Explore EnviCloud

Scientific Demos

Explore EnviCloud demonstrations through scientific workflows, summaries, and screenshots. Follow each demo link to open its interface and review the available data, models, and results.

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EnviTrace’s U.S. Geothermal Temperature & Stress Model

EnviTrace has recently developed a comprehensive U.S. Geothermal Temperature & Stress Model. This demo presents the results obtained. It supports regional geothermal screening by bringing continental temperature and stress model information into a common map. Users can compare available model layers, inspect geographic variation, and relate predicted conditions to the underlying inputs. The shared view helps researchers identify areas and model differences that merit closer examination during geothermal resource studies.

Maps geothermal inputs and model predictions across the continental United States.
Compares EnviTrace thermal and stress models with the published Stanford Thermal Model.
Lets users switch input and output layers while inspecting the same geographic area.
Provides regional screening and comparison workflow using existing model layers, rather than a new site-specific simulation.
Research papers summarizing the methods and results are in preparation.

Geysers Seismicity

The Geysers workflow presents seismic activity within a major operating geothermal field, connecting earthquake locations with their spatial organization over a decade of observations. We have performed various clustering and spatiotemporal ML analyses. We present 3D views of our closest server to make it easier to examine where events concentrate and how different groups occupy the field. The demo provides an interactive starting point for discussing induced seismicity and selecting patterns for further scientific investigation.

Explores earthquake observations from the Geysers geothermal field over 2010–2019.
Groups events into prepared spatial clusters to reveal differences in event density and location.
Supports pan, zoom, hover, and comparison of two clustering views.
Uses prepared observational visualizations; browsing does not fit a new seismic model or establish a causal explanation.
A recent research paper titled “Deep-Embedded-Clustering of Microseismicity Identifies Multiple Failure Mechanisms at The Geysers Geothermal Field” summarizes preliminary ML results.

Geo Modeling

The geological modeling example brings surface terrain and subsurface structures together in a synthetic three-dimensional field. Wells, horizons, faults, and temperature intervals can be examined in the same scene to understand their spatial relationships. This integrated perspective helps geoscience teams explain subsurface geometry and discuss how well information relates to a broader geological interpretation.

Combines wells, geological horizons, faults, terrain, and temperature intervals in an interactive 3D scene.
Relates well trajectories and depth-dependent temperatures to their surrounding geological context.
Provides layer controls, terrain opacity, vertical relief, and well inspection.
Provides access to our open-source and proprietary ML for geospatial analyses.
Uses deterministic synthetic geology for illustration; the scene is not a field-validated subsurface interpretation.

Data Imputation

Incomplete geologic (geospatial) measurements can make regional patterns difficult to interpret and limit the comparisons available to a modeling team. This demo organizes the reconstruction of missing Great Basin data and the evaluation of spatial predictions within a common analysis workflow. Its comparison tools help users examine how selected methods perform when observations are deliberately withheld for checking.

Examines incomplete Great Basin geothermal measurements and their spatial reconstruction.
Organizes dataset preprocessing, held-back splits, comparison methods, and evaluation metrics in one workflow.
Supports regional prediction grids and comparison of reconstructed values with observations withheld from fitting.

Well Log Gap Filling

A geological well log records changes along depth, but gaps can interrupt the patterns needed for interpretation. This synthetic example uses neighboring measurements to estimate missing values while retaining the ordered depth structure of the record. Comparing the reconstructed log with held-back measurements helps users assess the behavior of the gap-filling method before considering a similar workflow for their own data.

Fills intentionally missing measurements along an ordered synthetic geological well log.
Uses our proprietary bounded RobustML neighbor method, with quick and stability-check configurations.
Compares observed and filled values using held-back RMSE and MAE.
Distinguishes the labeled precomputed example from a registered live workspace analysis.

Spatial Geology Gap Filling

Spatial gaps can obscure relationships between geological measurements collected across a field area. This compact synthetic example demonstrates how nearby observations can inform estimates at missing locations through a bounded, distance-weighted neighbor model. Viewing the reconstructed field alongside its evaluation metrics helps users judge both the spatial result and its agreement with held-back evidence.

Reconstructs missing values in a compact synthetic geological field sample.
Uses bounded, distance-weighted RobustML neighbors to estimate values at missing locations.
Shows spatial observations and predictions alongside held-back RMSE and MAE.
Retains a labeled precomputed fallback; live workspace execution is a separate action.

Information Theory Analyses

Scientific datasets often contain more detail than a particular analysis needs, yet excessive simplification can remove useful structure. The Information Theory Explorer lets users examine this balance through synthetic measurements arranged along a line or across a surface. Visual comparisons connect the amount of retained information with recognizable patterns, making resolution choices easier to understand and discuss.

