Five Signs Your Field Data Workflow Is Holding Back Better Exploration Decisions

Portable analytical instruments have changed the pace of mineral exploration. Field teams can now collect information on elemental chemistry, mineralogy, alteration, lithology, and physical properties before samples ever reach a laboratory.

The measurements, however, are only part of the job.

Picture a typical exploration program: the same sample may be analyzed by pXRF for elemental data, pNIR or pSWIR for clays and alteration minerals, and pXRD for broader mineralogical information. It may also be photographed, geologically logged, and linked to a specific location or drill interval. Each tool adds another piece to the story, but those pieces often end up scattered across different applications, spreadsheets, and folders. When sample identifiers, preparation records, QA/QC results, and instrument outputs are not kept together, teams must spend valuable time reconstructing the context behind each measurement.

The challenge is no longer simply collecting more data — it is keeping everything connected, traceable, and ready to support field decisions.

One publicly disclosed example comes from Cascade Copper’s 2025 beta collaboration with Blockware Technologies. Historical datasets from two exploration projects had already been assimilated into the modelling workflow, and the resulting predictions identified priority areas for field follow-up. For subsequent field deployment, the workflow was designed to use Fieldscan ONE™ to collect and quality-check new pXRF, LIBS, and NIR/SWIR data before modelling, allowing updated predictions to reach crews while follow-up decisions were still possible [7].

This is a recurring limitation in field analytics: the instruments are working, but the relationships among their data are not sufficiently preserved.

The FAIR Guiding Principles emphasize that scientific data should remain findable, accessible, interoperable, and reusable [1]. In field analytics, that requires more than organized final files. Sample identity, metadata, analytical methods, QA/QC results, and correction history must remain connected from acquisition through interpretation.

Here are five signs that a field-data workflow may no longer be keeping pace with the program it supports.

1. Every instrument has become a separate data island

A pXRF produces elemental concentrations. An NIR/SWIR instrument generates reflectance data and mineral interpretations. A pXRD identifies crystalline phases. Gamma measurements, photographs, GPS coordinates, geological observations, and sample-preparation records may all be stored elsewhere.

This arrangement can be manageable with one instrument and a limited sample set. As a program grows, it often leads to repeated exports, file conversions, manual spreadsheet joins, inconsistent units, and several competing versions of the project dataset.

Sample identifiers illustrate the problem. These labels could refer to the same interval—or to three different samples:

• DDH-024-115.5
• DDH024_11550
• 24-115.5

Without controlled naming rules and explicit relationships, someone eventually has to reconstruct the answer by hand.

An integrated workflow does not require every instrument to report identical information. Instrument-specific parameters remain essential. What should be consistent is the context surrounding each measurement:

• sample identity and location;
• operator and timestamp;
• analytical method and units;
• QA/QC status.

This principle is reflected in the ISO and Open Geospatial Consortium standard for observations, measurements, and samples, which provides a framework for maintaining relationships among observations, procedures, samples, and results [2].

A useful test is straightforward: can the team compare outputs from several technologies without repeatedly rebuilding the underlying sample history?

2. Metadata quality depends on the operator

A numerical result has limited long-term value unless it remains tied to the context required to interpret it. A reviewer may need to know the sample type and condition, location or interval, preparation method, instrument, analytical mode, acquisition time, calibration profile, operator, and applicable QA/QC status.

When this information is recorded primarily as free text, consistency depends on individual habits. One operator records “drill core,” another enters “core,” and a third uses “DDH.” Moisture, surface condition, or preparation details may be documented carefully by one person and omitted by another.

The measurement itself may still be valid. The later problem is determining exactly what it represents and whether it is appropriate for the decision being considered.

Analytical validity and data integrity are different issues:

• Analytical validity concerns whether the method adequately measures the property of interest.
• Data integrity concerns whether the result remains connected to the information needed to understand how it was produced.

Project templates, mandatory fields, standardized units, controlled terminology, and validation rules improve metadata consistency at the point of acquisition. Free-text notes still matter, but they should add context rather than replace structured information.

Six months after a field program, another geoscientist should be able to understand the dataset without relying on the memory of the original operator.

3. QA/QC problems are discovered too late

Standards, blanks, duplicates, calibration checks, drift monitoring, laboratory comparisons, and documented corrective actions are not administrative details to add at the end of a project. They help determine whether analytical results are fit for their intended purpose.

