inclusions significantly decreased the P-wave velocity to 1706 and $1351\mathrm{m}/\mathrm{s}$ parallel and normal to the bedding plane. The fast and slow S-wave velocities also decreased to 812 and $656\mathrm{m}/\mathrm{s}$. A fluid substitution experiment, performed with water, increased (20%–46%) P-wave velocities and decreased (9%–10%) S-wave velocities. Theoretical predictions using Schoenberg’s linear-slip theory and Hudson’s penny-shaped theory were calculated, and we found that both theories matched the measurements closely (within 5%). The 3D printed material has interesting and definable properties and is an exciting new material for understanding wave propagation, rock properties, and fluid effects.

Rock salt (essentially halite) is a special type of sedimentary rock that has played a large role throughout tectonic and economic history. The unique physical properties of halite (ductility, low density, flowability, and impermeability) can be critical factors in hydrocarbon traps and underground storage. However, seismic imaging and interpretation can be challenging when salt structures are present due to their complex geometry and large impedance contrasts relative to surrounding rocks. To investigate the properties of rock salt in terms of elastic parameters, we use ultrasonic laboratory measurements and well logs. In the laboratory, we have analyzed the effects of composition, crystalline structure, pressure, and temperature on the elastic behavior of a variety of rock salt samples. The samples include pure halite (>95 wt%) from the Gulf of Mexico (GOM) area, argillaceous rock salt from the Zipaquirá Mine, Colombia, and crystalline salt from the Goderich Mine, Canada. Current measurements suggest that the GOM salt cores behave isotropically in general. The Zipaquirá salt samples show velocity and density variations on account of their heterogeneous composition. The Goderich halite crystals display distinct cubic anisotropy. Measurements on the GOM samples at varying confining pressures and temperatures indicate that increasing pressure elevates velocity whereas increasing temperature decreases velocity. From the analysis of 145 log suites from boreholes drilled through rock salt in the northern GOM, we found that, within the salt formations, P-wave velocities increased slightly with depth (approximately $5\mathrm{m}/\mathrm{s}$ per km). The S-wave velocities from three wells range from 2280 to $2580\mathrm{m}/\mathrm{s}$. Bulk densities from all the wells cluster at $2160\pm 30\mathrm{kg}/{\mathrm{m}}^3$. These laboratory and log measurements provide new values for the elastic properties of rock salt, which can assist in velocity model building, synthetic seismogram generation, and the understanding of the rock physics of halite.

Summary. Seismic migration and inversion are closely related processes for obtaining images of the subsurface. Both techniques attempt to infer petrophysical a

Subtle fault detection plays a vital role in reservoir development studies because faults may form baffles or conduits that significantly control how a petroleum reservoir is swept. Small-throw faults are often overlooked in interpreting seismic amplitude data. However, seismic attributes can aid in mapping small faults. Over the years, dozens of seismic attributes have been developed that offer additional features for interpreters with associated caveats. Using the Maui 3D seismic data acquired in the Offshore Taranaki Basin, New Zealand, we have generated seismic attributes that are typically useful for fault detection. We find that multiattribute analysis provides greater geologic information than would be obtained by the analysis of individual attribute volumes. We extract the geologic content of multiple attributes in two ways: interactive corendering of different seismic attributes and the unsupervised machine learning algorithm self-organizing maps (SOM). Corendering seismic attributes that are mathematically independent but geologically interrelated provides a well-integrated structural image. We suggest eight combinations of 16 various attributes useful for a human interpreter with interest in fault and fracture detection. Current interpretation display capabilities constrain corendering to only four attribute volumes. Therefore, we use principal component analysis and SOM techniques to efficiently integrate the geologic information contained within many attributes. This approach gathers the data into one classification volume based on the interrelationships between seismic attributes. We show that our resulting SOM classification volume better highlights small faults that are difficult to image using conventional seismic interpretation techniques. We find that SOM works best when a fault exhibits anomalous features for multiple attributes within the same voxel. However, human interpreters are more adept at recognizing spatial patterns within various attributes and can place them in an appropriate geologic context.

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.