Explores how scientific patterns appear in data along a line and across a surface.
Varies the character of synthetic measurements and the goal of the information summary.
Uses TensIT comparisons to balance useful retained detail against grid size.
Makes resolution choices inspectable through visual comparisons of the original evidence and its simplified representation.

Tensor Information (TensIT)

TensIT addresses the practical question of how much spatial and temporal detail a scientific dataset should retain. TensIT tool provides capabilities to compare candidate tensor grids against an information target and a computational budget while accounting for structure in the data. Its recommendations and supporting comparisons help users select a manageable representation and review the evidence behind that choice.

Compares candidate tensor resolutions for a reproducible scientific-data scenario.
Balances an information-retention target and cell budget with spatial and temporal structure.
Reports a recommended grid together with retained information and computational-cost comparisons.
Keeps the resolution-selection evidence and saved run history available for review.

Feature Extraction (temporal)

A measured time series may combine several underlying processes whose individual contributions are difficult to distinguish. NMFk is applied here to show how unknown signatures can be extracted from mixed synthetic observations. Comparing the recovered signals with the original mixtures provides a practical introduction to interpretable feature extraction and the choice of decomposition size.

Separates mixed synthetic time-series observations into interpretable latent temporal signals.
Uses NMFk to compare candidate signal counts rather than assuming a single decomposition size.
Allows a computed run or an explicitly precomputed example, with progress and cancellation for live execution.
Supports comparison of recovered components with the original observations for a chosen value of k.

Feature Extraction (spatial)

Large geoscience datasets can contain recurring combinations of attributes that vary across a region. This demo uses NMFk to identify latent spatial factors and groups from selected measurements, with configurable preprocessing and factorization settings. Maps, biplots, and reconstruction views help users connect the extracted mathematical structure with the locations and attributes being studied.

Extracts latent spatial factors and clusters from selected dataset attributes with NMFk.
Configures the factor-count range, restart count, and normalization before factorization.
Provides matrices, latent maps, biplots, and reconstruction evidence to interpret recovered structure.
Supports optional regional interpolation after results exist; a factorization does not by itself establish a geological cause.

Groundwater Source Unmixing

Groundwater sampled at a well can contain a mixture of chemical influences from several sources. This demo applies NMFk to separate those mixtures into candidate source fingerprints and estimate their contributions at individual wells. Examining alternative source counts, stability, and reconstruction behavior helps users develop and compare explanations for observed groundwater chemistry.

Separates mixed groundwater chemistry into hidden source fingerprints using NMFk.
Compares candidate source counts and repeat-run stability.
Relates each recovered source to contributions at individual wells.
Can hide one measurement to examine its reconstruction, while preserving the distinction between measured and estimated chemistry.

Model Diagnostics

A close match to observed data leaves important questions about a model's reliability and the information contained in its parameters. This demo combines calibration with held-back checking, sensitivity analysis, and an examination of whether individual inputs can be constrained. The resulting views help users distinguish influential parameters from weakly supported ones and identify where additional evidence could improve an analysis.

Combines model calibration with a held-back data check.
Examines which inputs influence predictions and which parameters are poorly constrained.
Includes an optional unused input to illustrate an identifiability warning.
Encourages joint review of fit, validation, sensitivity, and identifiability rather than judging a model by fit alone.
Applies our open-source code Mads.jl

Contaminant Source Identification

Monitoring wells record the downstream consequences of a release, while its original timing and movement through groundwater may remain uncertain. This synthetic inverse problem uses concentration histories to investigate a release window and an associated transport speed. Bringing the monitoring layout and estimated source behavior together helps users understand how observations constrain a possible contamination history.

Uses monitoring-well concentration histories to infer a contaminant release window.
Estimates the groundwater transport speed connecting a release to downstream monitoring wells.
Provides quick and stability-check depths for the synthetic inverse problem.
Presents the monitoring layout, concentration histories, source timing, and transport estimate together for interpretation.

Q-Sure Quantum ML Assurance

Q-Sure explores how evidence can guide the use of quantum machine-learning methods when uncertainty and resource limits matter. The demo replays seeded offline-emulator records and presents assurance, prediction, and audit comparisons within a shared evidence workflow. Users can examine how available information supports accepting a result, allocating additional effort, or abstaining when the evidence is insufficient.

Replays seeded offline-emulator evidence for quantum machine-learning assurance.
Compares assurance savings, quantum-data prediction evidence, and an NMF audit.
Makes uncertainty, evidence budgets, and abstention decisions visible.
Uses frozen offline records, not live quantum hardware; the NMF audit reports no quantum advantage.