Portable instruments are powerful, but their performance can be affected by instrument configuration, sample matrix, interferences, preparation, surface condition, acquisition time, and calibration suitability. Hall, Bonham-Carter, and Buchar showed that pXRF performance can vary by element and instrument, reinforcing the need for application-specific calibration and quality-control procedures [3].

Although developed for soil and sediment, the United States Environmental Protection Agency’s Method 6200 similarly illustrates the fit-for-purpose principle: performance must be evaluated against project-specific data-quality objectives [4].

Operational risk emerges when control samples are measured in the field but reviewed only after export. By the time a failure or drift trend is identified, hundreds of additional samples may have been analyzed, the instrument may have moved to another site, and the original samples may no longer be readily accessible.

QA/QC is most effective when it can still influence what happens next. A robust workflow can indicate when a control is due, compare the result with predefined acceptance criteria, retain pass and fail outcomes, and associate each control with the samples it governs.

Automation can apply these rules consistently, but it cannot decide whether the rules are scientifically appropriate. Standards, thresholds, corrections, and analytical methods still require qualified judgment based on the sample matrix, concentration range, instrument, and project objectives.

4. A reported value can no longer be traced to its origin

Every analytical result should remain connected to both the physical sample and the process that produced it. That connection is the foundation of data provenance.

The W3C PROV Data Model describes provenance in terms of the entities, activities, and agents involved in producing data [5]. Applied to field analytics, this means a reviewer should be able to identify the sample measured, when and where it was collected, how it was prepared, which instrument and method were used, who performed the measurement, and whether the required QA/QC criteria were met.

Corrections and superseded results must remain visible. A corrected result should not silently replace the original measurement.

The record should retain:

• the raw and corrected values;
• the correction method;
• coefficients or reference materials used;
• the applicable instrument and element;
• the date, responsible user, and software or calibration-profile version.

The same principle applies when a result is repeated, rejected, or superseded. Preserving this history strengthens the dataset by making the basis for the final reported value transparent and defensible.

If producing an audit trail requires searching through renamed files and archived spreadsheets, provenance has become an operational vulnerability.

5. Field results influence the project only after demobilization

The strategic value of portable analytical instruments lies partly in their ability to inform decisions while field activities remain active. That advantage is diminished when results must pass through repeated exports, spreadsheet joins, separate QA/QC reviews, and office-based processing before they can be compared or interpreted.

A program may collect measurements in near real time without producing decision-ready information in the same operational timeframe.

Earlier access to structured, quality-controlled field data can support decisions such as:

• prioritizing samples;
• adjusting sampling density;
• identifying intervals for follow-up;
• refining lithological or alteration boundaries;
• selecting material for laboratory confirmation.

Faster access does not make portable results interchangeable with final laboratory values. Bourke and Ross showed that heterogeneity and sample preparation can materially affect pXRF performance when comparing unprepared drill core with powdered material [6]. Portable methods must therefore be interpreted within their analytical limitations and in relation to project objectives.

Accelerated access should not encourage premature interpretation. Its purpose is to give geoscientists relevant, fit-for-purpose information while they can still collect another measurement, revisit an interval, or adjust the sampling plan.

What integration can—and cannot—solve

Not every field program needs a fully integrated software environment. One instrument, one experienced operator, and a limited sample set may be managed effectively with controlled spreadsheets and well-documented procedures.

The requirement changes as a program expands. Additional instruments, operators, locations, sample types, and QA/QC requirements create more relationships that must be preserved. At that point, fragmentation appears as manual reconciliation, delayed interpretation, inconsistent metadata, and uncertainty about which version of the data is current.

Integration alone does not guarantee scientific quality. Software cannot make an unrepresentative sample representative, correct an unsuitable analytical method, justify a weak calibration, or replace operator training. It can, however, make sound procedures easier to apply consistently and make departures from those procedures easier to identify.

Why Geospectra is developing Fieldscan ONE™

These workflow challenges shaped the development of Fieldscan ONE™, a sensor-agnostic environment developed by Geospectra in collaboration with its technology partners to organize portable analytical technologies within a coherent project structure.

