Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.

Propagating uplift controls on high-elevation, low-relief landscape formation in the southeast Tibetan Plateau

High-elevation, low-relief surfaces are widespread in many mountain belts. However, the origin of these surfaces has long been debated. In particular, the southeast Tibetan Plateau has extensive low-relief surfaces perched above deep valleys and in the headwaters of three of the world’s largest rivers (Salween, Mekong, and Yangtze Rivers). Various geologic data and geodynamic models show that many mountain belts grow first to a certain height and then laterally in an outward propagation sequence. By translating this information into a kinematic propagating uplift function in a landscape evolution model, we propose that the high-elevation, low-relief surfaces in the southeast Tibetan Plateau are simply a consequence of mountain growth and do not require a special process to form. The propagating uplift forms an elongated river network geometry with broad high-elevation, low-relief headwaters and interfluves that persist for tens of millions of years, consistent with the observed geochronology. We suggest that the low-relief interfluves can be long-lived because they lack the drainage networks necessary to keep pace with the rapid incision of the large main-stem rivers. The propagating uplift also produces spatial and temporal exhumation patterns and river profile morphologies that match observations. Our modeling therefore reconciles geomorphic observations with geodynamic models of uplift of the southeast Tibetan Plateau, and it provides a simple mechanism to explain the low-relief surfaces observed in several mountain belts on Earth.

Machine learning to identify geologic factors associated with production in geothermal fields: a case-study using 3D geologic data, Brady geothermal field, Nevada

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid-flow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fluid-flow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.

Challenges of a mature Russian field's redevelopment – Advantages and disadvantages of quick-look geologic modeling

Due to the global oil price crisis in 2014, one of the MOL's preventive/reactive measures was to identify geologically or commercially risky elements within their portfolio. This involved reevaluation of all geologic data from Field A in the Volga-Urals Basin. In re-evaluating Field A, several unexpected challenges, problems and pitfalls were faced by the interdisciplinary team performing the task of building a new database, quality checking, and interpreting data dating back to 1947. To overcome these challenges related to this mature field, new approaches and fit-for-purpose methods were required in order to achieve the overall goal of obtaining a reliable estimation of remaining hydrocarbon potential. In the first phase a first-pass 3D geologic model was constructed, along with wrangling, cleaning and interpreting 70 years of subsurface data. This paper focuses on the main challenges involved in evaluating or reevaluating reservoir aspects of a mature field.The primary challenges were related to the estimation of remaining in-place hydrocarbon volumes, the optimization of infill well placement, the identification of primary and secondary well targets, the identification of critical data gaps, and the planning of new data acquisitions. The hands-on experience gained during the development of the geologic model provided invaluable information for the next steps needed in the redevelopment of the field.

Mendeleev Rise basalts compile an acoustic basement of the North Chukchi Basin?

<p>The Eastern Arctic is poor studied by offshore drilling. There are some wells drilled on the Alaska shelf, but Russian sedimentary basins are separated from Alaska basins by tectonic structures, therefore seismic complexes could not be traced confidently from Alaska to the North Chukchi Basin. Nevertheless, seismic lines in the Eastern Arctic acquired in last decade, samples from seafloor scarps on the Mendeleev Rise (Skolotnev et al., in preparation) and geologic data from adjacent onshore geology allows to assume the mechanisms and timing of the Eastern Arctic Basins forming. According to data from De-Longa Islands and from sampling on the scarps of the Mendeleev rise, the wide basalt volcanism was acting during ±125-100 Ma. The volcanism related to forming of rift basins all over the Eastern Arctic. On the seismic lines crossing the Mendeleev Rise some structures that could be interpreted as volcanos and Seaward Dipping Reflectors (SDR) are identified at the base of geological section. The top of these structures are traced on the seismic lines, and continue from the Mendeleev rise to the North Chukchi Basin where they are covered by clastic complexes that prograde from the territory of the Early Cretaceous Verkhoyansk-Chukotka Orogen. On this account the North Chukchi Basin started to form not earlier than in Barremian-Aptian. Continuation of Mendeleev Rise into the North Chukchi Basin is confirmed by the data of magnetic anomalies. To the south of the North Chukchi Basin on the Wrangel-Gerald High the volcanic build-ups and associated intrusions are interpreted. Presence of magmatic features in this area is confirmed on the magnetic anomaly map. The volcanic horizons lay below the sedimentary cover of the North Chukchi Basin. Our main conclusion is that Mendeleev Rise and North Chukchi Basin started to form nearly simultaneously during Aptian (Barremian) - Albian time and they compile connected geodynamic system.</p>

Which Fault Threatens Me Most? Bridging the Gap Between Geologic Data-Providers and Seismic Risk Practitioners

The aim of the Fault2SHA European Seismological Commission Working Group Central Apennines laboratory is to enhance the use of geological data in fault-based seismic hazard and risk assessment and to promote synergies between data providers (earthquake geologists), end-users and decision-makers. Here we use the Fault2SHA Central Apennines Database where geologic data are provided in the form of characterized fault traces, grouped into faults and main faults, with individual slip rate estimates. The proposed methodology first derives slip rate profiles for each main fault. Main faults are then divided into distinct sections of length comparable to the seismogenic depth to allow consideration of variable slip rates and the exploration of multi-fault ruptures in the computations. The methodology further allows exploration of epistemic uncertainties documented in the database (e.g., main fault definition, slip rates) as well as additional parameters required to characterize the seismogenic potential of fault sources (e.g., 3D fault geometries). To illustrate the power of the methodology, in this paper we consider only one branch of the uncertainties affecting each step of the computation procedure. The resulting hazard and typological risk maps allow both data providers and end-users 1) to visualize the faults that threaten specific localities the most, 2) to appreciate the density of observations used for the computation of slip rate profiles, and 3) interrogate the degree of confidence on the fault parameters documented in the database (activity and location certainty). Finally, closing the loop, the methodology highlights priorities for future geological investigations in terms of where improvements in the density of data within the database would lead to the greatest decreases in epistemic uncertainties in the hazard and risk calculations. Key to this new generation of fault-based seismic hazard and risk methodology are the user-friendly open source codes provided with this publication, documenting, step-by-step, the link between the geological database and the relative contribution of each section to seismic hazard and risk at specific localities.

Geoprocessing Techniques for the Visualization of Subsurface Geologic Data in Geographic Information Systems

ABSTRACT The ability to visualize subsurface geologic information is critical to sound decision making in many disciplines of geology. While there are numerous commercial off-the-shelf software solutions available to model geologic data in both 2D and 3D, these can be costly and have a steep learning curve. Some of the same functionality of these software packages can be accomplished by workflows that incorporate built-in geoprocessing tools of Geographic Information System (GIS) software. These workflows allow the geologist to plot vertical or inclined borehole data in 2D or 3D, create section views of raster data along section lines, and provide a means to convert contact elevations from existing geologic cross sections into plan-view or 3D space. These workflows have been successfully used to visualize construction data and subsurface geologic information for several embankment dams. Grouting and exploratory borehole data from databases with tens of thousands of records have been transformed into 2D and 3D GIS features. The workflows were instrumental in developing a 3D GIS model of site geology from which a series of geologic cross sections were drawn. These sections were critical in informing risk decisions related to the foundation conditions for a recent risk assessment of an earthen embankment dam.