Mine-Scale Lithium Prospectivity

This demo presents saved machine-learning prospectivity evidence for the Bald Hill lithium area in Western Australia. Ranking views are considered alongside measured assays, spatial support, cross sections, and interpreted pegmatite geometry. The combined evidence helps exploration teams examine where model rankings are supported by observations and how they behave across spatial holdouts.

Explores machine-learning lithium prospectivity rankings for Bald Hill, Western Australia.
Compares P70, P85, and P95 ranking heads with measured assays and an independent support mask.
Links 3D exploration to cross sections and explicitly interpreted pegmatite geometry.
Reviews spatial-holdout robustness; these saved rankings are not a grade, reserve, or orebody model.

RAIN Watershed Dashboard

The RAIN dashboard connects hydrologic observations and saved modeling evidence across ten Colorado River watersheds. Users can explore monitoring stations and available streamflow, groundwater, weather, snow, and reservoir records in their geographic context. Links to detailed model comparisons support closer review of how individual methods behave across stations, forecast horizons, and distinct evaluation periods.

Connects ten Colorado River watershed dashboards with station observations and reservoir histories.
Supports inspection of measured streamflow, groundwater, weather, snow, and reservoir records where available.
Links to saved Upper Gila model comparisons by gage, forecast horizon, method, and evaluation period.
Browses saved evidence without fitting new models; training, validation, and final-test results retain their distinct meanings.

Bayesian Information Gap Decision

Environmental decisions often depend on both monitoring evidence and uncertainty that extends beyond a single assumed scenario. This demo combines Bayesian evidence with information-gap stress testing to compare contaminant source-termination choices. By showing how far each option can tolerate departures from its assumptions, it helps users discuss robustness alongside a selected limit on modeled failure probability.

Combines Bayesian monitoring evidence with information-gap stress testing for contaminant source termination.
Compares four choices against a selected maximum acceptable modeled failure probability.
Measures how much uncertainty each choice tolerates before exceeding that limit.
Supports comparison under deep uncertainty; the model informs the decision rather than making it for the operator.

WEF Nexus Decision

Water, energy, and food planning involves competing demands on resources, infrastructure, and budgets. This synthetic decision problem allows users to vary planning assumptions and examine feasible alternatives across cost, carbon, freshwater, and production objectives. Its Pareto frontier makes the trade-offs between efficient plans visible and supports discussion of which compromises best match a planning team's priorities.

Explores a synthetic planning problem linking water, energy, and food.
Changes demand, freshwater availability, and carbon-budget assumptions through policy presets.
Compares feasible nondominated plans on a Pareto frontier.
Keeps cost, carbon, freshwater, food, energy, and capacity consequences visible as competing objectives.

Energy Production/Storage

Subsurface energy decisions depend on how reservoir properties and well arrangements affect flow, heat transfer, and pressure. This synthetic example connects a chosen reservoir domain and physical inputs with field generation and a flow-and-thermal calculation. Comparing the resulting maps and summaries helps users investigate the consequences of an energy production or storage scenario.

Estimates energy production and pressure change for a synthetic reservoir and well arrangement.
Configures a reservoir polygon, resolution, permeability, rock and fluid properties, and operating inputs.
Generates a permeability field and runs the flow and thermal model through separate controls.
Relates output maps and energy or pressure summaries to the selected physical inputs; configuration alone is not a solved scenario.

Carbon Capture and Storage

Carbon storage planning requires an understanding of both available reservoir capacity and the pressure response to injection. This synthetic example relates geological properties, well spacing, and monitoring constraints to a defined storage domain. Its model views provide a way to compare how input assumptions affect estimated storage behavior and the operational limits that accompany it.

Evaluates carbon-storage capacity and pressure change in a synthetic reservoir scenario.
Connects permeability, porosity, well separation, and monitoring limits to the chosen reservoir domain.
Uses field-generation and solver controls before storage outputs can be interpreted.
Supports comparison of input and output maps, storage capacity, and pressure constraints; the example is not a certified storage assessment.

Geothermal Operations (GeoTGo Ops)

Operating a geothermal doublet involves balancing near-term energy recovery with the longer-term condition of the reservoir. GeoTGo Ops connects an operating priority with physics-informed forecasts, candidate strategies, and comparisons of thermal and pressure consequences. The linked views help users examine why one strategy is recommended and how closely competing options meet the same operating goals.

Explores physics-informed operating strategies for a two-well geothermal doublet.
Balances immediate energy output against longer-term thermal effects and pressure-limit risk.
Connects strategy, forecast, decision frontier, and power views to the chosen operating priority.
Distinguishes live-engine results from an illustrative frozen-checkpoint preview; changing a preview priority does not perform a new optimization.

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