The platform is built around four connected priorities:

Multi-sensor workflow

Fieldscan ONE™ provides an instrument-agnostic environment and a common user experience across supported technologies. Results from pXRF, pNIR/pSWIR, pLIBS, pXRD, and portable gamma-ray spectrometry can be organized around consistent project, location, and sample records while preserving the technical information specific to each method. Additional methods, including pFTIR, can be introduced as their support is confirmed.

Quality controls

Instrument calibrations, embedded standard checks, drift and error detection, metadata controls, and audit records remain connected to acquisition. Fieldscan ONE™ is also being developed to expand sample-classification workflows, OREAS reference-data integration, uncertainty and limit-of-detection handling, and spectral analysis. This gives teams greater visibility into data quality while corrective action is still possible.

Automation and control

Capabilities such as sampling plans, structured metadata fields, project templates, controlled inputs, and instrument profiles help teams standardize how measurements are collected and preserve the operating context of each result. This improves consistency at acquisition rather than during later data cleanup.

Data analytics and governance

A reporting suite, combined visualization and analysis of XRF and LIBS data, cloud synchronization, and API-ready integration help move information from acquisition into interpretation and project-level decisions. The API-ready architecture is intended to reduce manual exports, duplicate entry, and reformatting while preserving sample identity, analytical methods, calibration records, metadata, and QA/QC context. The objective is not to replace specialized geological databases or professional judgment. It is to support a more continuous digital workflow—from field acquisition and analytical validation to centralized project data management.

Fieldscan ONE™ strengthens the structure surrounding field science, reducing the need to reconstruct relationships that should have been preserved from the beginning.

From more measurements to better, more defensible decisions

A successful field analytical program should not be measured only by the number of scans completed. A useful workflow should help the team answer five questions with confidence:

• What was measured?
• How was it measured?
• Did it meet the defined quality criteria?
• How does it relate to the physical sample and the broader project?
• Is it appropriate for the decision being considered?

When those answers are difficult to retrieve, the primary limitation may no longer be the analytical instrument. It may be the workflow connecting the measurements to the professionals who need to apply them.

See how Fieldscan ONE™ structures multi-sensor data, QA/QC and metadata. Request a demonstration.

https://geospectra.ca/solutions/fieldscan-one/

References

[1] Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, Article 160018. https://doi.org/10.1038/sdata.2016.18

[2] International Organization for Standardization. (2023). Geographic information—Observations, measurements and samples (ISO Standard No. 19156:2023). https://www.iso.org/standard/82463.html

[3] Hall, G. E. M., Bonham-Carter, G. F., & Buchar, A. (2014). Evaluation of portable X-ray fluorescence (pXRF) in exploration and mining: Phase 1, control reference materials. Geochemistry: Exploration, Environment, Analysis, 14(2), 99–123. https://doi.org/10.1144/geochem2013-241

[4] United States Environmental Protection Agency. (2007). Method 6200: Field portable X-ray fluorescence spectrometry for the determination of elemental concentrations in soil and sediment (Revision 0). SW-846, Test Methods for Evaluating Solid Waste, Physical/Chemical Methods. https://www.epa.gov/sites/default/files/2015-12/documents/6200.pdf

[5] Moreau, L., & Missier, P. (Eds.). (2013). PROV-DM: The PROV Data Model. W3C Recommendation. https://www.w3.org/TR/2013/REC-prov-dm-20130430/

[6] Bourke, A., & Ross, P.-S. (2016). Portable X-ray fluorescence measurements on exploration drill-cores: Comparing performance on unprepared cores and powders for “whole-rock” analysis. Geochemistry: Exploration, Environment, Analysis, 16(2), 147–157. https://doi.org/10.1144/geochem2014-326

[7] Cascade Copper Corp. (2025, June 4). Cascade Copper announces collaboration with Blockware Technologies to develop field-level predictive modelling. https://www.cascadecopper.com/cascade-copper-announces-collaboration-with-blockware-technologies-todevelop-field-level-predictive-modelling/

About the contributors

This article brings together Geospectra’s operational, geological, spectroscopic, and product-development expertise.

Jean-Simon Tremblay

Business Operations Administrator / Marketing
Writer and editorial lead

Lidbert Alarcón Laime, Ph.D. candidate

Geoscientist / Operations & Solutions Development Manager
Technical review: economic geology, spectroscopy, and multi-sensor field workflows

José Brum

Product Manager and R&D
Technical review: pXRD, mineralogy, and product development